The Neural and Cognitive Foundations of Human Planning

Inaugural-Dissertation zur Erlangung des Doktorgrades der Wirtschafts- und Verhaltenswissenschaftlichen Fakultät der Albert-Ludwigs-Universität Freiburg im Breisgau

Vorgelegt von

Kai Nitschke geboren am 07.11.1988 in Erfurt

03. April 2017 Freiburg im Breisgau Gutachter Prof. Dr. Heinrichs Prof. Dr. Tuschen-Cafﬁer

Dekan Prof. Dr. Renkl

Tag der Promotion 28. September 2017 0 List of Contents Page Acknowledgments...... iv Abstract...... v Zusammenfassung...... vii

1 Theoretical and Methodological Background1 1.1 Planning and Executive Functions...... 1 1.2 The Neural Foundation of Planning...... 5 1.2.1 Insights from Lesion Studies...... 5 1.2.2 A Literature Research on Lesion Data...... 6 1.2.3 The Inﬂuence of Structuredness...... 12 1.2.4 Overviews on Hemispheric Lateralization...... 14 1.2.5 The Present Approach on Examining Neural Lateralization... 15 1.3 The Cognitive Foundation of Planning...... 16 1.3.1 A Sequential Model of Planning...... 16 1.3.2 The Tower of London and its Structural Parameters...... 18 1.3.3 The Segmentation of Planning...... 21 1.3.3.1 Cognitive Architectures for Modeling Sub-processes.. 21 1.3.3.2 Dissociating Components of Planning on Tower Tasks. 25 1.3.3.3 Insights from Eye Movement Studies...... 27 1.3.4 The Present Approach on Cognitive Processes of Planning.... 34 1.4 Methodological Background...... 35 1.4.1 Activation Likelihood Estimation...... 35 1.4.2 Eye Movement Measurement...... 36 1.4.3 Pupillometry...... 37 1.5 Aims and Objectives of the Thesis...... 39

i 2 A Meta-Analysis on the Neural Basis of Planning: Activation Likelihood Estimation of Functional Brain Imaging Results in the Tower of London Task 41 2.1 Theoretical Background...... 41 2.2 Methods...... 43 2.2.1 Study Selection...... 43 2.2.2 Meta-Analyses on Overall Planning and Planning Complexity.. 45 2.2.3 ALE Approach...... 47 2.3 Results...... 48 2.3.1 The Functional Anatomy of Planning Processes...... 48 2.3.1.1 Meta-analysis of neural activation patterns for overall planning...... 48 2.3.1.2 Meta-analysis of neural activation patterns for planning complexity...... 48 2.3.1 The Anatomical Localization of Planning Processes in the dlPFC...... 51 2.3.2 The Hemispheric Lateralization of Planning Processes in the PFC and beyond...... 52 2.3.2.1 ALE contrast analysis...... 52 2.3.2.2 Exploration of Systematic Differences between Studies 52 2.3.2.3 Whole-brain lateralization analyses...... 55 2.4 Discussion...... 55 2.4.1 The Localization of Planning Processes within the Mid-dlPFC.. 56 2.4.2 The Role of the Left and Right Mid-dlPFC in Planning...... 57 2.4.3 The Functional Anatomy of Planning Processes beyond (Mid)- dlPFC...... 62 2.4.4 Conclusion...... 63 2.5 Additional Data...... 64

3 Dissociating task-demand-specific differences in cognitive processing during planning and problem solving: A validation approach using pupillometry 71 3.1 Introduction...... 71 3.2 Methods...... 75 3.2.1 Sample Description...... 75 3.2.2 The Tower of London Task and Problem Parameters...... 75 ii 3.2.3 Experimental Groups...... 76 3.2.4 Procedure, Apparatuses, and Processing of Data...... 77 3.2.5 Statistical Analysis...... 81 3.3 Results...... 81 3.3.1 Main Analysis...... 81 3.3.2 Post-hoc Analyses...... 83 3.3.3 Control Analysis...... 85 3.4 Discussion...... 86 3.4.1 General effects on pupil dilation...... 87 3.4.2 Parameter-specific effects of Tower Configuration on pupil dilation and processes of internalization ...... 88 3.4.3 Parameter-specific effects of Search Depth on pupil dilation and processes of core planning ...... 90 3.4.4 Limitations...... 92 3.4.5 Conclusion...... 92

4 General Discussion 95 4.1 Summary of the Studies...... 95 4.2 The Neural Lateralization of Planning...... 97 4.2.1 Different Perspectives on the Neural Functional Lateralization. 97 4.2.2 An Approach on a Uniﬁed Neural Theory on Planning...... 103 4.2.3 Limitations of the Meta-analysis on Neural Correlates of Planning...... 106 4.3 Pupillometry and the Cognitive Framework of Planning...... 109 4.3.1 Integration of Present Results...... 109 4.3.2 The Potentials and Limitations of Pupillometry as an Indicator for Cognitive Processes...... 111 4.3.3 A Critique of a Sequential Model of Planning...... 114 4.4 Outlook on Linking the Cognitive and Neural Foundations of Planning 116 4.5 Conclusion...... 121

5 Bibliography 123

6 Appendix 145 Curriculum Vitae...... 146 Publication record and scientiﬁc work...... 149

iii ACKNOWLEDGMENTS

Acknowledgments

This thesis would not have been possible without the support of numerous people.

I am most greatly indebted to Dr. Christoph Kaller for supervising my PhD thesis from the early days of planning to the ﬁnal manuscript. Thank you for the opportunity to participate in your research and your support in all stages of this dissertation.

Thank you to Prof. Dr. Markus Heinrichs and Prof. Dr. Cornelius Weiller; without your support this thesis would not have been possible. I am very grateful for your professional supervision and the opportunity to be able to use structures of both the university clinic as well as the university.

I am greatly indebted to my colleague Dr. Lena Köstering all the discussions we had about various topics and all advice that you gave me during the PhD process. Thank you to Dr. Elisa Scheller for your support, especially in the ﬁnal stages. I thank my colleagues Konrad Schumacher and Charlotte Schmidt for an enjoyable and relaxing work atmosphere. You provided a great work environment full of discussions, support, and company in my daily work life.

Finally, I would like to thank Dr. Julia Asbrand for her tremendous encouragement that helped me overcome numerous obstacles in the entire course of this thesis.

Freiburg im Breisgau, 03. April 2017

Kai Nitschke

iv Abstract

Executive functions (EF) in terms of conscious control of behavior are highly relevant for adequate functioning in everyday life. Planning is a prototypical EF numerously studied in cognitive psychology and neuroscience. However, the specific neural and cognitive correlates are still subject to debate. In detail, there is an ongoing discussion about the neural lateralization of planning in the human brain as well as about the temporal separability of different phases of planning. These two issues were addressed in the presented thesis. Concerning the issue of neural lateralization, a recently published review article on the neural involvement during human planning based on selected brain lesion studies postulated a rather strict hemispheric lateralization of different types of planning tasks. Planning in well-structured tasks such as the Tower of London (ToL) was postulated to be processed solely by the prefrontal cortex (PFC) of the left but not the right hemisphere. This proposal was however contrasted by a qualitative overview of functional neuroimaging studies showing that the right PFC was activated in a large number of functional neuroimaging studies using the well-structured ToL. To substantiate either conclusion, in the first study of this thesis a quantitative meta- analysis was conducted based on the well-established activation likelihood estimation methodology. A thorough literature search on all available ToL studies yielded 29 neuroimaging publications suitable for such an analysis. The analysis statistically confirmed a bilateral involvement of left and right PFC during well-structured planning. Furthermore, an extensive literature search was conducted on brain lesion studies that using well-structured planning tasks also yielded no indication for a unilaterality of well-structured planning. Concerning the issue of temporally separable phases of planning, previous analyses of eye-movement patterns during the ToL determined distinct phases of planning. In a recent eye-movement study manipulation of specific structural parameters of ToL problems allowed to isolate and segment of two temporally distinct phases of cognitive processing, an initial phase serving to create a mental representation of the problem (representation creation) and a subsequent phase serving to generate

v ABSTRACT

the sequence of solution steps, i.e. the actual planning phase (sequence generation). However, the eye tracking methodology entails several limitations regarding the ability to validate sequential processes as well as applicability in more complex planning tasks. By contrast, pupillometry has shown to be a reliable and easy-to-use biomarker for cognitive load but is yet neglected in planning research. In the second study of this thesis, the insights on distinct sub-phases of planning were validated and extended by pupillometry. The main analyses revealed that pupil sizes during performing the ToL not only showed an increase in general but also speciﬁcally in the sequence generation phase with higher demands on planning ahead. In the general discussion of the thesis, the limitations and potentials of both studies were analyzed to identify targets of future research. Especially the direct link between the neural and cognitive processes identiﬁed in the present thesis was considered.

vi Zusammenfassung

Exekutive Funktionen spielen eine wesentliche Rolle für die erfolgreiche Bewälti- gung des Alltags. Die Fähigkeit zu Planen ist eine prototypische exekutive Funktion, die in zahlreichen Studien aus den Bereichen der kognitiven Psychologie und den Neurowissenschaften untersucht wurde. Allerdings sind die neuralen und die kognitiven Zusammenhänge immer noch nicht ausreichend verstanden. Aktuelle Debatten beschäftigen sich sowohl mit der Lateralisierung von Planungsprozessen im Gehirn als auch mit der zeitlichen Trennung kognitiv unterschiedlicher Phasen des Planens. Beide Themen wurden in der vorliegenden Doktorarbeit näher spezifiziert, mit eigenen Studien erweitert und anschließend diskutiert. In einem kürzlich veröffentlichten Überblicksartikel zu ausgewählten Läsion- sstudien wurde postuliert, dass die Strukturiertheit der Planungsaufgabe bestimmt, in welcher Hemisphäre des präfrontalen Kortex (PFK) die Aufgabe bearbeitet wird. Umfassend strukturierte Planungsaufgaben, wie der Tower of London (ToL), soll- ten hierbei vom präfrontalen Kortex in der linken Hemisphäre verarbeitet werden. Dem widersprach jedoch eine qualitative Gegenüberstellung von Studien mit funk- tioneller Bildgebung, die Ergebnisse zur Aktivierung des rechten präfrontalten Kortex während der Planung des ToL berichteten. Um diesen Widerspruch aufzulösen, wurde in der ersten Studie der vorliegenden Doktorarbeit eine quantitative Metaanalyse mithilfe der etablierten Activation Likelihood Estimation Methode durchgeführt. Eine ausführliche Literaturrecherche identifizierte 29 für eine Metaanalyse geeignete funk- tionelle Bildgebungsstudien zum ToL. Die Metaanalyse bestätigte statistisch, dass während der Planung des ToL beide Hemisphären des präfrontalen Kortex aktiviert werden. Zudem wurde eine umfangreiche Literaturrecherche zu Läsionsstudien und vollstrukturierten Planungsaufgaben durchgeführt, die ebenfalls keine Hinweise für eine einseitige Beteiligung des präfrontalen Kortex erbrachte. Vorherige Studien konnten anhand von Blickbewegungsanalysen zeitlich dis- tinkte Phasen des Planens im ToL nachweisen. Hierbei erlaubte die gezielte Ma- nipulation spezifischer Strukturparametern des ToL zeitlich getrennten kognitive Verarbeitungsphasen zu isolieren: eine initiale Phase zum Aufbau einer mentalen

vii ZUSAMMENFASSUNG

Repräsentation des Problems und eine sich anschließenden Phase zur Generierung von Lösungsschritten, der eigentlichen Planungsphase. Allerdings unterliegen Blick- bewegungsmessungen bestimmten Einschränkungen hinsichtlich des Nachweises se- quentieller Abläufe sowie der Anwendbarkeit auf komplexere Planungsaufgaben. Die Pupillometrie hingegen hat sich als ein reliabler und vielseitig einsetzbarer Biomarker erwiesen, der bis jetzt jedoch noch nicht im Rahmen der Planungsforschung eingesetzt wird. In der zweiten Studie der vorliegenden Doktorarbeit wurden die Erkenntnisse zu den einzelnen Subphasen des Planens mithilfe von Pupillometrie validiert und erweitert. Die Analysen zeigten nicht nur eine generelle Pupillendilatation während der Bearbeitung des ToL sondern darüber hinaus noch eine speziﬁsche Dilatation in Abhängigkeit der Planungsanforderungen der Aufgaben. In der detaillierten Diskussion der vorliegenden Doktorarbeit werden die Poten- ziale und Limitationen der vorgestellten Studien analysiert, um zukünftige Forschungs- ziele und -möglichkeiten aufzuzeigen. Allen voran wird die Kombination von methodis- chen Ansätzen zur gleichzeitigen Untersuchung der neuralen und kognitiven Prozesse, die in dieser Arbeit vorgestellt wurden, hervorgehoben.

viii Theoretical and 1 Methodological Background

1.1 Planning and Executive Functions

Planning is of utmost importance in our daily lives. It is relevant on a small scale when buying groceries on the way home from work to later prepare dinner when one is hungry. And it is relevant on a large scale as in pursuing good grades in high school so that one can visit law school and become a judge several years later. Most notably, its impact is revealed when planning fails or is missing completely. For instance, according to several developmental theories, teenage pregnancies, drunk driving, and risk-taking behavior in adolescence are consequences of the failure or resistance to plan (Adler, Moore, & Tschann, 1998; Chalmers & Lawrence, 1993; Sansone & Berg, 1993). In the literature the term planning is used to describe a variety of cognitive processes and in most cases is used as an equivalent to problem solving (Anzai & Simon, 1979; Klahr, 1994; Kotovsky, Hayes, & Simon, 1985; Simon, 1975; Zhang & Norman, 1994). The different deﬁnitions agree on i) the initial formation of a mental representation and ii) the planning as a process that needs the mental generation of intermediate steps to achieve a particular goal that is not directly achievable without these intermediate steps (Ward & Morris, 2005). Moreover, planning involves a complex variety of behavioral and mental processes that entail cognitive, emotional, and motivational resources (Friedman & Scholnick, 1998; Ward & Morris, 2005). An important part of planning is anticipating the outcome of a behavioral action correctly before executing the action. On the one hand, this means that subsequently

1 THEORETICAL AND METHODOLOGICAL BACKGROUND

possible steps that follow after the current action have to be anticipated. On the other hand, whether the ﬁnal goal is achieved has to be monitored. The alternative to planning would be acting by trial-and-error which means trying every possible step and subsequent steps until the desired goal is achieved. Especially in everyday life this approach is often not possible and rather planning ahead is necessary.

In cognitive psychology, planning behavior is subsumed under the category of executive functions (EFs). Executive functions comprise a number of higher-order, top-down controlled cognitive processes that are needed when non-routine tasks occur or prepotent, impulsive responses have to be inhibited (e.g. J. D. Cohen, Servan-Schreiber, & McClelland, 1992; E. Miller, 1999; E. Miller & Cohen, 2001; Passingham, 1993). In most definitions of executive functions, relevant cognitive components comprise attentional and inhibitory control, novelty and feedback processing, working memory, cognitive flexibility, reasoning, planning and problem solving (Burgess, Veitch, De Lacy, & Shallice, 2000; Chan, Shum, Toulopoulou, & Chen, 2008; Damasio, 1995; Grafman & Litvan, 1999; Shallice, 1988; Stuss & Benson, 1986; Stuss, Shallice, Alexander, & Picton, 1995). Among these diverse processes, planning is recognized as a prototypical executive function (e.g. Allport, 1989, 1993; Norman & Shallice, 1980; Köstering, Schmidt, Weiller, & Kaller, 2016; Norman & Shallice, 1986; Ward, 2005). There are numerous models on executive functions that utilize different approaches from different research backgrounds and, hence, focus on different components and relations. For instance, E. Miller and Cohen(2001) focus on the neurobio- logical, cytoarchitectonical and anatomical evidence about the PFC and emphasize its role as exerting top-down cognitive control which is assumed to monitor and regulate (almost) every other cognitive function by actively maintaining goals and the means to achieve them. Subsequently, two current models will be outlined and compared exemplarily, one derived from independent components found in a data-driven approach (Miyake et al., 2000; Miyake & Friedman, 2012) and one constituting a review and consensus on different models of EF (Diamond, 2013). Although this thesis focuses specifically on planning and not on EFs in general, in chapter 4.2.1 further theories

2 Planning and Executive Functions 1.1

on EFs that have direct implications on concepts of planning ability are explained and compared. Miyake et al.(2000) approached the segmentation of cognitive components underlying cognitive control by examining several different tasks that were assumed to mainly invoke executive functions. The experiment revealed three slightly correlated but clearly independent factors: Shifting was described as the attentional switching between different demands or sub-tasks, updating as the monitoring and processing of stored and new information, and inhibition as suppression of predominant automatic actions when they are not suitable for the current situation (Miyake et al., 2000). In a later revision of their model, an additional and superior component – the common executive function factor – was included (Miyake & Friedman, 2012). Consequently, the inhibition component was removed from the model due to the fact that most of its variance was covered in the common executive function component. In Miyake et al.’s (2000) model, planning mostly taps the updating component but also to a more limited degree the inhibition or the common executive function component, respectively. Diamond(2013) reviewed a large number of studies and presented a summary model of executive functions (Fig. 1.1). The model agrees on Miyake et al.’s original three core executive functions of inhibitory control (cf. inhibition), working memory (cf. updating), and cognitive flexibility (cf. shifting) but does not consider the superior common executive function component as postulated in Miyake and Friedman’s (2012) revised model. Moreover, Diamond argues in favor of a strong link between working memory and inhibitory control such that they are thought to operate interde- pendently, whereas Miyake et al. (2000; 2012) assume these components as rather independent. A crucial difference compared to the model of Miyake et al.(2000) is that Diamond explicitly establishes higher-level executive functions as a supraordinate component which, although separate, is highly influenced by the core components. This higher-level executive functions are not to be mistaken or equated with Miyake and Friedman’s (2012) executive function component which is supposed to influence the core components and not vice versa. The higher-level executive functions include reasoning and problem-solving (which taken together constitute fluid intelligence) as well as the focus of the present thesis, planning.

3 THEORETICAL AND METHODOLOGICAL BACKGROUND

EXECUTIVE FUNCTIONS

Working Memory Maintaining your goal, or what you Inhibitory Control should and shouldn’t do, in working memory is critical for knowing what to Response Effortful Including mental math, re- inhibit Interference Control Inhibition Control refers to ordering items, or relating the innate tempera- Inhibition of Inhibition at Self- one idea or fact to another Inhibition mental predisposi- thoughts and the level of at the level Regulation* tion to exercise Inhibiting environmental & internal dis- memories attention of behavior better or worse tractions is critical for staying focused on (Selective Verbal Visual-Spatial (Cognitive (Self- Self-Regulation the working memory contents of interest or Focused Working Working Inhibition) Control & Attention) Memory Memory Discipline)

*Self-Regulation includes (a) as Executive Attention is response inhibition, usually assessed (using a flanker (b) attention task), it is completely synonymous inhibition, but also Cognitive Flexibility with inhibitory control of attention in addition (c) maintaining optimal Including being able to “think outside the box,” see levels of emotional, something from many different perspectives, quickly switch motivational, and between tasks, or flexibly switch course when needed cognitive arousal supports creativity and theory of mind

Higher-Level Executive Functions

Reasoning Problem-Solving Planning Fluid Intelligence is completely synonymous with these

Fig. 1.1. Overview of components of executive functions. Planning is part of the higher-level executive functions and is, therefore, dependent on lower-level components like inhibition and working memory. Reproduced from Diamond(2013).

Although different EF models assume different components and different inter-

relations between them, the theoretical and empirical accounts of EFs consistently converge on the notion that the prefrontal cortex (PFC) (Fig. 1.2) is the neural core area (e.g. V. Anderson, Jacobs, & Anderson, 2008; Ball et al., 2011; Banich, 2009; E. Miller & Cohen, 2001; Miyake et al., 2000; Robbins, 1996; Stuss, 2011).

4 The Neural Foundation of Planning 1.2

Fig. 1.2. The lateral view of the prefrontal cortex of the human brain The brain can be subdivided in different Brodman areas (BA) by its cytoarchitectonical structure. The prefrontal cortex is located anterior to the pre-motor cortex (BA6) and contains BA8-10 and BA44-47 (Mesulam, 2000; Petrides, 2005, 2013; Petrides & Pandya, 1999, 2006; Petrides et al., 2012; Walker, 1940; Yeterian et al., 2012). The prefrontal cortex is recognized as the most crucial brain area for executive functions. The different colors and numbers denote the cytoarchitectonic structure in terms of Brodmann areas. Reproduced from Petrides(2005).

1.2 The Neural Foundation of Planning

1.2.1 Insights from Lesion Studies

Historically, lesion studies constitute an old approach that already allowed the examination of speciﬁc brain functions in the 19th century (Pearce, 2009; Tizard, 1959), that was seminal for today’s knowledge (Adolphs, 2016; Rorden & Karnath, 2004), and that is still employed to extend insights into the brain (e.g. Dressing et al., 2016; Martin et al., 2016; Rorden, Karnath, & Bonilha, 2007). However, these studies in humans depend on the natural occurrence of lesions which are in most cases spatially unspeciﬁc with extensive and regionally overlapping affected brain areas. Thus, mainly coarse-grained insights are derivable from lesion studies. There is a broad consensus about the distinguished role of the dorsolateral prefrontal cortex (dlPFC; BA 9, 9/46, 46; cf. Fig. 1.2) for planning (for a review see Unterrainer & Owen, 2006). Due to the fact that the functional lateralization debate arose from behavioral differences observed in lesion studies regarding the whole PFC this chapter will focus on the PFC

5 THEORETICAL AND METHODOLOGICAL BACKGROUND

instead of the dlPFC. For a more detailed anatomical distinctions and explanations of PFC and dlPFC see the chapters 2.1 and 2.4. The indisputable relevance of the prefrontal cortex (PFC) for executive functions is similarly applicable to planning as a prototypical executive function (e.g. Fuster, 2001, 2015; Grafman, 1989, 1995; Grafman & Hendler, 1991; Grafman, Spector, & Rattermann, 2005; Koechlin, Basso, Pietrini, Panzer, & Grafman, 1999; Koechlin, Ody, & Kouneiher, 2003; Koechlin, 2012). However, the hemispheric lateralization of planning processes in the PFC has been a matter of debate since Shallice(1982) reported that patients with left anterior brain lesions were found to have substantial decrements in planning accuracy (i.e. the percentage of correctly solved problems within the minimum number of moves possible) and increased planning times (i.e. the time needed for planning the solution without the actual execution) when compared to patients with right anterior lesions as well as with left and right posterior lesions. In contrast, this speciﬁc association of planning impairments with left (pre)frontal cortex was not replicable in a later study (Shallice, 1988, p. 347). Several neuropsychological studies with brain-lesioned patients addressed the lateralization of planning processes afterwards.

1.2.2 A Literature Research on Lesion Data

1.2.2.1 Overview on lateralization of planning ability in lesion studies

With the aim to provide a systematic overview on the insights about hemispheric lateralization of human planning derived from brain lesion based studies a literature search for studies on the most often employed planning tasks (Kaller, Rahm, Köstering, & Unterrainer, 2011) – the Tower of London task (ToL) and the Tower of Hanoi (ToH) (for a brief explanation see Fig. 1.3; see also chapter 1.3.2) – in patients with frontal brain lesions was conducted. Relevant studies were checked for cross references of

Parts of chapter 1.2.2 were published in a signiﬁcantly reduced and adapted form in the peer-reviewed journal article: Nitschke, K., Köstering, L., Finkel, L., Weiller, C., & Kaller, C.P. (2017). A Meta-Analysis on the Neural Basis of Planning: Activation Likelihood Estimation of Functional Brain Imaging Results in the Tower of London Task. Human Brain Mapping, 38(1), 396-413.

6 The Neural Foundation of Planning 1.2

Tower of Hanoi Tower of London

Figure 1.3. Illustrations of the Tower of Hanoi (ToH) und Tower of London (ToL) disc-transfer tasks (adapted after Kaller, Rahm, Köstering, & Unterrainer, 2011). The tower configurations of the two tasks consist of either three discs (ToH) or three balls (ToL) placed upon three rods of either the same (ToH) or different heights (ToL). In both tasks, the start configuration of the discs/balls has to be transformed into a given goal configuration while considering several rules (see chapter 1.3.2). However, the two tasks differ with respect to the restrictions imposed by the physical layouts: In the ToH, the three disks are isochromatic but differ in their size, so that a larger disk must not be placed on a smaller one. In the ToL, the three balls differ in their color and the rods in their length and capacity for holding balls. The left rod can hold up to three balls, the middle rod up to two balls, and the right rod only one ball. For both tasks, subjects are commonly instructed to solve a given problem in the minimum number of moves. Planning ahead is hence required for efficient problem solving in terms of attaining the optimal solution. further suited studies. The literature research revealed 20 studies in total that examined the ToL or ToH task (cf. Fig. 1.3) in patients with frontal brain lesions. However, note that not all of these reported patient samples were independent, as some studies reported (partly) overlapping samples (indicated in Tables 1.1 and 1.2; cf. Morris, Miotto, Feigenbaum, Bullock, & Polkey, 1997a, 1997b; Rushe et al., 1999; Owen, Downes, Sahakian, Polkey, & Robbins, 1990; Pantelis et al., 1997). Furthermore, descriptive statistics or verbatim information on the comparisons of interest here (frontal lesion patients vs. controls, left- vs. right frontal lesion patients) were not available for some studies (Cockburn, 1995; Levin et al., 1994; Lengfelder & Gollwitzer, 2001; Shallice, Burgess, Schon, & Baxter, 1989), whereas many of the remaining studies did not differentiate between left and right frontal lesions (V. Anderson, Anderson, Northam, Jacobs, & Mikiewicz, 2002; Andrés & Van der Linden, 2001; Andrews, Halford, Chappell, Maujean, & Shum, 2014; Glosser & Goodglass, 1990; Jacobs & Anderson, 2002; Mellier & Fessard, 1998; Pantelis et al., 1997; Shum et al., 2009).

7 THEORETICAL AND METHODOLOGICAL BACKGROUND n.s. 0.14 1.31†† 0.87*** 0.94† 0.60* 0.59† 0.64† n.s. 0.58†0.54†0.04 0.55† 0.59† -0.05 0.19 -0.06 -0.14 n.s. n.s. n.s. 0.69* 0.59* in frontally lesioned patients. n.s. n.s.le < ri n.s. n.s. le > ri vs. HC vs. ri vs. HC-1.02** vs. ri. vs. HC vs. ri vs. HC vs. ri -0.63* -1.28*** -0.82* -0.80* Tower of London 5 neutral5 facilitating5 misleading2-9 -0.16 -0.06 -0.42 0.56 0.71 0.18 0.33 0.49 2-34-5 -0.39 0.71 -0.21 -0.22 -0.26 3452-9 -0.182-5 -0.176-9 -0.34 -0.39 0.19 0.12 0.29 0.27 0.45 4-D 4-D 12.16 ToL 11.6 SoC 2 0.24 -0.24 0.27 13.2 ToL 316.6 ToL 2-5 -0.35 -0.23 0.41 0.69 2.717.7 ToL 2-5 SoC 2-5 13.3 -0.18 SoC 1-5 0.14 ± ± 2.8 ToL 2-5 0.11 ± ± ± ± ± ± SD) Tower Minimum Accuracy Number of Planning Movement ± 8.82 68.28 19.1 47.48 13.8 32.8 12.6 57.5 2.9 9.4 2.8 10.8 18.8 44.6 18.1 44.1 ± ± ± ± ± ± ± ± (m/f) (years) Type Moves Executed Moves Time Execution Time N Sex Age M 14/40 7/4/3 7/7 24/16 64.29 Pat/HC le/ri/bi Pat HC13/13 6/4/3 Pat 13/0 13/0 HC 33.2 26/31 8/15/3 15/1118/13 41.73 Pat le Pat le Pat le Pat le 20/80 15/5 43/37 11.4 26/26 8/15/3 14/1214/12 43.0 31/38 12/9/10 19/1219/19 11.3 #1 #1 Study Sample Task Reported Effects Andrews et al. Andrés and Van der Linden ( 2001 ) Carlin et al. ( 2000 ) 14/15 7/5/2 10/4 11/4 59.9 Pantelis et al. ToL V. Anderson et al. Owen et al. Shallice ( 1982 )Shum et al. ( 2009 ) 61/20 15/15 ToL ToL 2-5 Jacobs and Owen et al. ( 1995 ) 17/10 5/10/2 9/8 7/10 46.5 Anderson ( 2002 ) ( 2014 ) ( 1997 ) ( 2002 ) ( 1990 ) Table 1.1. Overview of studies investigating planning ability for the well-structured task Note. If descriptive statistics were available, the here reportedindicate effect posterior sizes tests were of computed significancefollowed (Students as by t-test Hedges’ numbers for indicate g partly independent ( L. or groupslesion; fully V. and bi, overlapping Hedges unequal samples bilateral & between frontal sample, Olkin the lesion; sizes) studies. ToL,1985 ) by Tower Abbreviations: of allowing N, the London; sample comparisons authors SoC, size; across of Stockings m, the of studies. male; Cambridge; f, present Otherwise female; 4-D, study effects Pat, four-disc based patients; version. were HC, on reported healthy the controls; available le, left data. frontal Superscripts lesion; starting ri, with right frontal hashtags verbatim (e.g. le > ri, n.s.). Significant results are highlighted in bold font (*, p < .05; ** p < .01. ***; p < .001; n.s., not significant). Stars (*) indicate p-values reported by the authors whereas crosses (†) 8 The Neural Foundation of Planning 1.2 Goel and Grafman ( 1995 ) did 1 ri > leri > le 0.56 0.60 -0.64 -0.50 HC in frontally lesioned patients. -0.08 -0.73*** vs. HC vs. ri vs. HC vs. ri. vs. HC vs. ri vs. HC vs. ri Tower of Hanoi 5length 6-7 dissimi- lar length n.s. -0.22 0.09 4 conflict5 congruent5 conflict -0.36 -0.22 -0.40 -0.10 -0.14 0.25 7-15 5-D 1.7 ToH 4 le > ri 0.01 -0.06 13.0 ToH ± ± SD) Tower Minimum Accuracy Number of Planning Movement ± 4.3 36.2 11.62 43.5 ± ± (m/f) (years) Type Moves Executed Moves Time Execution Time N Sex Age M 21/44 11/10/0 9/12 19/25 ToH 6-7 similar 21/44 11/10/0 9/12 19/25 34.1 21/44 11/10/0 9/12 19/25 ToH 4 congruent -0.60 0.41 11/56 adolescents 12 ToH 7 Pat > Pat/HC le/ri/bi Pat13/49 8/5/0 HC20/20 6/8/6 Pat HC 61.2 44.4 60.3 ToH Pat le Pat le Pat -0.04 le Pat le #2 #2 #2 1 Study Sample Task Reported Effects ( Morris et al. , ( Morris et al. , Rushe et al. Mellier and Fessard ToH Glosser and Goel and Grafman Goodglass ( 1990 ) 1997b ) 1997a ) ( 1999 ) ( 1998 ) ( 1995 ) not report planning accuracy but asample composite size; “performance m, score”. male; Superscripts f, female; starting Pat, with patients; hashtags HC, followed healthy by controls; numbers le, indicate left partly frontal or lesion; fully ri, overlapping right samples frontal between lesion; the bi, studies. bilateral Abbreviations: frontal lesion; N, ToH, Tower of Hanoi; 5-D, five-disc version. Table 1.2. Overview of studies investigating planning ability for the well-structured task Note. If descriptive statistics were available, the here reported effect sizes were computed as Hedges’ g ( L. V. Hedges & Olkin , 1985 ) allowing comparisons across studies. Otherwise effects were reported verbatim (e.g. le > ri, n.s.). Significant results are highlighted in bold font (*, p < .05; ** p < .01. ***; p < .001; n.s., not significant). Stars (*) indicate p-values reported by the authors whereas crosses (†) indicate posterior tests of significance (Students t-test for independent groups and unequal sample sizes) by the authors of the present study based on the available data. 9 THEORETICAL AND METHODOLOGICAL BACKGROUND

In consequence, demographic and performance data from 16 studies reporting effects of (pre)frontal lesions on planning ability were extracted (see Tables 1.1 and 1.2 for an overview). To allow for comparison of effect sizes between studies, if descriptive and/or inferential statistics were reported, Hedges’ g was calculated (L. V. Hedges & Olkin, 1985). If no statistical characteristics but only verbatim descriptions of observed results were reported in the original studies (e.g. Shallice, 1982: "the left anterior group was the slowest of all", p. 205) effects were listed descriptively (e.g. le > ri). Given the small overall number of studies together with the heterogeneity of reported information, a formal assessment in terms of a meta-analysis was not feasible. A brief overview and summary of the results is therefore listed below.

1.2.2.2 Frontally lesioned patients compared to healthy controls

A total of seven studies reported a reduced planning accuracy in patients compared to healthy controls (significant: Andrews et al., 2014; Goel & Grafman, 1995; Owen et al., 1990, 1995; Shum et al., 2009; not significant: Jacobs & Anderson, 2002; Pantelis et al., 1997) whereas only one study (V. Anderson et al., 2002) reported an opposite pattern (significantly higher accuracy in frontally lesioned patients). An increased number of executed moves as another index of impaired planning was observed for frontally lesioned patients in four studies (significant: Carlin et al., 2000; Owen et al., 1990; not significant: Mellier & Fessard, 1998; Pantelis et al., 1997). Although not significant, one study (Andrés & Van der Linden, 2001) found effects in the other direction (decreased number of executed moves). A prolonged planning time for lesioned patients was observed in four studies (significant: Pantelis et al., 1997; not significant: Andrés & Van der Linden, 2001; Jacobs & Anderson, 2002; Shum et al., 2009). One study reported a (not significant) reduction in planning time (Carlin et al., 2000). For another three studies no information on the direction of effects on planning time was available (Andrews et al., 2014; Owen et al., 1990, 1995). The movement execution time of patients was prolonged in four studies (all significant: Andrés & Van der Linden, 2001; Carlin et al., 2000; Owen et al., 1990; Pantelis et al., 1997).

10 The Neural Foundation of Planning 1.2

Table 1.3. Overview on the frequency and direction of effects on planning performance across lesion studies. Number of studies overall (number of studies with signiﬁcant effects or trends) Pat vs HC Pat < HC Pat > HC No direction reported Accuracy 7 (5) 1 (0) 0 Executed Moves 4 (2) 1 (0) 0 PT 4 (1) 1 (0) 3 MET 4 (4) 0 (0) 0

Left vs Right Frontal Left < Right Left > Right No direction reported Accuracy 3 (0) 0 (0) 2 Executed Moves 2 (0) 2 (0) 1 PT 2 (0) 3 (0) 2 MET 3 (0) 1 (0) 1 Note. PT, planning time; MET, movement execution time.

1.2.2.3 Left frontally lesioned compared to right frontally lesioned patients

With respect to the hemispheric lateralization of planning in well-structured tower tasks, none of the studies reported any significant findings, but descriptions of the effects’ directions. A total of three studies observed a lower accuracy in left lesioned versus right lesioned patients (Carlin et al., 2000; Goel & Grafman, 1995; Shallice, 1982) and another two studies (Owen et al., 1990, 1995) reported non-significant results without specifying the direction. The number of executed moves was increased for left lesioned versus right lesioned patients in two studies (Carlin et al., 2000; Morris et al., 1997a) and vice versa in another two studies (Glosser & Goodglass, 1990; Morris et al., 1997b), whereas one study reported (Owen et al., 1990) non- significant effects without specifying the direction. Planning time was increased for left lesioned patients in two studies (Morris et al., 1997b; Shallice, 1982) and for right lesioned patients in three studies (Carlin et al., 2000; Morris et al., 1997a; Rushe et al., 1999), another two studies (Owen et al., 1990, 1995) reported non-significant effects without specifying the direction. Movement execution time was prolonged for left lesioned patients in three studies (Carlin et al., 2000; Morris et al., 1997a; Rushe et al., 1999) and for right lesioned patients in one study (Morris et al., 1997b); again one study (Owen et al., 1990) reported non-significant results without specifying the direction. Taken together and summarized in Tables 1.1, 1.2 and 1.3, comparing frontally lesioned patients to healthy controls often results in a reduced planning accuracy and, occasionally, in an increased number of executed moves, while planning time and

11 THEORETICAL AND METHODOLOGICAL BACKGROUND

movement execution time are prolonged in some but not all cases. Neither a clear pattern nor any signiﬁcant ﬁndings emerges for comparing left and right frontally lesioned patients in common performance and latency measures of planning in well- structured tasks. However, when interpreting these results, it has to be borne in mind that the studies mostly consisted of small sample sizes and comprised a heterogeneous collection of etiologies (e.g., stroke, tumor, trauma) with different courses of disease and brain plasticity.

1.2.3 The Inﬂuence of Structuredness

The heterogeneity between brain lesion studies outlined in chapter 1.2.1 is all the more surprising given that these studies employed a circumscribed class of well- structured disc-transfer paradigms such as the ToL, ToH, or one of their variants (cf. Berg & Byrd, 2002). Featuring a similar physical appearance of three movable discs (or balls) placed on three pegs, tower tasks have in common that a start state has to be transformed into a given goal state in a minimum number of moves (Fig. 1.3, see also chapter 1.3.2). The task variants differ with respect to the rules and restrictions to be applied (Berg & Byrd, 2002; Kaller, Rahm, Köstering, & Unterrainer, 2011), but share at least partly comparable cognitive demands as optimal problem solutions can only be attained by mentally planning ahead. By stressing the conceptual differences between planning in such well-structured laboratory tasks versus the ill-structured nature of planning in real-world situations, Goel(2010) recently put forward the hypothesis that left and right PFC may differ- entially contribute to planning under well-structured and ill-structured constraints, respectively. A key distinguishing feature between planning in laboratory tasks (well- structured) compared to real-world situations (ill-structured) is that the initial and final states as well as the possible transformations are completely specified in the former, whereas these are not necessarily provided in the latter type of problems. In addition, constraints in laboratory problems are logical or constitutive of the task, whereas real-world problems encounter nomological as well as social, political, economic, cultural, and other constraints that are often not definitional or constitutive but

12 The Neural Foundation of Planning 1.2

negotiable dependent on the context (Goel, 2010). Outcomes in real-world planning are hence evaluated along a dimension of better or worse instead of a dichotomy of right or wrong. Recently, Goel(2015) published a review on the differences between the hemispheres regarding their ability to process determinacy and indeterminacy of information and relations. He argued that the left hemisphere works mostly as an interpreter that seeks full determinacy and certainty. The right hemisphere, however, not only copes with but rather enhances indeterminacy and uncertainty. Both hemispheres counterbalance each other’s pursuits for determinacy or indeterminacy and are necessary to successfully solve problems. Interferences on this balance, e.g. by hemispheric lesions, overreach a hemisphere’s share and, hence, result in suboptimal or non-successful solutions. Ill-structured planning tasks possess a higher degree of indeterminacy compared to well-structured tasks. In dealing with ill-structured problem representations, patients with right PFC lesions were repeatedly found to exhibit impairments in various aspects of real-world planning such as financial planning (Goel, Grafman, Tajik, Gana, & Danto, 1997), architectural design planning (Goel & Grafman, 2000), preparation of dinner (Penfield & Evans, 1935), and travel planning (Goel et al., 2013). The proposed asymmetric involvement of left versus right PFC in laboratory (i.e. well- structured) versus real-world (i.e. ill-structured) planning tasks is further corroborated by a similar dissociation in closely related cognitive domains, namely decision making and reasoning (Goel, Shuren, Sheesley, & Grafman, 2004; Goel, Stollstorff, Nakic, Knutson, & Grafman, 2009; Goel et al., 2007). Finally, it is argued that patients with right PFC lesions often perform well in laboratory planning tasks whereas they fail to do so in real life (Goel, 2010; Burgess, 2000). Given this rationale, one would hence expect a stronger involvement of left PFC in determined well-defined laboratory planning tasks such as the aforementioned ToL and other tower tasks (cf. Goel, 2010; Goel et al., 2013). In this regard, as summarized in Tables 1.1, 1.2, and 1.3, evidence on the lateralization of planning processes in well-structured tasks from previous lesion studies using the ToL and ToH is inconclusive, thus not corroborating the purported significance of left-lateralized PFC lesions for planning impairments under well-structured constraints. However, putative

13 THEORETICAL AND METHODOLOGICAL BACKGROUND

dissociations may be attenuated due to several methodological reasons such as the small number of lesion studies and their usually small sample sizes, together with a commonly high variability in extent, localization, and focality of lesions in PFC and beyond, as well as a high variability in functional impairments given heterogeneous etiologies and time courses of reorganization (Rorden & Karnath, 2004). The plethora of neuroimaging studies in healthy volunteers on the ToL may hence represent a more homogeneous foundation for assessing the proposed left lateralization of prefrontal contributions in laboratory planning tasks.

1.2.4 Overviews on Hemispheric Lateralization

Cazalis et al.(2003) provided a ﬁrst descriptive overview on eight ToL neuroimaging studies suggesting a relative preponderance of left dorsolateral prefrontal cortex (dlPFC) in well-structured planning, as one half of the considered studies reported a lateralization towards left dlPFC (Morris, Ahmed, Syed, & Toone, 1993; Owen, Doyon, Dagher, Sadikot, & Evans, 1998; Owen, Doyon, Petrides, & Evans, 1996; Rowe, Owen, Johnsrude, & Passingham, 2001), whereas the other half found bilateral dlPFC involvement (Baker et al., 1996; Elliott, McKenna, Robbins, & Sahakian, 1998; Dagher, Owen, Boecker, & Brooks, 1999; Lazeron et al., 2000). This qualitative overview was later extended by Kaller, Rahm, Spreer, Weiller, and Unterrainer(2011) who also listed a larger number of unilateral activation results in left (Beauchamp, Dagher, Aston, & Doyon, 2003; Morris et al., 1993; Owen et al., 1996, 1998; Rowe et al., 2001) versus right dlPFC (Dagher et al., 1999; van den Heuvel et al., 2003; Wagner, Koch, Reichenbach, Sauer, & Schlösser, 2006), but nonetheless emphasized that the overwhelming majority of studies reported bilateral dlPFC activations (Baker et al., 1996; Beauchamp, Dagher, Panisset, & Doyon, 2008; Boghi, Rampado, et al., 2006; Boghi, Rasetti, et al., 2006; Cazalis et al., 2003; de Ruiter et al., 2009; den Braber et al., 2008; Elliott, McKenna, et al., 1998; Fitzgerald et al., 2008; Just, Cherkassky, Keller, Kana, & Minshew, 2007; Lazeron et al., 2000; Newman, Carpenter, Varma, & Just, 2003; Schall et al., 2003; van den Heuvel et al., 2005) (see Table 1.4).

14 The Neural Foundation of Planning 1.2

Table 1.4. Laterality of dlPFC activity during planning compared to baseline. This table was reproduced from (Kaller, Rahm, Spreer, et al., 2011) References Method dlPFC activity Morris et al., 1993 SPECT Left Owen et al., 1996 PET Left Owen et al., 1998 PET Left Rowe et al., 2001 PET Left Beauchamp et al., 2003 PET Left Wagner et al., 2006 fMRI Left Baker et al., 1996 PET Left Elliott et al., 1997 PET Bilateral Lazeron et al., 2000 fMRI Bilateral Cazalis et al., 2003 fMRI Bilateral Newman et al., 2003 fMRI Bilateral Schall et al., 2003 fMRI Bilateral Lazeron et al., 2004 fMRI Bilateral van den Heuvel et al., 2005 fMRI Bilateral Boghi, Rampado, et al., 2006 fMRI Bilateral Boghi, Rasetti, et al., 2006 fMRI Bilateral Just et al., 2007 fMRI Bilateral Beauchamp et al., 2008 fMRI Bilateral den Braber et al., 2008 fMRI Bilateral Fitzgerald et al., 2008 fMRI Bilateral de Ruiter et al., 2009 fMRI Bilateral Dagher et al., 1999 PET Right van den Heuvel et al., 2003 fMRI Right Wagner et al., 2006 fMRI Right Andreasen et al., 1992 SPECT No effect Rezai et al., 1993 SPECT No effect Dagher et al., 2001 PET Not reported Cools et al., 2002 PET Not reported Schall et al., 2003 PET Not reported Unterrainer et al., 2004 fMRI Not reported Unterrainer, Ruff, et al., 2005 fMRI Not reported Rasser et al., 2005 fMRI Not reported Cazalis et al., 2006 fMRI Not reported Williams-Gray et al., 2007 fMRI Only patients Annotation. SPECT, single photon emission computed tomography; PET, positron emission tomography; fMRI, functional magnetic resonance imaging.

1.2.5 The Present Approach on Examining Neural Lateralization

However, these qualitative and descriptive overviews do not account for the spatial location of individual activation peaks, in particular in large brain regions such as the dlPFC, and can thus only reveal coarse trends across experiments, whereas robust evidence requires a meta-analytic approach on estimating the underlying real effect based on statistical criteria. Moreover, if reported peak voxels are compared across studies that are labelled equally, the homogeneity between these voxels is much less certain than the labelling indicates. Quantitative meta-analyses have a long history in clinical research (O’Rourke, 2007) but are a rather new methodology for functional neuroimaging with increasing popularity (e.g. Eickhoff, Amunts, Mohlberg, & Zilles, 2006; Farrell, Laird, & Egan, 2005; Price, Devlin, Moore, Morton, & Laird, 2005;

15 THEORETICAL AND METHODOLOGICAL BACKGROUND

Wager, Jonides, & Reading, 2004; Wager & Smith, 2003). They reveal the relationship between certain brain areas and certain behavior while being mostly independent from characteristics of samples and experimental procedures, task implementation, imaging parameters and artefacts, preprocessing, statistical thresholds and correction mechanisms. In chapter 2, this meta-analytic activation likelihood estimation (ALE) approach was used to examine the proposed lateralization of well-structured planning in laboratory tasks towards left dlPFC (cf. Goel, 2010). The meta-analysis was focused on functional neuroimaging studies using the ToL, the most frequently applied experimental paradigm on well-structured planning (Kaller, Rahm, Köstering, & Unterrainer, 2011). Given the large extent of the dlPFC including functionally entirely different parts of the middle and superior frontal gyri, another question concerned the precise spatial localization of planning processes within dlPFC. Although the neural correlates of planning are commonly associated with the whole dlPFC the ALE approach holds a promise to narrow down areas within the dlPFC.

1.3 The Cognitive Foundation of Planning

In chapter 1.2 the involvement of both left and right dlPFCs during well-structured planning was outlined (see also chapter 2). Opposing assumptions of an absolute hemispheric lateralization of planning, there are theoretical frameworks that postulate a relative lateralization where the left and right dlPFC perform different sub-processes that are all necessary for a successful problem solution (cf. chapter 4.2.1). For a further pursuit of this relative lateralization, a deepened understanding of the cognitive sub- processes is mandatory.

1.3.1 A Sequential Model of Planning

Planning and problem solving are often considered equal processes, employing mostly the same cognitive components, and have similar cognitive demands (Anzai & Simon, 1979; Klahr, 1994; Kotovsky et al., 1985; Simon, 1975; Zhang & Norman, 1994). There

16 The Cognitive Foundation of Planning 1.3

is a great variety of models regarding planning coming from different backgrounds (e.g. psychology, robotics, sociology), with foci on different aspects (e.g. cognitive or social) and different scopes (e.g. societal vs. individual). The most basic model of planning differentiated only between i) the cognitive skills of an individual and ii) the act of planning (De Lisi, 1987; Inhelder & Piaget, 1958). This model was created from a developmental point of view and tried to explain the observable discrepancy between the inability of infants to generate valid plans and the ability of adults to do so. Furthermore, the inconsistency between adolescents and adults who possess the same basic planning abilities but employ them to different degrees is addressed (cf. chapter 1.1)(De Lisi, 1987; Inhelder & Piaget, 1958). Based on this simple model, extended models were developed that considered additional cognitive components. These information processing models have in common that they all assume an initial construction of a representation of the problem space and a subsequent sequence generation to achieve the goal (e.g. Anzai & Simon, 1979; Friedman & Scholnick, 1998; Klahr, 1994; Kotovsky et al., 1985; G. Miller, Galanter, & Pribram, 1960; Pea & Hawkins, 1987). Moreover, these models integrate the execution and simultaneous sequence monitoring as crucial for the succeeding of a plan. Depending on the focus of the model, further components are considered: for instance the long-term memory and the individual’s knowledge base (Chi, Glase, & Rees, 1982; Hammond, 1990; Nurmi, 1991), individual skill sets that enable subjects to succeed in one planning task but fail in another (Kotovsky et al., 1985; Zhang & Norman, 1994), foresight, organizational skill, flexibility, and inhibition (Welsh & Pennington, 1988; Welsh, Pennington, & Groisser, 1991), and sequencing skill (Spitz, Webster, & Borys, 1982). In the present thesis the focus will be on the well-structured Tower of London (ToL) planning task as well as on the shared cognitive core components (see chapter 3) rather than inter-individual differences. For that reason the influence of many of the other aforementioned model components were not relevant for the studies of this thesis and, hence, were tried to be minimized. For example, as the ToL is a highly artificial laboratory task, the influence of previous knowledge and long-term memory can be disregarded (Goel, 1995, 2010) as all subjects in the studies reported here

17 THEORETICAL AND METHODOLOGICAL BACKGROUND

were naive to the ToL. All subjects in the study reported in chapter 3 were chosen only if healthy and in the same developmental state – early adulthood –, hence, the cognitive skills were assumed to be comparable (cf. chapter 3.2). The working model of chapter 3 was a strict sequential model and, therefore, comprised only the initial representation creation and the subsequent sequence generation.

1.3.2 The Tower of London and its Structural Parameters

The Tower of London (ToL) task is the most frequently applied experimental paradigm on planning (Kaller, Rahm, Köstering, & Unterrainer, 2011) and consists of two different states. Both ToL states comprise of three differently colored balls that are placed on three pegs. According to the original ToL variant, the three pegs differ in their height which determines their maximum ball capacity (Shallice, 1982). In the present thesis an adaption of the Ward-and-Allport-Tower-Task (WATT) was used (Ward & Allport, 1997), a variant of the ToL. In the WATT all pegs are of the same height and can, hence, accommodate all three balls (cf. Fig. 1.3 resp. 1.4). Concerning other rules and restrictions, the ToL and the adapted WATT are identical. That is i) only one ball can be moved at a time, ii) a move includes the picking-up of a ball from one peg and the putting-down on another peg (putting-down of a ball anywhere else than on a peg is prohibited), and iii) only the top-most ball of every peg can be moved. Different ToL problems have different number of minimal moves that they can be solved with. The subjects are asked to find the solution with the fewest moves possible and to plan ahead the whole solution prior to ball movements so as to ensure that planning is evoked rather than trial-and-error behavior. A ToL problem’s configuration of balls of the start and goal state, and how both states relate to each other can be described in terms of structural problem parameters. These also influence the amount of planning ahead that is necessary to successfully solve the problem. The most frequently manipulated structural parameter in research and clinical application is the minimum number of moves (Shallice, 1982; Kaller, Unterrainer, Rahm, & Halsband, 2004; for a review see Kaller, Rahm, Köstering, &

18 The Cognitive Foundation of Planning 1.3

A Search Depth B Tower Configuration

Exemplary ToL solution without intermediate move

partial

Goal Goal Goal full Start Goal

Move Move Move

full

Start Goal -

Exemplary ToL solution with intermediate move partial Start Goal

Inter-

mediate Goal Goal

partial

Move Move Move -

Start Goal partial Start Goal

Figure 1.4. Illustration of the structural parameters Search Depth and Tower Configuration. A. The Search Depth characterizes whether a problem needs an intermediate move to be solved or can be solved with three consecutive goal moves. Problems with intermediate moves invoke a higher amount of sequence generation. B. In three-move ToL problems with intermediate moves one ball is a dummy ball: In the illustrated problems the dark gray ball must not be moved. The move sequence that has to be planned is identical for all three problems, only the Tower Configurations of the start and the goal states are changed. Hence, Tower Configuration effects have to be independent from sequence generation but tap the representation creation instead.

Unterrainer, 2011). The minimum number of moves characterizes the number of moves that are necessary for an optimal problem solution with the fewest moves possible. In the vast majority of ToL studies, minimum number of moves have been used as the sole definition of problem difficulty (Kaller, Rahm, Köstering, & Unterrainer, 2011). However, as the minimum number of moves influences the difficulty of the ToL in a global fashion, it is insufficient for a fine-grained analysis of planning processes and further problem parameters have to be considered as well (Kaller et al., 2004). Overviews of the structural parameters of the ToL are given by Berg, Byrd, McNamara, and Case(2010) and Kaller, Rahm, Köstering, and Unterrainer(2011). For the present thesis the two structural parameters Search Depth and Goal Hierarchy resp. Tower Configuration are relevant. Search Depth is defined as the number of intermediate moves before the first goal move (also referred to as subgoaling) (Berg et al., 2010; Kaller, Rahm, Köstering, & Unterrainer, 2011; Kaller et al., 2004). A goal move is a move where a ball is placed into its final position. An intermediate move is a move where a ball is relocated to another peg but not in its final position. Nevertheless, intermediate moves are

19 THEORETICAL AND METHODOLOGICAL BACKGROUND

necessary for a successful solution of certain ToL problems. The Search Depth of a ToL problem thus determines how many (potentially counter-intuitive) moves have to be planned in advance before a first subgoal (a goal move) is achievable. Three-move ToL problems are an exception in terms of several structural parameters. With regard to Search Depth, three-move problems can be generated without any intermediate moves, hence (almost) no planning is necessary, and with one intermediate move, thus planning is necessary to avoid detours (needing additional intermediate moves to reach the solution) or dead ends (un-doing a move my moving the same ball on two consecutive moves). In chapter 1.3.1 the two sub-processes of planning representation creation and sequence generation are illustrated. Search Depth is assumed to affect solely the sequence generation (Kaller, Rahm, Bolkenius, & Unter- rainer, 2009; Nitschke, Ruh, Kappler, Stahl, & Kaller, 2012; Ruh, Rahm, Unterrainer, Weiller, & Kaller, 2012). The Goal Hierarchy is defined as the deducibility of the goal move sequence from the ball composition of the goal state (Berg et al., 2010; Kaller, Rahm, Köstering, & Unterrainer, 2011; Kaller et al., 2004). That is, if in the goal state all balls are stacked on the same peg (full goal tower), it is obvious that the bottom-most ball has to be placed in its goal position first, subsequently the middle ball and finally the top-most ball. In contrast, if all balls are on different pegs (flat goal state) one cannot deduce which ball has to be placed in its goal position first. Regarding Goal Hierarchy, three- move ToL problems constitute an exception as well. In a problem which only contains three balls and needs three moves to be solved, one of which one is an intermediate move (cf. Search Depth), one ball has to be moved twice. Correspondingly, another ball must not be moved in this problem and constitutes a dummy ball which is entirely irrelevant to the solution sequence (Nitschke et al., 2012). Three-move ToL problems can be generated that are identical in their solution move sequences but only differ in the position of the dummy ball (Fig. 1.4 B), hence, planning itself cannot be affected (Nitschke et al., 2012). Therefore, any effect that arises from the manipulation of the dummy ball location allows inferences about processes that are different from sequence generation. Instead, this structural parameter is assumed to influence how difficult the initial representation creation of a problem is (Nitschke et al., 2012).

20 The Cognitive Foundation of Planning 1.3

Given that the goal state configuration in 3-move-problems induces these specific processes, which are different from the cognitive demands in terms of actual planning by their goal sequence deducibility in problems with more than 3 minimum moves, the terminology Goal Hierarchy is replaced with Tower Configuration for 3-move problems (Kaller, Rahm, Spreer, et al., 2011; Nitschke et al., 2012). Both assumed sub-phases sequence generation and representation creation are not directly observable and, hence, difficult to disentangle. The structure of the 3-move ToL plays an exceptional role in chapter 3 and the entire cognitive research domain. In the 3-move ToL, Search Depth and Tower Configuration allow to properly segregate the two assumed subphases of the sequential model of planning (see chapter 1.3.1) by remaining fully independent of each other and, thus, allowing a non-confounded design. In other minimum move problems of the ToL (e.g. the 5-move ToL), the structural parameters are confounded with each other and not manipulable without restraints.

1.3.3 The Segmentation of Planning

1.3.3.1 Cognitive Architectures for Modeling Sub-processes

For a fairly long time the Tower of Hanoi (ToH) was a rather mildly interesting mathematical problem (Claus, 1884; Domoryad, 1963; Hinz, 1992) until it became of interest for psychological studies (J. Anderson & Douglass, 2001; Ewert & Lambert, 1932; Gagné & Smith, 1962). In the 1970’s, information processing gained a larger focus as a research area (Egan & Greeno, 1973, 1974; Hayes & Simon, 1974; Simon, 1975). The Tower of London was developed later to manipulate the difﬁculty levels of problems more easily and freely (cf. Fig. 1.4) (Shallice, 1982). Hence, especially early information processing studies focused on the ToH whereas in today’s studies the ToL is used predominantly (Kaller, Rahm, Köstering, & Unterrainer, 2011). Therefore, insights from both tower tasks, ToH and ToL, have to be considered in terms of planning research although results employing the two tower tasks are not completely inter-changeable given the different rules and the assumed differences in cognitive

21 THEORETICAL AND METHODOLOGICAL BACKGROUND

processes invoked by the ToH and ToL (Berg & Byrd, 2002; Bull, Espy, & Senn, 2004; Hinz, Kostov, Kneissl, Sürer, & Danek, 2009; Unterrainer, Rahm, Halsband, & Kaller, 2005; Welsh, Satterlee-Cartmell, & Stine, 1999; Zook, Davalos, Delosh, & Davis, 2004). Cognitive architectures (CAs) are one approach on modeling how the human mind operates and were used since the beginning of the information processing research. CAs are theoretical models that describe how the mind works in general but also which basic cognitive and perceptional operations, structures and mechanisms are necessary to provide functionality (cf. Langley, Laird, & Rogers, 2009). Originally, they were developed to explain concurrent study results of their era, hence are closely intertwined with the past research, and should not be disregarded when reviewing past insights regarding disc-transfer-tasks (i.e. ToH and ToL). The SOAR (State, Operator And Result) is a CA that describes how a problem solution could basically be processed by the mind. It assumes a solution sequence that begins at a starting state which is repeatedly transformed into other states until the goal state is achieved (J. Laird, Rosenbloom, & Newell, 1984, 1986; J. Laird, Newell, & Rosenbloom, 1987; Newell, 1990). These assumptions and derivable information appear rather basic but mark a change in mental models because prior models assumed a storage of the whole problem space (Fig. 1.5) in working memory and the solution generation constituted just the detection of a path through the problem space. In contrast, the SOAR assumed a step-wise advance that allocated only single interim states to working memory. In prior models, especially problems that require many moves would lead to an exponential growth of the problem space, which would overstrain the capacity of human working memory. These obvious prior misconceptions were overcome by the SOAR. In models that assume the entire problem space as mentally available, only the building-up of the problem storage would be mentally costly. The plan generation itself – ﬁnding the (shortest) path from the start state to the goal state through the problem space – would be independent of the characteristics of i) the single interim states and ii) their connections and would at most be inﬂuenced by the distance between start and goal state (in tower tasks the distance corresponds to the number of moves). In

22 The Cognitive Foundation of Planning 1.3

Figure 1.5. The problem space of the ToL. The problem space comprises all possible states and their interconnections, i.e. problem states that are achievable by relocating one ball. Hence, the solution of every possible ToL problem is the shortest path from any start to any goal state. This illustration was reproduced from Dehaene and Changeux(1997).

contrast, a model like the SOAR assumes state-by-state advance through the problem space where only one state is actively considered and reviewed for possible next steps. Mislead mental steps to seemingly favorable but actually incorrect interim states might occur because subsequent connections from this next state are not foreseeable yet. Therefore, the characteristics of an interim state and its connections can cause additional mental costs because they first have to be mentally reviewed and refused first. In a fully available problem space, however, these incorrect steps will not occur because subsequent connections are always foreseeable and do not have to be costly reviewed. When published, the SOAR approach was in line with findings using the ToH that reported additional mental costs when unobvious subgoal generation (by removing obstacles) was necessary (Klahr & Robinson, 1981; Klahr, 1985, 1994). A competing CA that addressed tower tasks is ACT-R (Adaptive Control of Thought-Rational) by John R. Anderson (J. Anderson et al., 1995; J. Anderson &

23 THEORETICAL AND METHODOLOGICAL BACKGROUND

Lebiere, 1998; J. Anderson et al., 2004). It specifies in more detail than SOAR how exactly working memory works from an information processing perspective (cf. Simon, 1975). ACT-R defines in detail how the creation, storage and the retrieval of goals (and subgoals) is supposed to be accomplished by the human mind. Therefore, it is of special interest for planning in tower tasks and the subgoal generation necessary to solve them. One basic assumption of ACT-R is that (sub)goal generation is a cognitive operation that needs time and processing capacity. The main difference to SOAR regarding disc-transfer tasks is that (sub)goals have to be stored and retrieved which are resource-intensive and fault-prone processes. At the time, ACT-R expanded the understanding of errors related to subgoaling that were repeatedly observed (J. Anderson & Douglass, 2001). Another important step towards the understanding of subgoaling in the ToL was a cognitive model presented by Dehaene and Changeux(1997). It assumes three hierarchically organized levels that increase in their complexity. The bottom-most level, the gesture level, recognizes the different spots on the pegs and, therefore, also points to the different balls. The medium level, the operation level, is responsible for moving balls which comprises of two gestures first the pointing to the present ball and second the pointing to the desired goal position. The topmost level, the plan level, works in a trial-and-error exploration of the problem space (cf. Fig. 1.5). It consists of three sub-units, the working memory unit which maintains previous steps, the plan unit which generates novel states, and the reward unit which accepts or rejects input from the plan unit. By systematically excluding and including specific sub-components of their model in simulations, Dehaene and Changeux(1997) could demonstrate that models which include an extra component for sub-goal moves outperform models that exclude such a component in terms of correctly predicting error rates and overall solution time. Hence, Dehaene and Changeux substantiated on the basis of simulated data that sub-goal moves have different cognitive demands and functioning than goal moves and, therefore, Search Depth has a crucial influence on the difficulty of a ToL problem. The presented CAs have in common that they assume, explain and model the undisputed costs of subgoaling and underlying mental processes. The developments of

24 The Cognitive Foundation of Planning 1.3

both CAs, SOAR and ACT-R, are still continued and gradually adapted to new research results. Hence, there is neither a better or worse CA but rather one CA that is better suited for certain insights. SOAR and ACT-R were presented exemplarily as historic as well as prevailing milestones in the investigation and understanding of mental sub-processes during problem solving and planning. The interest in CAs is unabated today as it is one approach pursued to create artificial intelligence by understanding and mimicking the human mind. Moreover, CAs are still used for cognitive modelling, e.g. for the ToL in ACT-R (Albrecht & Ragni, 2014). However, there is a multitude of different CAs available today (cf. Langley et al., 2009, Appendix), which makes their suitable employment more difficult especially when considering that every working model has to be implemented for all of the CAs’ languages (Langley et al., 2009). Most CAs favor logic and formalism albeit at times humans operate on more visual bases, not strictly logical, or against prior experience. A major caveat of CAs is their restrictiveness in terms of assumptions and considered modeling components. To reduce the ramifications of this limitation a better understanding of sub-processes is crucial.

1.3.3.2 Dissociating Components of Planning on Tower Tasks

In the previous sections were presented: i) the cognitive working model for the present thesis as consisting of the two distinct sub-processes, representation creation and the subsequent sequence generation (chapter 1.3.1), ii) how structural parameters of the ToL allow the manipulation of these sub-processes distinctly (chapter 1.3.2), and iii) how historical insights about tower tasks and the computational modeling approaches of mental processes are intertwined (chapter 1.3.3.1). In the following chapter speciﬁc insights regarding the sub-processes of planning from tower tasks are presented, compared and integrated chronologically, starting with studies solely employing structural parameters, followed by a study that introduced an eye movement analysis approach, and concluding with two studies that combined structural parameters and gaze pattern analysis.

25 THEORETICAL AND METHODOLOGICAL BACKGROUND

Klahr and Robinson(1981) tested children of different ages with the ToH to analyze age-dependent differences in planning strategies (see also Klahr, 1985). They used ToH problems with flat goal states (all discs on different pegs) and tower goal states (all pegs on the same peg). This approach is comparable to the Goal Hierarchy of the ToL (see chapter 1.3.2). They found problems with flat goal states to result in more errors than problems with tower goal states as is to be expected (cf. chapter 1.3.2). They also considered subgoaling effects (cf. Search Depth). However, in the ToH, the discs are interdependent (only smaller discs can be placed on larger discs) whereas in the ToL there is no such interdependence between the balls (all balls can be placed on all other balls). Thus, in the studies of Klahr and Robinson(1981) as well as Klahr(1985), these effects of subgoaling are not separable from goal configuration effects. In a ToL variant with five balls, Ward and Allport(1997) were able to demonstrate an effect of subgoal moves (identical to Search Depth) on the planning time and on the error rate. Unfortunately, the study used problems with up to ten moves. Combined with the five balls this led to a high correlation of the structural parameters subgoal move and subgoal chunks. Subgoal chunks describe a consecutive series of subgoal moves that relocate several balls from the same peg to another peg. The parameter subgoal chunks presumably combines the effects of subgoal moves (Search Depth) and Tower Configuration, because moving a stack of balls to another peg is rather one planning step including several dummy balls than several distinct planning moves. The reported results for planning time and error rates were observed for both subgoal moves and subgoal chunks and are not distinguishable in that study. However, Ward and Allport regarded subgoal chunks as more important because of a higher observed effect size. In 2004, Kaller et al. published a study where different structural parameters of three-move ToL problems were examined (Kaller et al., 2004). The aim was to demonstrate that the typically manipulated minimum number of moves are an imprecise and, hence, insufficient measurement of difficulty, particularly if other parameters are not controlled. Two of the structural parameters were Search Depth and Tower Configuration (in the article termed Goal Hierarchy, see also chapter

26 The Cognitive Foundation of Planning 1.3

1.3.2). Both parameters were found to significantly prolong the planning time. No interaction between both parameters was observed which might be a hint for two separately influenced processes but which does not constitutes sufficient evidence. The insights of Kaller et al.(2004), that both structural parameters, Search Depth and Tower Configuration, influenced the planning time, also provides an explanation for the results of Ward and Allport(1997), where subgoal moves ( Search Depth) were excelled by subgoal chunks. If subgoal chunks allegedly include the effects of Search Depth and of Tower Configuration, which both prolong the planning time individually, it necessarily excels the single effect of Search Depth in an additive fashion. However, subgoal chunks as an amalgam of both parameters is not adequate for fine-grained analyses. Taken together, first evidence for the crucial influence of structural parameters on planning as well as the potential of manipulating them was established. However, solely manipulating structural parameters without extending methods for measuring more subtle effects does not allow for deeper insights into planning sub-processes. Subsequent studies hence added additional measurements to detect these subtle effects. Eye movement analysis emerged as the most suitable methodology.

1.3.3.3 Insights from Eye Movement Studies

In an eye movement study, Hodgson, Bajwa, Owen, and Kennard(2000) used the ToL to achieve insights into different planning phases. In their ToL version the subjects did not execute their solution but indicated how many moves were necessary to solve each problem (cf. chapter 2.2). In the majority of the trials subjects initially gazed on the goal state (initial assessment). Afterwards they tended to inspect the start state (sequence generation) before they finally re-fixated the goal state (verification phase). Hodgson et al. used a vertical presentation of the start and goal state. Even when the states were interchanged, the described fixation pattern persisted. The initial fixation of the goal state constitutes first evidence towards the validity of a sequential model of planning (cf. chapter 1.3.1), where an initial representation creation was assumed.

27 THEORETICAL AND METHODOLOGICAL BACKGROUND

fixation empty towers planning phase solution phase fixation cross towers remaining cross

gaze on gaze on gaze on goal state goal state goal state

EOG signal final inspection before gaze on movement gaze on start state execution start state

fixation empty representation sequence final fixation movement execution cross towers creation generation verification cross

−500 0 500 1000 1500 2000 2500 3000 3500 4000 4500 Trial Time in ms

Figure 1.6. A prototypical EOG scan path during one ToL trial that starts with fixating the goal state and ends after several gaze shifts on the start state. Solely the very last fixation lengthened with a higher Search Depth, i.e. when an intermediate move was necessary. Therefore, the sequence generation was assumed to be isolated during this last fixation. This illustration was adapted from Kaller et al. (2009).

Kaller et al.(2009) combined the approaches of the studies of Kaller et al.(2004) as well as Hodgson et al.(2000) by manipulating Search Depth while simultaneously recording eye movements in the three-move ToL. They did not analyze effects on the error rate but replicated the effect of Search Depth on a prolonged planning time (cf. Kaller et al., 2004). In contrast to Hodgson et al.(2000), Kaller et al.(2009) used a horizontal start and goal state presentation as well as a ToL version where the actual solution execution was performed. However, comparable to Hodgson et al. (2000) they also interchanged the start and the goal states to control for a possible location bias. They found no tendency of an initial inspection of the goal state (cf. Hodgson et al., 2000) but a bias for an initial fixation of the left state independent of which state was presented there. Moreover, they found a strong tendency of finally inspecting the start state right before the solution execution began independent of the side the start state was presented on (Fig. 1.6). This seems to contrast the results from Hodgson et al.(2000), who found a final fixation of the goal state (cf. verification phase). However, given that in the study by Hodgson et al.(2000) solutions had not to be executed, in the study of Kaller et al.(2009) the verification phase might transfer into the execution phase. Before the verification phase, Hodgson et al.(2000) assumed the sequence generation to take place which occurs while the start state is

28 The Cognitive Foundation of Planning 1.3

fixated (cf. Hodgson et al., 2000). That characterized phase corresponds to the final fixation of the start state in Kaller et al.(2009). This explanation is supported by the findings about Search Depth which is assumed to solely tap the sequence generation phase (cf. chapters 1.3.1 and 1.3.2). Kaller et al.(2009) analyzed the fixations on the start and goal state separately during the course of the planning phase and found that exclusively the last fixation of the start state (before the execution started) was prolonged by Search Depth but none of the other fixations (cf. Fig. 1.6). This finding constitutes further evidence for a sequential model of planning where after an initial representation creation the sequence generation is assumed to follow (cf. Fig. 1.6). This also supports the explanation regarding the accordance between sequence generation and Hodgson et al.’s (2000) sequence generation. However, this explanation approach was still to be confirmed with experimental evidence. Nitschke et al.(2012) pursued two goals with their study. First, they aimed at extending the evidence for a sequential model of planning and, second, they targeted the afore-mentioned discrepancy between the results of Hodgson et al.(2000) and Kaller et al.(2009) regarding planning phases. Similar to Kaller et al.(2009), Nitschke et al.(2012) used the three-move ToL with actual solution execution and manipulated Search Depth in order to tap the sequence generation phase in four independent groups. The findings of Kaller et al.(2009) of i) a strong tendency to gaze at the start state right before solution execution (cf. Fig. 1.6) and of ii) an isolated prolongation of only the very last fixation with a higher Search Depth were replicated by Nitschke et al.(2012) in all groups (Fig. 1.7 and Nitschke et al., 2012, Supplementary Material), hence verifying the sequence generation phase. The sequential model of planning1 additionally assumes the representation creation phase. Kaller et al.(2009) concluded that during the time before the sequence generation started the representation creation phase takes place. However, they only referred to this phase with indirect inference and plausibility by demonstrating the isolated sequence generation. Nitschke et al.(2012) examined the representation creation phase directly by additionally manipulating Tower Configuration

1In both publications, Kaller et al.(2009) and Nitschke et al.(2012), representation creation was labeled internalization and sequence generation was labeled planning.

29 THEORETICAL AND METHODOLOGICAL BACKGROUND

Figure 1.7. The effects of Tower Configuration (A and C) and Search Depth (B and D) on the number of gaze shifts (A and B) and on the last fixation duration (C and D). A double dissociation was observed where Tower Configuration solely influenced the number of gaze shifts whereas Search Depth influenced solely the last fixation duration, hence, verifying the two distinct planning phases representation creation and sequence generation. This illustration was reprinted from Nitschke et al.(2012).

which is assumed to i) leave the sequence generation phase unaffected, but ii) to affect visual matching processes occurring when the initial mental representation of a problem is created (for a detailed explanation see chapter 1.3.2). Nitschke et al.(2012) found that the very last fixation before the solution execution was unaffected by Tower Configuration (Fig. 1.7), thus, the sequence generation remained unaffected as expected. The durations of all fixations before the very last inspection were unaffected by both Search Depth and Tower Configuration. However, the number of fixation shifts between the start and the goal state before the very last inspection increased with higher difficulty of Tower Configuration (Fig. 1.7) substantiating the inferred assumption

30 The Cognitive Foundation of Planning 1.3

of Kaller et al.(2009) that Tower Configuration affects the phase of representation creation. Taken together, the double dissociation (Fig. 1.7) demonstrated by Nitschke et al.(2012) provided evidence for two separable phases – representation creation and sequence generation – as assumed by a sequential model of planning. Another aim of Nitschke et al.(2012) was to resolve the discrepancies between the results of Hodgson et al.(2000) and Kaller et al.(2009). Kaller et al.(2009) demonstrated that the isolated planning phase within the last fixation which was independent of the location of the start state. In contrast, Hodgson et al.(2000) reported an additional verification phase but similarly independent of the start state location. There were three major methodological differences between the two studies that might have caused this discrepancy: i) spatial arrangement of start and goal state, ii) eye measurement technique, and iii) whether an actual solution execution was performed. Hodgson et al.(2000) used a vertical spatial arrangement whereas Kaller et al. (2009) used a horizontal presentation of the start and the goal states. Horizontal and vertical eye movements naturally differ from each other, e.g. in their onset, speed and smoothness (Rottach et al., 1996). The cause of the differences between vertical and horizontal eye movements is still unclear and may be connected to the horizontal arrangement of the eyes on the human’s head and the higher natural occurrence of horizontal trajectories (Rottach et al., 1996) or to reading habits (Seo & Lee, 2002; Yu, Park, Gerold, & Legge, 2010). To ensure the independence of Kaller et al.’s evidence of the sequence generation phase, all possible spatial locations of the start and goal state of the ToL were examined by Nitschke et al.(2012): Four groups comparable in size, age and sex ratio were assessed. Two groups were presented with a vertical arrangement (one group with the start state on the top and one group with the start state on the bottom screen half) (similar to Hodgson et al., 2000) and the other two groups were presented with a horizontal arrangement (one group with the start state on the left and one groups with the start state on the right screen half) (similar to Kaller et al., 2009). All results regarding the isolated representation creation and sequence generation phases were found in all four groups and are, therefore, independent of the specific spatial arrangement (see Nitschke et al., 2012 and Supplementary Materials).

31 THEORETICAL AND METHODOLOGICAL BACKGROUND

Both studies used different eye measurement techniques, Kaller et al.(2009) employed electrooculography (EOG) wherease Hodgson et al.(2000) employed video- oculography (VOG)2. To ensure the independence of Kaller et al.’s (2009) results of the eye tracking technique, Nitschke et al.(2012) examined all four groups with the same technique. Although EOG of vertical eye movements is possible, its stability compared to horizontal EOG is essentially lower (Chang, Cha, & Im, 2016; Joyce, Gorodnitsky, King, & Kutas, 2002). VOG is based on a camera ﬁlming the eye and its movements and does not suffer from the instability regarding vertical eye movement measurement. Therefore, VOG was utilized by Nitschke et al.(2012) to ensure comparable validity of horizontal and vertical results and, hence, to ensure that possible differences are ascribable to human behavior rather than to the employed technique. Nitschke et al. found the distinct sequence generation phase in all four groups employing Search Depth, thus showing that these results (cf. Kaller et al., 2009) are independent of the employed eye tracking methodology. Two of the three listed methodological differences between Hodgson et al.(2000) and Kaller et al.(2009) were addressed by Nitschke et al.(2012). The remaining difference was the actual execution of the planned problem solution (Kaller et al., 2009) or just the indication on how many moves were necessary (Hodgson et al., 2000). Both variants are approximately equally common in planning studies (cf. chapter 2.2, Table 2.1) and are assumed to evoke the same planning mechanisms. This assumption is supported by the results of the meta-analysis described in the study of chapter 2 where no differences between the actual execution and the move number indication were observed in neural activity in planning-relevant brain areas (see chapter 2.5 for details). However, a caveat of this substantiation was the small number of studies available for contrast analyses and, hence, the lack of statistical power for detecting potential more subtle differences. Thus, this unresolved issue is still to be addressed by future studies. The independence of Kaller et al.’s (2009) results regarding an isolated sequence generation phase of the spatial arrangement and eye tracking technique shown by Nitschke et al.(2012) hence provide ﬁrst – although

2 For a more detailed description of both eye tracking methodologies see chapter 1.4.2.

32 The Cognitive Foundation of Planning 1.3

only implicit – evidence that the discrepancies between the findings of Hodgson et al. and Kaller et al. might be rooted in whether the problem solution is actually executed. Another gap of knowledge concerns the scope of cognitive demands of sequence generation and representation creation. It is unanimously assumed that the sequence generation sub-phase is cognitively highly demanding as several moves and their outcomes are mentally generated and evaluated (e.g. Hodgson et al., 2000; Kaller et al., 2009; Nitschke et al., 2012). In contrast, the detailed functionality of the representation creation and its cognitive demands is still elusive. On the one hand, key balls of a certain problem might be identified by mere visual comparison of start and goal state thus leaving representation creation a cognitively undemanding process. On the other hand, the goal (and possibly the start) state might be transferred into the working memory completely thus demanding cognitive resources during representation creation. Insights in the functionality of representation creation are rather vague. This might be due to structural differences between different planning tasks or even the number of moves within the same planning task. It is plausible that three balls and their positions of a goal state of the original ToL (cf. Shallice, 1982) in three move problems are fully memorized whereas the start-goal matching in variants with five balls and ten moves (cf. Ward & Allport, 1997) is processed in a rather visual fashion. Other planning tasks have completely different requirements and the representation creation might deviate entirely. This heterogeneity between and within studies with different moves aggravates an overview of and the insights into the functionality of representation creation. Taken together, Hodgson et al.(2000), Kaller et al.(2009) and Nitschke et al. (2012) were able to provide evidence for two distinct planning phases – representation generation and sequence generation – employing eye tracking while manipulating structural parameters of the ToL and, hence, established the validity of a sequential model of planning.

33 THEORETICAL AND METHODOLOGICAL BACKGROUND

1.3.4 The Present Approach on Cognitive Processes of Planning

Pupillometry is a traditional method of linking processes which are not directly observable with a directly observable measure and is known at least since the 16th century (Andreassi, 2000). Connections between the dilation of pupils and cognitive processes could be established for several functions, e.g. arithmetics (Hess & Polt, 1964), language processing (Ahern & Beatty, 1979; Beatty, 1982a; Hyönä, Tommola, & Alaja, 1995), stimulus-response matching (Beatty & Wagoner, 1978; Poock, 1973), short- and long-term memory (Beatty, 1982b; Kahnemann & Beatty, 1966), and working memory (Granholm, Asarnow, Sarkin, & Dykes, 1996; Granholm, Morris, Sarkin, & Asarnow, 1997; Klingner, Tversky, & Hanrahan, 2011; Van Gerven, Paas, Van Merrienboer, & Schmidt, 2004) (see chapter 1.4.3 for further details). Despite the great range of different functions that were examined, the results are outstandingly consistent, namely that cognitive processing leads to higher pupil dilation. Hodgson et al.(2000), Kaller et al.(2009) as well as Nitschke et al.(2012) were able to distinguish different sub-processes of planning from each other and related them to specific eye movement and fixation patterns. However, this distinction was only possible because the ToL is a well-defined and artificial laboratory planning task (see chapter 1.2.3). Well-defined planning tasks are necessary to understand the architecture and components of human planning but, as Goel(2010) stated, have limited ecological validity. In the ToL, the goal and the start state are spatially separated which is a necessity to distinguish eye fixations from each other. In ill- defined tasks or in natural scenes, this separation will not be that clear and will only be classifiable (if at all) with enormous effort. Other interferences are multiple information sources, imagined or recalled information and prior knowledge that will make it more difficult to detect meaningful, separable gaze instances or even render that completely impossible (Chi et al., 1982; Hammond, 1990; Nurmi, 1991). Thus, another measurement approach besides gaze fixations that reliably indicates the current phase of planning would be of great value. Additionally, another methodology that replicated the findings of Nitschke et al.(2012) would further strengthen evidence for the validity of the sequential model of planning. In chapter 3, a study is presented

34 Methodological Background 1.4

that focuses on pupil diameter change during the planning process in the ToL in general as well as the distinct impacts of the structural parameters Tower Conﬁguration and Search Depth.

1.4 Methodological Background

1.4.1 Activation Likelihood Estimation

Literature reviews and meta-analyses are common and important methods to robustly and most undistortedly estimate effects in their occurrence and size. Literature reviews list and compare available studies on a specific research topic and typically reach a conclusion on that topic in a qualitative manner. In contrast, meta-analyses collect statistical parameters and calculate an overall effect in a quantitative manner. There- fore, meta-analyses are to be preferred to literature reviews because - if conducted soundly - they provide statistical evidence that is free of human interpretational errors. Meta-analyses are a broadly approved methodology with a rich history, for example in clinical research (O’Rourke, 2007). However, for several reasons performing meta- analyses is not always reasonably possible, for example if approaches on a topic are too different, if necessary parameters were not reported, or if the number of studies on a subject is too small. Neuroimaging studies help understanding the functionalities and disease-related dysfunctionalities of the brain. The methodology of neuroimaging is, however, relatively young and presents special challenges for meta-analyses. Most imaging studies suffer from small sample sizes, low reliability and other methodological constraints (Feredoes & Postle, 2007; Price et al., 2005; Raemaekers et al., 2007; Stark & Squire, 2001) which emphasize the need for meta-analyses in this field of research. First attempts on meta-analyses in neuroimaging started by textual and graphical summaries (Joseph, 2001; Peyron, Laurent, & García-Larrea, 2000). It has progressed since to quantitative methods that detect significant clusterings of reported brain coordinates (Eickhoff et al., 2006; Farrell et al., 2005; Price et al., 2005; Wager et al., 2004; Wager & Smith, 2003).

35 THEORETICAL AND METHODOLOGICAL BACKGROUND

The activation likelihood estimation (ALE) is the most common meta-analytic approach in neuroimaging (A. Laird et al., 2005; P. E. Turkeltaub, Eden, Jones, & Zefﬁro, 2002; Wager, Lindquist, & Kaplan, 2007) and, hence, was employed in the study reported in chapter 2. In an ALE analysis the reported coordinates of the collected studies are not treated as points in space but as spatial probability distributions instead. These probability distributions are tested with permutation tests against random spatial distributions with the same number of coordinates. These calculations result in p-value maps that indicate above-chance clustering of reported coodinates for each voxel of the brain (Eickhoff et al., 2009; Eickhoff, Bzdok, Laird, Kurth, & Fox, 2012) which provides evidence for the involvement of particular brain areas for a certain task.

1.4.2 Eye Movement Measurement

Research that utilized eye movement measurement dates back to the 19th century (Tatler & Wade, 2003; Wade, Tatler, & Heller, 2003). Different research questions and tracking techniques yielded multiple eras of eye measurement (Rayner, 1978, 1998). Enhancements of precision, sampling rate, simplicity of application and advent of more non-invasive techniques led to a prosperity of eye tracking in psychological studies (Rayner, 1998). In today’s research mainly two techniques are employed: electrooculography (EOG) and video-oculography (VOG). During VOG the eye is recorded and analyzed frame by frame. Today’s computational power allows an online analysis of the gaze data. The basis of the gaze detection is the constant corneal reflection (1st Purkinje image) of a head-related stable light source, i.e. an infrared light (Richardson & Spivey, 2004). Additionally, the pupil – easily detectable due to its darkness compared to the illuminated rest of the eye – is identified. The reflection and the pupil are recorded and set in relation with each other and, hence, after a calibration the gaze direction is derivable (Duchowski, 2007). EOG is based on the measurement of the electric field of the eye. The retina contains a surplus of electrons which is caused by an increased metabolism compared to the cornea. Hence, the human eye is a dipole with the retina as negative and the

36 Methodological Background 1.4

cornea as positive pole (Eggert, 2007; Mowrer, Ruch, & Miller, 1935). During EOG measurement two or four electrodes are attached to the subject’s head depending on whether only horizontal or only vertical eye movements are of interest or eye movements in both dimensions. An eye movement changes the magnetic ﬁeld caused by the eyes’ dipoles which is measured as potency difference by the electrodes. After an initial calibration the potency differences allow to meter gaze shifts. Both techniques, VOG and EOG, hold different advantages and disadvantages that determine their adequacy for different research questions and circumstances. In the study reported in chapter 3 VOG was employed due to several reasons. For an illustration of the topic-related considerations see chapter 1.3.4. Two methodological considerations suggested VOG: i) the setup and calibration of VOG is faster and more straightforward compared to EOG and ii) the comparison between the different spatial arrangements – especially the comparison between horizontal and vertical arrangements – are less vulnerable for different methodological issues. Another advantage of VOG over EOG is that most of the different available VOG equipment allows the additional measurement of the pupil dilation without additional measurement effort (see chapter 1.4.3).

1.4.3 Pupillometry

In most mammals the pupil is a circular hole in the eye ball that allows light to fall on the retina on the inner back of the eye ball and thus enables vision (Sirois & Brisson, 2014). The size of the hole is regulated by the iris to adapt to differently illuminated conditions and, thus, to optimize vision. Besides the pupillary light reﬂex, a link between the pupil dilation and the present state of mind was already hypothesized in the antiquity as the eyes were seen as "windows to the soul" (frequently ascribed to Cicero) (Sirois & Brisson, 2014). Early scientiﬁc interests in this link are attestable in the 16th century (Andreassi, 2000). Today, pupillometry is the study of the pupil size patterns and deducible states and changes of the mind. Pupil dilation was linked to a large number of social, language, and cognitive processes. Pupil dilation was associated with emotional arousal (Bradley, Miccoli,

37 THEORETICAL AND METHODOLOGICAL BACKGROUND

Escrig, & Lang, 2008), sexual arousal (Hess & Polt, 1960; Hess, Seltzer, & Shlien, 1965; Rieger & Savin-Williams, 2012; Simms, 1967), face perception (Honma, Tanaka, Osada, & Kuriyama, 2012; Blackburn & Schirillo, 2012), emotion perception (Duque, Sanchez, & Vazquez, 2014; Kret, Roelofs, Stekelenburg, & de Gelder, 2013; Kret, Stekelenburg, Roelofs, & de Gelder, 2013), higher oxytocin levels (Prehn et al., 2013), human touches (Ellingsen et al., 2014), lying (Dionisio, Granholm, Hillix, & Perrine, 2001), and pain perception (Chapman, Oka, Bradshaw, Jacobson, & Donaldson, 1999; Ellermeier & Westphal, 1995; Höﬂe, Kenntner-Mabiala, Pauli, & Alpers, 2008). For cognitive processes links were found for attention (J. L. Bradshaw, 1968; Kahneman, 1973), reasoning (Prehn, Heekeren, & van der Meer, 2011), arithmetics (Bradshaw, 1967; J. L. Bradshaw, 1968; Hess & Polt, 1964; Payne, Parry, & Harasymiw, 1968), cognitive load (Siegle, Ichikawa, & Steinhauer, 2008), decision making, conﬂict resolution, reward contingencies (Cavanagh, Wiecki, Kochar, & Frank, 2014; Chiew & Braver, 2013; de Gee, Knapen, & Donner, 2014; Jepma & Nieuwenhuis, 2011; Kahneman & Beatty, 1967; van Steenbergen et al., 2013), stimulus-response matching (Beatty & Wagoner, 1978), and working, short- and long-term memory (Ariel & Castel, 2014; Beatty, 1982b; Elshtain & Schaefer, 1968; Goldinger & Papesh, 2012; Kafkas & Montaldi, 2012; Kahnemann & Beatty, 1966; Otero, Weekes, & Hutton, 2011; Papesh, Goldinger, & Hout, 2012; Van Gerven et al., 2004). Pupillometry in humans is conducted via direct observation or different kinds of picture recording. In early research direct observation was the only available method; in later research photography was mostly employed (Bellarminow, 1885; Garten, 1897; Lowenstein & Loewenfeld, 1958; Reeves, 1920). In today’s research almost exclusively VOG (see chapter 1.4.2) is employed (Sirois & Brisson, 2014). Since VOG is based on the pupil recording to recognize the gaze direction, this information is already detected and processed. Computationally negligible corrections and adjustments to the raw data have to be applied to make the pupil dilation available and suitable for analyses (Brisson et al., 2013). Deviant aquisition methods from the common image recordings might include the two muscles that control the contractions of the iris or the respective associated autonomic nervous system: the dilator pupillae resp. sympathetic system and the sphincter pupillae resp. parasympathetic system (Goldwater, 1972).

38 Aims and Objectives of the Thesis 1.5

The central nervous system renders another assessment possible: The subcortical structure locus coeruleus (Koss, 1986; Laeng, Sirois, & Gredeback, 2012; Samuels & Szabadi, 2008) as well as the noradrenergic system (Aston-Jones & Cohen, 2005; Laeng et al., 2012; Sara, 2009) were closely associated with pupil dilation and already used in single-cell recordings in monkeys (Costa & Rudebeck, 2016; Joshi, Li, Kalwani, & Gold, 2016; Rajkowski, Majczynski, Clayton, & Aston-Jones, 2004). Taken together, VOG provides a non-invasive, well-established, quick-to-set-up, and economic method to track pupil changes. For these reasons, the experiment described in chapter 3 employed this method to conduct pupillometry.

1.5 Aims and Objectives of the Thesis

The aims of this thesis were to further ascertain the understanding of the neural and the cognitive processes underlying human planning as well as to compare and update available theories. To reach these aims, the structure of this thesis will be as follows: First, the neural foundations of planning will be investigated. There are several contradicting theories on how the brain is lateralized in its functionalities. In a quantitative meta-analysis of the brain areas and their lateralization involved during planning the ToL task will be examined. Therefore, complementing the results of individual study results a larger and more robust picture about the neural foundations of planning will be revealed in chapter 2. Second, the cognitive sub-processes of planning will be dissected by an additional methodology to the already established ones.The sequential model of planning assumes two distinct processes: representation creation and the subsequent sequence generation. First evidence was provided for both independent sub-processes. These insights are extended and substantiated by the yet unemployed methodology of pupillometry while manipulating structural parameters of the ToL. Pupillometry was linked to a large number different cognitive processes, such as cognitive load. In chapter 3 pupillometry will be examined to validate and exceed ﬁndings of former eye movement studies and to establish its validity as indicator for planning sub-processes.

39 THEORETICAL AND METHODOLOGICAL BACKGROUND

In the discussion part of this thesis current theories on functional lateralization of the brain in planning will be introduced, compared and evaluated according to the meta-analysis results. An approach will be undertaken to unify the available theories with each other and the insights gained from the meta-analysis. The potential and caveats of pupillometry for its employment in cognitive dissociation studies will be reviewed critically. At present the neural and cognitive research domains remained relatively separated. In a detailed outlook the combination of both domains is discussed.

40 A Meta-Analysis on the 2 Neural Basis of Planning: Activation Likelihood Estimation of Functional Brain Imaging Results in the Tower of London Task

2.1 Theoretical Background

The functional anatomy of planning processes in the prefrontal cortex (PFC) and the concomitant question of hemispheric lateralization have been a matter of interest since the seminal study of Shallice(1982) on the Tower of London (ToL) task. In that study, patients with left anterior brain lesions were found to have substantial decrements in planning accuracy and increased planning times when compared to patients with right anterior lesions as well as with left and right posterior lesions (Shallice, 1982). Lateralization of planning processes in the ToL was thereafter addressed in several neuropsychological studies with brain-lesioned patients. But as summarized in Box 1, effects of lateralization of (pre)frontal lesions on planning accuracy (in terms of optimal solutions within the minimum number of moves) and planning times could not be found in any of these subsequent studies (Tables 1.1 and 1.2) (see also Sullivan, Riccio, & Castillo, 2009). This chapter was published as a peer-reviewed journal article as: Nitschke, K., Köstering, L., Finkel, L., Weiller, C., & Kaller, C.P. (2017). A Meta-Analysis on the Neural Basis of Planning: Activation Likelihood Estimation of Functional Brain Imaging Results in the Tower of London Task. Human Brain Mapping, 38(1), 396-413.

41 A META-ANALYSIS ON THE NEURAL BASIS OF PLANNING

However, effects of prefrontal lateralization may have been attenuated in lesion studies due to several methodological reasons: The number of lesion studies on the topic is small and their sample sizes are mostly limited as well. Furthermore, there is commonly a high variability in extent, localization, and focality of lesions in PFC and beyond, as well as a high variability in functional impairments given heterogeneous etiologies and time courses of brain plasticity and reorganization following brain injuries (cf. Rorden & Karnath, 2004). Hence, the plethora of neuroimaging studies in healthy volunteers on the ToL may represent a more homogeneous foundation for examining the functional anatomy of planning processes and for addressing questions concerning the localization and lateralization in the PFC. Most notably in this respect, although planning is commonly associated with the mid-dorsolateral prefrontal cortex (dlPFC) (cf. Unterrainer & Owen, 2006), the surprisingly wide-spread distribution of prefrontal peak voxels derived from 31 neuroimaging studies on the ToL (see Fig. 2.2) does not imply a clear focus of planning-related neural activation within mid-dlPFC and thus runs counter to this common tenet. The term "dlPFC" is in fact used to refer to an extended prefrontal area that covers parts of the middle and superior frontal gyri and thus includes the functionally different, Brodmann areas (BA) 9, 9/46, 46, and parts of 8 and 10 (Petrides, 2005). Therefore, the precise spatial localization of planning processes within the dlPFC is highly relevant for correctly identifying the neural underpinnings of planning processes. That is, adding to the unresolved issue of a possible lateralization of PFC involvement in planning (cf., Tables 1.1 and 1.2) (see also Cazalis et al., 2003; Kaller, Rahm, Spreer, et al., 2011), the often assumed association with its mid-dorsolateral part (e.g. Unterrainer & Owen, 2006) remains to be empirically conﬁrmed by a quantitative examination of activation coordinates across studies. Quantitative meta-analyses have a long history in clinical research (O’Rourke, 2007) but have also become increasingly popular in functional neuroimaging (e.g. Eickhoff et al., 2006; Farrell et al., 2005; Price et al., 2005; Wager et al., 2004; Wager & Smith, 2003), as they allow assessing brain-behavior relationships independent of study-speciﬁc confounds such as characteristics of samples and experimental procedures, task implementation, imaging parameters and artefacts, preprocessing,

42 Methods 2.2

statistical thresholds, and correction mechanisms. As one of the most common algo- rithms for coordinate-based meta-analyses, activation likelihood estimations (ALE) are computed for the distribution of reported activation peaks of all included studies and compared against random distributions to account for the spatial nature of neuroimaging results (Eickhoff et al., 2009; A. Laird et al., 2005; P. Turkeltaub et al., 2012). In the present study, we therefore adopted this metaanalytic ALE approach with the aim of examining the functional anatomy of planning processes and particularly with the aim of resolving the open questions on the speciﬁc localization and the hemispheric lateralization of PFC involvement. To this end, the meta-analysis was based on functional neuroimaging studies using the ToL, as it constitutes the most frequently applied experimental planning paradigm in clinical and cognitive neuroscience (Kaller, Rahm, Köstering, & Unterrainer, 2011).

2.2 Methods

2.2.1 Study Selection

The web-hosted databases PubMed (www.ncbi.nlm.nih.gov/pubmed), ISI Web of Knowledge (www.webofknowledge.com), Science Direct (www.sciencedirect.com) and PsycINFO (www.apa.org/psycinfo) were searched using pair-wise combinations of the key term ToL with the following terms: fMRI, functional magnetic resonance imaging, PET, positron emission tomography, SPECT, single photon emission computed tomography, NIRS, near infrared spectroscopy, EEG, electroencephalography, planning, problem solving, brain imaging, functional imaging, activity. In addition to these brain-imaging specific search terms, the key term ToL was also combined with the broader terms planning and problem solving to maximize chances for detecting all relevant studies. The present literature search included all studies published until December 2014. As illustrated in the flow chart in Figure 2.1, 636 individual reports were identified. During the subsequent selection process, abstracts and/or manuscripts were screened and 565 studies excluded as not relevant for the topic. Of the remaining

43 A META-ANALYSIS ON THE NEURAL BASIS OF PLANNING

Studies identified through Studies excluded (n=236): database search (n=636) No peer reviewed article (n=94) Unrelated topic (n=129) Literature reviews (n=13)

Studies excluded (n=369): Studies screened (n=400) No functional brain imaging technique (n=323) No disc-transfer task (n = 1) Other disc-transfer task than ToL (n=5) Patients only, no healthy controls (n=5) Data re-analyses (n=6) No activation coordinates reported (n=16) Studies included in ROI-analyses (n=9) quantitative analysis (n=31) No Overall Planning condition (n=4)

Figure 2.1. Flow chart illustrating the step-wise study selection procedure.

71 functional imaging reports, another 40 studies did not qualify for the present meta-analysis. A ﬂowchart of the selection process is provided in Figure 2.1. The ﬁnal selection consisted of 31 independent datasets that were published in 31 neuroimaging papers3 (Table 2.1). All included studies used three-ball versions of the ToL with either three differently sized pegs (n = 16) (cf. Shallice, 1982) or three equally sized pegs (n = 3) (cf. Ward & Allport, 1997), or the Stockings of Cambridge (SoC) version with differently sized pockets (n = 510) (cf. Owen et al., 1990). Furthermore, studies differed with respect to the response modes (cf., solution execution in Table 2.1): In some studies, subjects were asked to solve the problems by executing the individual moves (n = 8), whereas in other studies, subjects only had to indicate the minimum number required for the optimal solution of a given

3Note in this respect that Elliott et al.(1997) reported a reanalysis of the data of Baker et al.(1996), whereas Owen et al.(1998) reported a reanalysis of the data of Owen et al.(1996). However, as the analyses on Overall Planning in the original studies were extended in both follow-up studies by analyses of Planning Complexity, this additional information was included in the present meta-analysis. Schall et al.(2003) and van ’t Ent, den Braber, Rotgans, de Geus, and de Munck(2014) reported two independent samples each. den Braber et al.(2008) examined twins where one twin suffered obsessive-compulsive symptoms. Only the reported activations of the healthy twins were included in the present ALE analyses. de Ruiter et al.(2009) examined problem gamblers, smokers, and healthy controls. They reported only the activation foci across all groups, as they did not ﬁnd interactions between groups and experimental conditions. Therefore, the reported foci of the main effect of planning versus baseline were included; however, solely the sample size and description of the healthy controls were used.

44 Methods 2.2

problem without actually executing these moves (n = 24)4. However, assuming that planning ahead was required for both types of response modes, these were not further differentiated in the subsequent analyses.

2.2.2 Meta-Analyses on Overall Planning and Planning Complex- ity

In the majority of functional brain imaging studies using the ToL task, planning- related neural activation is identified by either contrasting planning with a baseline condition or by experimentally manipulating planning complexity with factorial or parametric designs (see overview Kaller, Rahm, Spreer, et al., 2011). In consequence, in the present ALE study separate meta-analyses were conducted for Overall Planning (compared with a baseline) and for Planning Complexity. The meta-analysis on Overall Planning comprised independent 29 datasets with the following control conditions (see Table 2.1): counting balls on screens (n = 12); zero-move problems with identical start and goal states (n = 5; including one study in which additionally appearing yellow rings had to be touched); ball detection and selection of a blinking ball while constantly tapping the screen (n = 1); one-move problems (n = 2); problem displayed but subjects were requested not to solve it (n = 1); examiner showed the solution and participants had to simply copy moves (n52; including one study where in an additional control condition empty SoC pockets were presented); counting vowels of words written on the ToL balls (n = 1); scrambled, unrecognizable picture of the ToL (n = 1); an empty screen or a fixation cross (n = 4). For the meta-analysis on Planning Complexity, independent 14 datasets were identified (see Table 2.1). In 10 of these, problem difficulty was modeled as a parametric modulation in terms of the minimum number of moves to solution. In the

4Studies with versus without solution execution (cf., Table 2.1) sum up to 32, as one study applied both response modes in two different runs (Rowe et al., 2001) (reported coordinates refer to a collapsed analysis across runs).

45 A META-ANALYSIS ON THE NEURAL BASIS OF PLANNING . Moreover, the Planning Complexity or Overall Planning Sample Task Number of Reported Activation Foci Data Study Imaging N Sex Mean Age ToL Version Minimum Num- Solution Design Overall Planning Set # Technique (m/f) (years) ber of Moves* Execution Planning Complexity 1.1 1.2 2 Baker et al. , 1996 3 Elliott et al. , 1997 4 Beauchamp5 et al. , 2003 Boghi,6 Rasetti, et al. , 2006 Campbell7 et al. , PET2009 M.8 PET CohenfMRI etPET al. , 2014 Dagher9 et al. , 1999 de10 Ruiter et al. , 2009 fMRIde11 Ruiter et al. , 2011 den12 fMRI Braber 6 etDesco 18 al. , 12 et 2008 al. , 13 2011 6Fallon etPET al. , 14 2013 fMRIGoethals et al. , 15 2004 fMRI 1HuyserfMRI 9/9 et 5/1 6/6 al. , 16 2010 17Just 5/1 et al. , 17 2007 Kempton etfMRI al. , 18 2011 19Lazeron 6fMRI etSPECT 1/0 35.9 al. , 56.819.1 15/22000 31 15Lazeron et 12 al. , 19.2 2004 31fMRILiemburg etOwen al. , et20 2015 al. , 19/01996 fMRIOwen 2/4 14 et21 20.9 al. , 21 10 0/15fMRI1998 WATT SoC 52 5/7fMRI22.1 SoCRasmussen et al. , fMRI22.22006 SoC 34.1fMRIRowe 25 etSchall al. , et23 9/5 58.62001 al. , 58.2 6/42003 23/29 WATT 10 1-324 PET vs. 32.8 Original 4-6 ToL moves 18fMRI 3-525 PET moves 9/16 9Schöpf 1-6 n.r. et moves al. , Original26 ToL2011 18 64.3 13.4Spreng 2-3 20 vs. et SoC 4-5 5/5 al. , Original 2427 moves ToL2010 no 1-7 1-5Stokes moves 15/3 moves et OriginalPET al. , ToLPET28.1 2011 1-5 13.7van moves 12 den 5/4 Heuvel et28.2 12/4 1-5 al. , 8 moves2003 van 14/6 denvan no Heuvel 6 SoC SoC ’t et29 16.8 al. , Ent yes2013 fMRI et 24.5fMRI al. , n.r.2014 Original 1-5 no ToL movesfMRIfMRI 6/6 Original 36.6 ToL 2-6 22 10Wagner 31.1fMRI moves et 8/0 no 6 no al. , 2006 4/2 yes 1-5 moves B SoC no SoCfMRI 2-4 B 2-5 moves moves 28 no 22 fMRI 41.4 B 10/0 20 Original 11 ToL 5/1 Original 25 ToL Original 57.7 yes ToL E 47 14 E n.r.fMRI 2-4, 12/16 6-8 11/11 1-2 moves vs. yes 2-7 E 3-5 moves moves 2-4 moves 46 3/17 1-3 SoC E 27 moves no 4/7 6 33.8 19/28 no no 27.3 SoC Original 29.9 B ToL 11 no 13/33 no 17 20 21.3 B 3-5 moves fMRI 35.7 25.5 SoC 18 5/1 3-5 E WATT moves no 8 9 no Original E B 36.9 ToL Original no ToL 17 9/8 3 vs. B 4-5 13 n.r. Original moves B ToL 1-5 moves SoC Original ToL 45 31 3-7 1-7 29 moves moves 0-9 yes moves B 1-5 moves Original 8 E 27.5 ToL B 1 yes yes 13/32 1-5 moves 14 21 3-5 9 7 WATT 13 moves no B Original 11 6 ToL 36.9 yes, 8 no no no B B 2-5 no no moves 22 10 1-7 moves 8 13 no E B Original ToL 10 no E B 1-5 E moves B 25 no 3 E 26 no B 18 17 no E 20 20 B 9 9 8 1 E 8 26 10 25 6 8 3 3 Table 2.1. Overview of the functional neuroimaging datasets included in the ALE meta-analyses. Note. Listed are reference numbers and respective studies with author and year of all 31 studies that were included in the ALE analysis on respective imaging technique, the sample size (overall and separated forplanning gender), complexity analysis mean was age parametrical of whereas the ifn.r., not there sample reported; are and B, two the block numbers or design; amount ranges E, of listed event-related reported separated design. by activation a foci "vs." are (e.g. listed. 1-3 vs. SoC, 4-6 Stocking moves) of the reported Cambridge. planning WATT, complexity analyses was categorical. Ward and Allport Tower Task. * The minimum number of moves states the all problem difﬁculties that were utilized in the studies. Moreover, if there is solely a range listed (e.g. 1-5 moves) the reported

46 Methods 2.2

remaining four datasets, ToL problems were divided into easy versus difﬁcult problems so as to run comparisons for these two types of problems.

2.2.3 ALE Approach

ALE is a method to perform quantitative meta-analysis on functional brain imaging results based on all local activation maxima (resp. foci) (Eickhoff et al., 2009). The algorithm treats the local maxima not as individual points in space but as spatial probability distributions, so that an activation probability can be computed for every voxel. To create a null distribution for statistical evaluations, random foci maps are generated based on the same amount of foci as inserted into the meta-analysis. Subsequently, the probability map resulting from the meta-analysis is compared with the random map so as to calculate q values of the false discovery rate (FDR) (Eickhoff et al., 2009). Present analyses were performed with GingerALE 2.3.2 (www.brainmap.org) using the approach of P. Turkeltaub et al.(2012) to minimize within-experiment and within-group effects on the overall estimation. Coordinate-based ALE meta-analyses were conducted in Montreal Neurological Institute (MNI) stereotactic standard space. Coordinates of brain activation foci reported in Talairach standard space (Talairach & Tournoux, 1988) were converted into MNI space using the icbm2tal_spm algorithm (Lancaster et al., 2007) implemented in GingerALE 2.3.2. All activation foci reported in the included studies were double-checked for plausibility and congruence with the provided anatomical labels. Observed divergences between signs of x-coordinates and reported hemisphere were corrected by reversing the sign before entering the coordinates in the ALE meta-analyses. This correction concerned 13 foci from three studies5. GingerALE automatically determines full-width-at-half-maximum (FWHM) values, so that no additional FWHM ﬁlter was applied here, which conforms to the suggested default analysis procedure (www.brainmap.org). For masking outliers, the

5The x-coordinates of 10 activation foci from Lazeron et al.(2000), one activation focus from M. Cohen et al.(2014), and two foci from Schöpf et al.(2011) were accordingly corrected.

47 A META-ANALYSIS ON THE NEURAL BASIS OF PLANNING

less conservative approach implemented in GingerALE 2.3.2 was selected. ALE analyses were computed with a FDR of P < 0.01 as cluster-forming threshold and P < 0.05 for cluster-level inference as recommended in the manual.

2.3 Results

2.3.1 The Functional Anatomy of Planning Processes

2.3.1.1 Meta-analysis of neural activation patterns for overall planning

Of the 31 published neuroimaging papers on the ToL suitable for ALE meta-analysis, 29 independent datasets from 28 studies were included in the analysis on Overall Planning, comprising 537 normal subjects (48.8% male) with a mean age (6 standard deviation, SD) of 32.4 13.1 years. Adjusted for sample size, the weighted mean age ± was 35.0 years. The analysis was based on a total of 391 activation foci (Fig. 2.2A), while another 20 foci (5.1%) were disregarded due to a location outside of the applied ALE brain mask (see above). As illustrated in Figure 2.3A, results of the ALE meta-analysis on Overall Planning activation with a cluster level threshold of P = 0.05 and a FDR-threshold of q < 0.01 revealed bilateral contributions of mid-dlPFC, which were however more pronounced for its right ([41 36 30], 3,776 mm3) than for its left homolog ([241 32 31], 2,736 mm3). Results further yielded bilateral involvement of the frontal eye ﬁelds (FEFs), caudate, anterior insula, inferior parietal lobule (IPL), supplementary motor area (SMA), precuneus, and rostrolateral prefrontal cortex (rlPFC). Lateralized activation was found for right inferior occipital gyrus, right inferior temporal gyrus, and left posterior cingulate. A detailed overview on the contributions of the individual studies underlying these results is provided in Table 2.2.

2.3.1.2 Meta-analysis of neural activation patterns for planning complexity

The ALE meta-analysis on Planning Complexity was conducted on 14 datasets of 13 studies that comprised 182 normal subjects (63.2% male) with a mean age ( SD) of ± 48 Results 2.3

A. Overall Planning

Sample Size Imaging Technique 1 fMRI PET 52 B. Planning Complexity SPECT

Figure 2.2. Spatial distributions of activation foci from ToL neuroimaging studies included in the present ALE meta-analyses on (A) Overall Planning and (B) Planning Complexity (see Methods section for further details). Individual foci are marked by spheres. The sizes and the colors of the spheres refer to the underlying sample sizes and the used brain imaging techniques. The renderings for the lateral views only comprise the activation foci for the respective hemisphere, whereas the topview renderings represent all foci.

32.9 12.2 years and a weighted mean age of 29.8 years. The analysis was based ± on a total of 154 activation foci (Fig. 2.2B), while another seven foci (4.5%) were disregarded given a location outside the applied ALE brain mask (see above). Results for the cluster level threshold of P = 0.05 and the FDR-threshold with q < 0.01 yielded a bilateral lateralization for mid-dlPFC with a more pronounced activation extent in left ([-43 33 31], 2,832 mm3) compared to right mid-dlPFC ([42 39 27], 1,808 mm3) (Fig. 2.3B). In addition, bilateral involvement was found for the FEFs, precuneus, SMA, IPL, and caudate as well as a left lateralization for the rlPFC and anterior insula. Detailed results as well as the individual contributing studies are listed in Table 2.2.

49 A META-ANALYSIS ON THE NEURAL BASIS OF PLANNING

A. Overall Planning

-40 -35 -30 -25 -20 -15 -10

-5 0 5 10 15 20 25

30 35 40 45 50 55 60

B. Planning Complexity

-40 -35 -30 -25 -20 -15 -10

-5 0 5 10 15 20 25

30 35 40 45 50 55 60

Figure 2.3. Spatial distribution of resulting activation patterns for the ALE meta-analyses on (A) Overall Planning and (B) Planning Complexity. White contours reﬂect the applied FDR threshold of P < 0.01. Numbers besides the axial slices refer to z-coordinates in MNI-space.

50 Results 2.3

A. Brodmann Area 46 B. Overall Planning C. Planning Complexity

SFG SFG

MFG MFG IFG IFG MFG IFG MFG IFG

Rajkowska & Goldman-Rakic (1995b)

Figure 2.4. (A) BA 46 distribution maps in ﬁve human post-mortem brain dissections (left) and their resulting overlay in the Talairach and Tournoux(1988) space. Reproduced and adapted from Rajkowska and Goldman-Rakic, Cereb Cortex, 5, 323–337, reproduced by permission (B + C) Overlay of BA 46 as described by Rajkowska and Goldman-Rakic(1995a, 1995b) (gray) and the activation cluster in dlPFC (red) resulting from the ALE meta-analyses for (B) Overall Planning and (C) Planning Complexity (rendered at a FDR-correction of P < 0.01). IFG, inferior frontal gyrus; MFG, middle frontal gyrus; SFG, superior frontal gyrus.

2.3.1 The Anatomical Localization of Planning Processes in the dlPFC

The localization of planning processes within left and right dlPFC and, in particular, the empirical validation of their putative assignment to the mid-dlPFC (Unterrainer & Owen, 2006) as a circumscribed cytoarchitectural structure in terms of BA 46 (and 9/46) (cf. Rajkowska & Goldman-Rakic, 1995a, 1995b) were main objectives of the present meta-analysis. However, as of yet, probabilistic maps for BA 46 are not available at the voxel level of a stereotactic standard space. We therefore projected the spatial distribution of BA 46 estimated from the ﬁve post-mortem brains investigated by Rajkowska and Goldman-Rakic(1995a) onto the renderings of the mid-dlPFC activation for Planning Overall and Planning Complexity (Fig. 2.4). The resulting overlap indicates that planning is indeed speciﬁcally associated with activation of a spatially circumscribed part in the center of the middle frontal gyrus presumably corresponding to BA 46. Furthermore, the overlay with the cytoarchitectural projections of BA 46 suggests that the activation in mid-dlPFC for overall planning and particularly for planning complexity slightly extends in the caudal direction presumably corresponding to BA 9/46 (cf. Petrides, 2005; Petrides & Pandya, 1999).

51 A META-ANALYSIS ON THE NEURAL BASIS OF PLANNING

2.3.2 The Hemispheric Lateralization of Planning Processes in the PFC and beyond

2.3.2.1 ALE contrast analysis

The above reported lateralization of mid-dlPFC activation during planning was so far assessed only at a descriptive level and revealed larger activation extents for left and right mid-dlPFC for analyses of Overall Planning and Planning Complexity, respectively (see The Functional Anatomy of Planning Processes section and The Anatomical Localization of Planning Processes in the dlPFC section). To statistically contrast left and right mid-dlPFC activations at the voxel level, we further transformed the respective foci into a common space. To this end, we used the mid-dlPFC activations centers (or weighted centroids) derived from the Overall Planning meta-analysis. To determine the activation extents, inspection of the distance histograms showed that activation foci from individual studies clustered within a range of 20 mm from the weighted centroids. In consequence, all activation foci within this distance were included in the ALE contrast analysis and transformed into a common space by subtracting the weighted centroids. In total, 13 coordinates from the left and 15 from the right hemisphere were included. Performing an ALE contrast meta-analysis revealed no lateralization effects for left versus right mid-dlPFC on the voxel level. A control analysis in which the coordinates from the right hemisphere were mirrored (in x-direction) with respect to the right centroid (so as to account for the brain surface’s convexity) did also not yield any lateralization effects. Note however that both analyses were highly unlikely to have sufﬁcient statistical power to detect (subtle) differences given the relatively small number of analyzable studies and activation foci.

2.3.2.2 Exploration of Systematic Differences between Studies

To assess whether systematic methodological differences may account for lateralization differences between studies, all studies included in the ALE meta-analyses for Overall

52 Results 2.3

Planning6 were divided into four groups according to the presence of planning-related activations in left mid-dlPFC (yes/no) and right mid-dlPFC (yes/no) thus resulting in a 2 × 2 contingency table (cf., Table 2.2). This yielded 16 studies without any activation peaks in the mid-dlPFC, another three studies with exclusively left and three studies with exclusively right mid-dlPFC activation peaks, and seven studies with bilateral mid-dlPFC activation peaks. Analyses of potential between-study differences concerned the subjects’ age and sex ratio and the average and maximum of the applied problem’s minimum moves which were entered as dependent variables in separate ANOVAs with subdivisions of studies according to left and right dlPFC activation patterns as between-study factor. However, no significant between-study differences could be observed for any of these variables (for detailed results, see chapter 2.5, Table S2.1). But given that two of the four groups only comprised three studies the negative findings may be due to insufficient power. Further explorative analyses concerned study characteristics such as study design (block vs. event-related), imaging technique (fMRI vs. PET), modeling approach (categorical vs. parametric), solution execution (moves executed vs. moves not executed), and ToL version (original ToL version vs. SOC) which were nominally coded and hence entered together with the 2 × 2 study subdivision (see above) into analyses of multi-dimensional contingency tables. Notably, these analyses suffered heavily from zero-frequency cells and most cells with a frequency lower than five which may result in severe distortions. However, the analyses appear to indicate some important differences: In studies applying the original ToL version, the occurrence of right dlPFC activation was balanced (irrespective of left dlPFC activation), whereas none of the studies applying the SOC found activation in right dlPFC (see chapter 2.5, Table S2.2). Moreover, the study design (block vs. event-related) also appeared to affect the probability of right-lateralized activation (see chapter 2.5, Table S2.4). In studies with event-related designs activation of right dlPFC was slightly more frequent, whereas in studies with block designs the number of studies without reporting activation of right dlPFC was four times higher than that of studies reporting activation in right dlPFC.

6Note that between-study analyses on Planning Complexity were not carried out due to the limited number of data sets.

53 A META-ANALYSIS ON THE NEURAL BASIS OF PLANNING

Table 2.2. Overview of signiﬁcant ALE clusters for Overall Planning and Planning Complexity. ALE Area Lat. Volume Weighted Centroid Number Dataset (Reference #) (mm3) x y z of Foci

Overall Planning

dlPFC left 2736 -40.6 31.8 30.6 10 1.1, 2, 7, 9, 13, 14, 19.1, 20, 24, 27 right 3776 41.4 35.5 29.9 13 1.1, 7, 8, 9, 13, 17, 20, 23, 24, 26, 27 FEF left 7552 -26.2 8.7 57.1 26 1.1, 2, 3, 6, 8, 9, 10, 13, 14, 15, 16, 17, 20, 21, 22.1, 24, 25, 26, 27, 28.1, 29 right 7160 29.3 11.3 55.2 21 1.1, 3, 6, 8, 9, 10, 11, 13, 14, 15, 19.1, 20, 21, 23, 24, 25, 26, 28.1, 29 (pre-)SMA bilateral 3984 -1.9 25.8 43.9 13 1.1, 3, 7, 8, 10, 13, 14, 19.1, 20, 24, 25, 26, 29 left 2200 -38.2 -45.6 44.9 7 2, 7, 10, 15, 19.1, 20 right 4376 46.3 -39.8 47.2 15 3, 4, 6, 7, 8, 13, 15, 18, 19.1, 20, 24, 26, 27, 29 precuneus bilateral 13664 0.0 -61.5 57.3 43 1.1, 2, 6, 7, 8, 9, 10, 11, 13, 14, 15, 17, 18, 1.9, 20, 21, 23, 25, 26, 27, 28.1, 28.1, 29 right 1512 32.9 -72.1 42.5 8 1.1, 6, 9, 19.1, 21, 26 caudate left 1776 -12.2 14.3 1.0 7 4, 7, 9, 13, 26, 27, 28.1 right 1232 13.9 10.1 2.6 5 2, 7, 9, 26, 27 ant. insula left 2000 -31.0 23.8 -0.9 7 6, 13, 14, 20, 24, 25, 27 right 2784 33.1 24.3 -4.2 10 1.1, 13, 14, 20, 21, 24, 25, 26, 27, 29 rlPFC left 2600 -35.3 55.8 5.6 9 8, 9, 13, 14, 20, 25, 28.1, 28.1, 29 right 648 33.7 57.6 3.8 4 9, 11, 23, 25 post. cingulate left 792 -17.1 -58.2 21.5 3 8, 11, 28.1 inf. occip. gyrus right 656 43.3 -79.2 -4.1 3 19.1, 24 inf. temp. gyrus right 624 52.1 -62.1 -5.7 2 8,23

Planning Complexity

dlPFC left 2832 -43.2 32.8 30.9 8 6, 7, 9, 1.2, 20, 26, 27 right 1808 42.2 38.7 27.3 6 5, 6, 7, 9, 26 512 46.0 26.1 42.8 2 18, 27 FEF left 1728 -24.8 -0.9 65.2 5 9, 13, 26, 27 1176 -25.0 22.5 53.3 5 3, 6, 18, 26 right 1576 29.6 17.5 55.9 5 3, 5, 6, 26, 27 400 22.8 -0.5 66.0 2 9, 13 (pre-)SMA bilateral 2504 -3.5 24.5 45.6 7 3, 5, 7, 13, 22.1, 26, 27 IPL left 808 -53.8 -40.7 48.8 3 7, 26, 27 728 -40.5 -51.9 51.0 3 5, 9, 27 right 1792 51.2 -42.8 46.7 7 3, 7, 9, 20, 26, 27 precuneus left 1832 -11.1 -57.7 60.9 4 5, 7, 13, 26 right 1848 8.3 -59.3 58.4 5 7, 13, 26, 27 504 42.4 -74.8 39.3 2 13, 26 caudate left 2432 -16.4 7.5 11.1 8 6, 7, 9, 13, 27 right 1184 18.3 6.8 15.6 3 7, 13, 26 520 14.1 5.2 -2.1 2 26, 27 ant. insula left 504 -32.9 23.1 -4.5 2 26, 27 rlPFC left 1176 -40.0 55.5 10.4 5 1.2, 9, 26, 27 Note. Results are reported at a threshold of FDR p < .01 and a cluster extent of 50 mm3. The number of foci contains the number of individual activation foci that contributed to the respective cluster. Dataset (Reference #) refers to the respective column in Table 1. Differences between the number of foci and the number of listed datasets are due to multiple foci of single studies within the same brain area. Lat, lateralization; Inf, inferior; post., posterior; ant, anterior; occip, occipital; temp., temporal; dlPFC, dorsolateral prefrontal cortex; FEF, frontal eye ﬁelds; SMA, supplementary motor area; IPL, inferior parietal lobule; rlPFC, rostrolateral prefrontal cortex.

54 Discussion 2.4

2 However, these effects of ToL version and study design were not independent (χ 1 = 5.882, P = 0.015, see also chapter 2.5, Table S2.8), as studies applying the SOC more often used block design and studies applying the original ToL more often used event-related designs. Neither the comparison of imaging techniques nor modeling approach nor solution execution yielded any signiﬁcant differences (see chapter 2.5, Tables S2.2).

2.3.2.3 Whole-brain lateralization analyses

To examine lateralization effects beyond the (mid-)dlPFC, two additional whole-brain ALE contrast analyses were performed separately across all foci from Overall Planning and from Planning Complexity. For Overall Planning, two areas were found to have stronger activation on the right hemisphere than on the left hemisphere, namely the IPL and premotor cortex (see chapter 2.5 S2.2 for detailed results). For Planning Complexity, a stronger activation was observed in the left compared to the right SMA.

2.4 Discussion

The present ALE meta-analyses on 29 neuroimaging datasets using the ToL task yielded two major ﬁndings on the speciﬁc involvement of the dlPFC in human planning. First of all, the results corroborated that planning-related activation in dlPFC is localized in circumscribed neural assemblies in the mid-dorsolateral part of the PFC (cf. Unterrainer & Owen, 2006), presumably corresponding to BA 46 (Petrides, 2005; Petrides & Pandya, 1999; Rajkowska & Goldman-Rakic, 1995a, 1995b) and possibly to caudally adjacent parts of BA 9/46 (Petrides, 2005; Petrides & Pandya, 1999). Second, results clearly emphasized the importance of bilateral mid-dlPFC for planning in tasks like the ToL.

55 A META-ANALYSIS ON THE NEURAL BASIS OF PLANNING

2.4.1 The Localization of Planning Processes within the Mid-dlPFC

The present study revealed the involvement of the bilateral dlPFC in well-structured planning. Moreover, instead of the extended area of the dlPFC that is referred to in most ToL studies, mainly comprising BA 9, 9/46, and 46 (cf. Petrides, 2005, 2013; Petrides & Pandya, 1999), but sometimes also parts of BA 6, 8, and 10, the middle part of the dlPFC (mid-dlPFC) emerged as the essential subregion for planning in the present meta-analysis. Importantly, the present meta-analyses particularly emphasize the functional distinction between BA 9 and BA 46 (see Fig. 2.4) in that overlapping the present results with the human cytoarchitectonic maps from Rajkowska and Goldman-Rakic(1995a) suggests planning-related brain activity in mid-dlPFC to be conﬁned to BA 46 (Fig. 2.4) and, if following the nomenclature by Petrides(2005, 2013), extending slightly into caudally adjacent parts of BA 9/46. Cytoarchitectonically, the mid-dlPFC is a brain area with a relatively large layer thickness and, on average, with a high neuronal packing density (Rajkowska & Goldman-Rakic, 1995b). The layers of BA 46 and BA 9/46 are characterized by a well-developed internal granular layer (IV) (Fuster, 2015; Petrides, 2005; Petrides et al., 2012) and, for BA 46, an external pyramidal layer (III) with only small to medium pyramidal neurons (Petrides, 2005; Petrides & Pandaya, 1994; Petrides & Pandya, 1999; Petrides et al., 2012). By contrast, BA 9 has a poorly developed layer IV with lower neuronal density than BA 46 and 9/46 and comprises large pyramidal neurons in layer III (Petrides, 2005; Petrides et al., 2012; Petrides & Pandya, 1999). In terms of connectivity based on tracer studies in monkeys (e.g. Petrides & Pandya, 1999; Schmahmann & Pandya, 2006; for a review, see Yeterian et al., 2012), the three areas share a common basic pattern in that they are connected with each other and with other prefrontal and frontal areas (mainly BA 6, 8, 10, 45, and 47), multimodal temporal areas, the anterior and posterior cingulate, and receive afferents from the retrosplenial cortex. Critically although, BA 46 and 9/46, but not BA 9, are reciprocally connected to medial as well as inferior and superior lateral parietal areas and send efferents to the retrosplenial cortex (Petrides, 2005; Petrides et al., 2012; Petrides & Pandya, 1999, 2006). Thus, BA46 and 9/46 are distinguishable from BA 9 by their

56 Discussion 2.4

strong connection to heteromodal parietal areas that were also found here to form part of the planning network (cf. Fig. 2.3, Table 2.2), such as the IPL and precuneus (see below), as well as a reciprocal connection to the retrosplenial cortex. Given that Brodmann’s (1908) and Walker’s (1940) descriptions of area 46 in humans and macaque monkeys, respectively, suggest a very similar cytoarchitecture (Petrides, 2005), it is likely that not only the functional properties of BA 46 and BA 9/46 but also their structural connections share a substantial amount of commonalities in both species and give hence rise to comparable cognitive functions (cf. Petrides, 2005; Petrides & Pandya, 1999). Thus, considering this connectivity pattern, it has been argued that BA 6 and 8 subserve the simple maintenance of visuo-spatial working memory contents, whereas the higher-order monitoring and manipulation of these representations is subserved by BA 46 and 9/46, that is, the mid-dorsolateral PFC (Petrides & Pandaya, 1994; Petrides & Pandya, 1999; Petrides, 2005). In sum, when considering the cytoarchitecture and anatomical connections of the dlPFC, current results can be regarded as reﬂecting bilateral activity of the mid-dlPFC, speciﬁcally located in BA 46, during human planning, which in turn represents a prototypical example of goal-directed higher-order cognitive processes in the visuo-spatial domain.

2.4.2 The Role of the Left and Right Mid-dlPFC in Planning

The present meta-analysis demonstrated the involvement of the bilateral mid-dlPFC in planning, which runs contrary to the tenet of a strictly lateralized involvement of either left or right mid-dlPFC. Furthermore, a relative lateralization suggesting a stronger activation of left over right mid-dlPFC or vice versa could not be found (see The Hemispheric Lateralization of Planning Processes in the PFC and beyond section) but has to be interpreted with caution given the small number of studies underlying this comparison. Furthermore, as not all studies included in the meta-analysis have uniformly reported bilateral dlPFC activation, the question remains whether methodological differences between the studies may have driven the differences concerning unilateral

57 A META-ANALYSIS ON THE NEURAL BASIS OF PLANNING

and bilateral dlPFC involvement during planning. Explorative analyses indicated two (partially confounded) between-study differences entailing stronger contributions of left compared to right dlPFC, namely the tower version (SoC vs. original ToL) and the task design (block vs. event-related presentation and modeling of hemodynamic responses). It could hence be that block designs are generally less effective to detect right dlPFC activation during planning. This interpretation is supported by recent comparisons between stimulus-locked and response-locked modeling approaches for fMRI data on the ToL showing that activation in left mid-dlPFC was elicited particularly early during the planning phase and for a longer period of time, whereas activation in right mid-dlPFC occurred later in the planning phase and for a shorter period of time (Ruh et al., 2012) (see also below). Activation of right mid-dlPFC during planning seems hence more likely to be attenuated by coarse block-type modeling of the hemodynamic response across several problem items. In this regard, one might further question whether the common modeling approach – be it event- or block-related – of convolving a canonical hemodynamic response function across the duration of one or several problem items is appropriate at all for fitting hemodynamic changes during complex cognitive task such as planning. That is, this approach goes along with the implicit assumption of a continuous neural activation during the totally modeled complete course of cognitive processing, which may not equally hold for all brain areas involved and all cognitive processes modeled. That is, modeling approaches using sets of finite impulse response basis functions, which allow for a closer and area-specific fit to the actual course of the hemodynamic response during longer intervals of cognitive processing, may constitute a promising modeling alternative in future studies (e.g. Kaller, Rahm, Köstering, & Unterrainer, 2011; Kaller, Rahm, Spreer, et al., 2011; Ruh et al., 2012). As an alternative interpretation, the effects of tower version (which were however associated with the aforementioned effects of task design), may further indicate structural differences between the ToL versions and/or the applied problem sets (e.g. Kaller, Rahm, Spreer, et al., 2011; Kaller, Rahm, Köstering, & Unterrainer, 2011; Newman, Greco, & Lee, 2009) (see also below for effects of problem structure on neural activation patterns). In this regard, the SoC versions compared to the original

58 Discussion 2.4

ToL versions may have used problems that tap differently into speciﬁc planning processes that are performed by the left and right dlPFC. However, these potentional explanations warrant caution, as the two effects of between-study differences on dlPFC lateralization during planning were not independent and cannot be unambiguously interpreted. Taken together, present results clearly demonstrate that the left and right mid- dlPFC are jointly activated both during planning in general and as a function of the complexity of the ToL problems to be solved in particular. However, the ﬁnding of a bilateral activation does not preclude that the left and right mid-dlPFC subserve differential roles during the overall planning process. This raises the question as to what these supposed differential roles might be. Newman et al. (Newman et al., 2003, 2009; Newman & Pittman, 2007) systematically manipulated structural problem parameters of the ToL. They conclusionded that the left mid-dlPFC is responsible for control processes as well as the representation and maintenance of general task demands, whereas the right mid-dlPFC is associated with integration, manipulation, and maintenance of information in working memory that serve to formulate a strategy for solving a given problem (Newman et al., 2009; cf. Newman & Pittman, 2007). Kaller, Rahm, Spreer, et al.(2011) examined the functional lateralization of the mid-dlPFC using a comparable approach of manipulating structural problem parameters of the ToL. Foundation of the experiment was the concept that the overall planning process dissociates into two separable phases, an early internalization phase (transfer of the start and goal into working memory) and the subsequent core planning phase, where the start is mentally manipulated until its representation matches the goal (cf. Newell & Simon, 1972). It was found that the internalization phase elicited stronger activation of the left mid-dlPFC, whereas the core planning phase elicited stronger activation in the right mid-dlPFC (Kaller, Rahm, Spreer, et al., 2011). Moreover, the data revealed that the left mid-dlPFC activated earlier during the time course of each trial and that internalization processes occured only initially (Ruh et al., 2012). In contrast, the right mid-dlPFC activated especially during the end of

59 A META-ANALYSIS ON THE NEURAL BASIS OF PLANNING

the planning phase and the core planning process was found solely here (Ruh et al., 2012). In a similar focus on the time course of cognitive subprocesses during planning, Byrd, Case, and Berg(2011) examined the temporal course of the planning phase which was separated into three parts. In the first part bottom-up stimuli processing was observed (Byrd et al., 2011). In the second part, the stimuli were being processed by the (right) dlPFC. During the last part of the planning phase, again a left dlPFC activation was reported. In interpreting the present findings, it has to be noted that the mid-dlPFC is found to be involved in a great variety of tasks as a part of the multiple demand (MD) network (Duncan, 2010; Duncan & Owen, 2000). That is, the very specificity of planning processes that is ascribed to the left and right dlPFC fades in view of their general involvement in complex cognitive tasks. Yet, this study aimed at delineating the neural basis of planning by conducting a meta-analysis on functional imaging studies using the ToL. Thus, a characterization of the role of the mid-dlPFC in planning was the focus of this work, not a characterization of the range of cognitive processes requiring (mid-)dlPFC involvement. Notwithstanding this, a complete understanding of dlPFC functioning would greatly benefit from future meta-analyses complementing the present one that explicitly investigate the range of cognitive processes drawing on the (mid-)dlPFC. In this regard, a characterization of the precise cognitive and neural processes underlying human planning could then be conceptualized as specific examples of more general, domain-independent cognitive processes subserved by the dlPFC in giving rise to higher-order human cognition. It has also to be noted that well-structured tasks like the ToL exert different demands on planning ahead than problems in real-life situations (e.g. Burgess, 2000; Goel, 2010). Hence, the here established significance of bilateral mid-dlPFC for planning in the ToL does not necessarily extrapolate to planning ahead solutions to real-world problems. Finally, the preceding sections emphasized the crucial role of the mid-dlPFC for planning. However, not all included studies found activation that contributed to the mid-dlPFC clusters, which seems to argue against this critical role of the

60 Discussion 2.4

mid-dlPFC. Possible reasons for this discrepancy are discussed in the following. In the present meta-analysis, the clusters with the largest effect sizes and the highest number of contributing activation foci (cf., Table 2.2) were found in the FEFs (and the precuneus). For ALE analyses, the peak voxel of a given significant cluster is taken as representation of the underlying activation pattern. However, if activations of multiple functionally distinct regions form one large cluster, then the peak of activation does not necessarily constitute an adequate representation of all areas within that large cluster. Given that the FEFs and the dlPFC are located relatively close to each other, the dlPFC-although significantly activated at the voxel level-might not have contributed an activation focus to the meta-analysis, as the activation peak of its cluster lay in the FEFs. Indeed, connected activation clusters of the FEFs and dlPFC can be found in some of the studies that did not contribute a dlPFC activation focus (e.g. Campbell et al., 2009; Dagher et al., 1999; van ’t Ent et al., 2014). Another reason might be the issue of varying significance thresholds, which are chosen rather arbitrarily and differ between studies (Lieberman & Cunningham, 2009). To be specific, in several of the studies not contributing to the dlPFC activation of the ALE analyses, dlPFC clusters are nonetheless visible (in illustrations at liberal thresholds) that, however, do not pass the given significance threshold of that particular study (e.g. Boghi, Rasetti, et al., 2006; Desco et al., 2011; Lazeron et al., 2000; Liemburg et al., 2015; Rowe et al., 2001; Schall et al., 2003; Wagner et al., 2006). To appropriately address this widely recognized issue of threshold effects, it was recently suggested that in imaging studies all activation independent of significance should be depicted (Allen, Erhardt, & Calhoun, 2012). In sum, owing to these methodological considerations, the fact that not all studies included in the meta-analyses contributed foci of dlPFC activation (i) does not necessarily imply that the dlPFC was not involved during planning in these studies at all and (ii) does not generally argue against the pivotal role of the dlPFC for planning ability.

61 A META-ANALYSIS ON THE NEURAL BASIS OF PLANNING

2.4.3 The Functional Anatomy of Planning Processes beyond (Mid)- dlPFC

Concurring with the known anatomy and connectivity of BA46 and BA 9/46, the present ALE meta-analyses revealed a planning-related network of brain regions comprising prefrontal, premotor, parietal, and insular regions as well as the caudate nucleus. In terms of functional networks, these areas substantially overlap with recent conceptions of fronto-parietal networks that underlie the executive control of complex behavior. For instance, the dlPFC and rPFC, IPL, the anterior insula, and the caudate nucleus can be assigned to a circumscribed fronto-parietal control network which is engaged in a variety of tasks that demand controlled information processing (Vincent, Kahn, Snyder, Raichle, & Buckner, 2008). In line with the network’s role in planning performance, these tasks can be summarized as taxing higherorder, adaptive top- down control functions (Dosenbach et al., 2007; Dosenbach, Fair, Cohen, Schlaggar, & Petersen, 2008; Fair et al., 2007). Moreover, large parts of these fronto-parietal networks can also be ascribed to the MD network (Duncan, 2010; Duncan & Owen, 2000), namely the inferior frontal sulcus, anterior insula, the pre-SMA, and rostral PFC. The MD network is observed in a great variety of functional imaging studies and is characterized by its involvement in ﬂuid intelligence and other complex, goal-directed activities (see Duncan, 2013, for dedicated review). Remaining areas of the present analysis, namely the FEFs, posterior parietal cortex, intraparietal sulcus, superior parietal lobule and also the pre-SMA emerge as main parts of the dorsal attention network (Vincent et al., 2008) which is assumed to be involved in spatial attention, eye movements, and hand-eye coordination and, hence, concurs well with the attentional as well as visuo-spatial processes induced by performing the ToL. There have also been attempts to ascribe speciﬁc function to the single areas rather than consider them a network. Cognitive processing within rlPFC is assumed to operate on a higher-order level in that the rlPFC is responsible not for devising or executing moves of the ToL, but rather for selecting, evaluating, and monitoring sequences of moves (Baker et al., 1996; Boghi, Rasetti, et al., 2006; Elliott et al., 1997;

62 Discussion 2.4

Rasmussen et al., 2006; Schöpf et al., 2011; cf. van den Heuvel et al., 2003; Wagner et al., 2006). The FEFs are also a located in the frontal lobe and are thought to control eye-movements and attention (Baker et al., 1996; Boghi, Rasetti, et al., 2006; Dagher et al., 1999; den Braber et al., 2008; Desco et al., 2011). The pre-SMA is assumed to also manage attention during planning (Baker et al., 1996; Boghi, Rasetti, et al., 2006). The present analysis further conﬁrmed the contribution of parietal areas. The superior parietal lobule and the precuneus are relatively uniformly associated with visuospatial processing and attentional representation, that is, with the visuo-spatial working memory component of planning (Baker et al., 1996; Beauchamp et al., 2003; Boghi, Rampado, et al., 2006; de Ruiter et al., 2011; den Braber et al., 2008; Desco et al., 2011; Rasmussen et al., 2006; Wagner et al., 2006). The insula and the nucleus caudate are both mostly neglected in considerations of individual area. For the insula, the automatic sequencing of moves (Baker et al., 1996) but also the salience processing and shifting between the default mode and the dorsal attention network (Schöpf et al., 2011) were proposed. The caudate forms part of fronto-striatal processing loops subserving executive functions (Alexander, 1986) and was speciﬁcally ascribed to be involved in the selection of appropriate responses and their monitoring during performance of the ToL (Dagher et al., 1999).

2.4.4 Conclusion

The present quantitative meta-analytic evidence empirically substantiates the common notion of the crucial contribution of the dlPFC to planning in the ToL and further pinpoints it to be specifically focused on its middorsolateral part. Furthermore, instead of a unilateral involvement, results highlight the bilateral contribution of left and right mid-dlPFC to planning on the ToL. In this regard, findings from previous studies converge on suggesting that the differential involvement of the left and right mid-dlPFC may be invoked by specific sub-processes of planning. However, the present methodological approach does not allow addressing this assumed process-dependent relative lateralization. Therefore, future studies have to explicitly investigate and

63 A META-ANALYSIS ON THE NEURAL BASIS OF PLANNING

identify these sub-processes so as to disentangle the differential roles of the left and right mid-dlPFC in human planning and thereby contribute to a comprehensive understanding of the general role of the PFC in complex cognitive functions. Apart from the mid-dlPFC, a range of further frontal (e.g., rostrolateral PFC, FEF, SMA), parietal (e.g., IPL, precuneus), and opercular regions (anterior insula) are signiﬁcantly involved in planning performance. In this respect, extending the focus of the present meta- analyses on mid-dlPFC toward its interaction with these other planning-associated brain areas may constitute a promising avenue for further research on the neural basis of complex cognition.

2.5 Additional Data

S2.1 Analyses of Systematic Differences Between Studies For further analyzing potential effects of systematic between-study differences on mid-dlPFC activation, all datasets were categorized depending on whether they were identified by the ALE analysis for Overall Planning as contributing to the ALE cluster found for left and/or right mid-dlPFC (cf. Table 2, main manuscript). This resulted in a 2×2 contingency table with four different groups of datasets: (i) contribution to left and right mid-dlPFC ALE cluster, (ii) contribution to left but not to right mid-dlPFC cluster, (iii) contribution to right but not to left mid-dlPFC cluster, (iv) no contribution to either mid-dlPFC cluster. Parametric analyses were carried out analyzing whether the different groups of datasets contributing to dlPFC ALE clusters significantly differed in continuous variables of study characteristics (i.e., sample size, age, sex ratio, and task difficulty in terms of the mean and maximum of the minimum number of moves for optimal solution). That is, a series of univariate ANOVAs were computed on each of the study characteristic variables as dependent measure and the 2×2 contingency table entered as two between-studies factors, i.e. factor left (contribution/no contribution) and factor right (contribution/no contribution) (see Table S2.1).

64 Additional Data 2.5

Frequency analyses were carried out analyzing whether the different groups of datasets contributing to dlPFC ALE clusters signiﬁcantly differed in the following categorical variables of study characteristics: study design (event-related vs block design), imaging technique (fMRI vs PET), modelling approach (parametric vs categorical), solution execution (moves executed vs moves not executed), and ToL version (original ToL vs SOC). That is, the 2×2 contingency table was extended to a 2×2×2 table for each of the categorical variables and a log linear analysis was performed (see Table S2.2). Note that the two-way interaction left×right (not shown in Table S2.2) and the three-way interaction left×right×study characteristic variable (e.g. imaging technique) were always signiﬁcant (see Table S2.2) because of unequal frequency distribution of the 2x2 dlPFC contribution groups. That is, most datasets either yielded a bilateral dlPFC involvement (n=7) or no dlPFC involvement (n=16). In contrast, only six datasets contributed unilaterally to dlPFC ALE clusters (n=3 for left dlPFC and n=3 for right dlPFC cluster).

Table S2.1. Results of ANOVAs on the continuous variables to explore differences in study characteristics. Between-studies factors Dependent left right left × right variables F p F p F p Sample Size 1.167 .290 0.014 .905 0.003 .958 Age 0.098 .756 0.514 .480 2.955 .098 Sex Ratio 1.567 .222 1.488 .234 1.080 .309 Mean Moves 0.071 .792 0.387 .540 1.138 .297 Max Moves 0.589 .450 0.082 .777 2.088 .162 Annotation. The lateralization was determined by the contribution of the datasets to the dlPFC areas found for the Overall Planning ALE analysis.

65 A META-ANALYSIS ON THE NEURAL BASIS OF PLANNING

Table S2.2. P-values for the log linear analysis of 2-way and 3-way interaction effects of 2×2×2 contingency tables for categorical measurements of studies characteristics. Factor Factor × left Factor × right Factor × left × cf. Table right Study Design .306 .040* .007** S2.3 Imaging Technique .121 .180 .011* S2.4 Modelling Approach .677 .765 .015* S2.5 Solution Execution .202 .145 .013* S2.6 ToL Version .076 .005** .001** S2.7 Note that 2 studies used the WATT ToL Version, one study using SPECT as imaging technique, and one study not reporting the type of design were excluded from the analyses.

Table S2.3. The number of studies separated by ToL Version and no/left/right/bilateral cluster contribution. original ToL Stockings of Cambridge contribution no right right no right right no left 8 3 6 0 left 0 6 3 1 Note. The WATT ToL version was not considered in this analysis as it was only applied in two studies (both yielding neither left nor right mid-dlPFC activation).

Table S2.4. The number of studies separated by Study Design and no/left/right/bilateral cluster contribution. block design event-related design contribution no right right no right right no left 9 1 5 2 left 3 2 0 5 Note. One study that did not report the type of design was excluded from the analysis.

66 Additional Data 2.5

Table S2.5. The number of studies separated by Imaging Technique and no/left/right/bilateral cluster contribution. fMRI PET contribution no right right no right right no left 13 3 2 0 left 1 6 2 1 Note. SPECT as imaging technique was only used in one study (reporting neither to left nor right dlPFC activation), which was thus not considered in this analysis.

Table S2.6. The number of studies separated by Modelling Approach and no/left/right/bilateral cluster contribution. categorical parametric contribution no right right no right right no left 2 0 13 2 left 0 0 3 7

Table S2.7. The number of studies separated by Solution Execution and no/left/right/bilateral cluster contribution. indication of minimum solution execution move number contribution no right right no right right no left 12 3 3 0 left 1 6 2 1

Table S2.8. The relationship between the utilized ToL Version and the utilized study design. ToL Version Original ToL Stockings of Cambridge block 6 10 design event 10 2 Note. The WATT ToL version was not considered in this analysis as it was only applied in two studies (both yielding neither left nor right mid-dlPFC activation).

67 A META-ANALYSIS ON THE NEURAL BASIS OF PLANNING

Supplementary Figure S2.1. Results of the Overall Planning contrast analysis. Note that for voxel-wise comparisons across hemispheres, all activation reported for the left hemisphere was ﬂipped into the right hemisphere.

S2.2 Whole-Brain ALE Contrast Analysis on Left vs. Right Hemispheric Activa- tion For this whole-brain ALE contrast analysis all left-lateralized activation foci were flipped to the right hemisphere. For the contrast of greater right- than left-lateralized activation, results revealed a significant cluster of 568 mm3 in the inferior parietal lobe (IPL) (Fig. S2.1). This result mirrors the Overall Planning ALE analysis, where a considerably larger cluster for the right than for the left IPL was found (cf. Table 2, main manuscript). Moreover, a small activation (136 mm3) was found for the right middle frontal gyrus (BA 6), again resembling the larger right-lateralized BA 6 cluster in the Overall Planning ALE analysis (cf. Table 2, main manuscript). However, when brought into the same space by the weighted centroid approach (see main manuscript, section 3.3.1 on Left frontally lesioned compared to right frontally lesioned patients), this effect disappeared hence indicating a spatial shift of the activation focus between hemispheres instead of a significant difference in the activation extent. No effects were observed for the contrast of greater left- than right-lateralized activation.

68 Additional Data 2.5

Supplementary Figure S2.2. Illustration of the overlap (pink color) between the results of the two separate Overall Planning ALE analyses restricted either to the 22 datasets without solution execution (red color) or to the six datasets with solution execution (blue color).

S2.3 ALE Analyses on the Type of Solution Execution To assess systematic effects between studies that required either only to indicate the number of moves necessary for solution or to actually execute these moves, two separate ALE analyses were computed on the respective datasets. The results were nearly identical with the results of the main Overall Planning ALE analysis. As can be seen in Figure S2, when overlaying results from two separate analyses on the 6 datasets with actual solution execution and the 22 datasets without solution execution, subtle differences only emerge for the inferior occipital gyrus and inferior temporal gyrus.

Dissociating task-demand- 3 speciﬁc differencesin cognitive processing during planning and problem solving: A validation approach using pupillometry

3.1 Introduction

The direct link between the size of the human pupil and variations in the state of the mind is known at least since the 16th century (Andreassi, 2000). As of yet, relationships with pupil dilation have been established for a wide range of somatic and also cognitive processes (e.g. Beatty, 1982b, 1982a; Chapman et al., 1999; Granholm et al., 1996, 1997; Hess & Polt, 1960, 1964; Hess et al., 1965; Hyönä et al., 1995; Kahnemann & Beatty, 1966; Simms, 1967). Several studies with cognitively demanding tasks reported a strong correlation between the amount of cognitive demand and pupil dilation (Causse, Sénard, Dé- monet, & Pastor, 2010). Beatty and Lucero-Wagoner(2000) have therefore concluded that pupil dilation reflects the current cognitive load. Intra-individual evidence for the relation between pupil size and cognitive demands is further extended by inter- individual findings that healthy subjects with higher cognitive resources show smaller This chapter was submitted as a peer-reviewed journal article as: Nitschke, K., Rahm, B., Köstering, L., Weiller, C., & Kaller, C.P. (submitted). Dissociating task-demand-specific differences in cognitive processing during planning and problem solving: A validation approach using pupillometry. Frontiers in Psychology.

71 TASK-DEMAND-SPECIFIC DISSOCIATING BY PUPILLOMETRY

effects in pupil dilation (Verney, Granholm, & Marshall, 2004). In addition, dissociations between cognitive processes with different loads or task demands have also been demonstrated in psychiatric samples, for instance by showing higher dilation for irrelevant and lower dilation for relevant stimuli in schizophrenic (Granholm & Verney, 2004; Minassian, Granholm, Verney, & Perry, 2004), depressed (Siegle, Steinhauer, & Thase, 2004), and amnestic patients (Laeng et al., 2007) as compared to controls. Taken together, the extant literature on the effects of cognitive demands on pupil dilation is remarkably consistent across studies and psychological domains (Beatty & Lucero-Wagoner, 2000). This seems to render pupillometry a highly valuable source of information for studying cognitive processes. Surprisingly, in studies of eye movements during complex cognition such as planning and problem solving the often co-recorded7 data on pupillometry have been largely neglected. In the present study, we therefore explored the value of pupillometry for investigating the course of cognitive processing during planning. Planning is a prototypical executive function (Burgess, 1997; Norman & Shallice, 1986) and an illustrative example for the fact that analyses of eye movements can reveal valuable insights into the nature and course of the underlying cognitive operations (Hodgson et al., 2000). The planning of future actions requires mental conception and evaluation of several behavioral alternatives and their associated consequences particularly in those situations beyond everyday routine in which goals cannot be achieved directly because behavioral schemata are either not applicable or suitable (Ward & Morris, 2005). In this respect, the successful completion of purposive behavior relies on the ability to identify and select an appropriate sequence of actions before their actual execution. However, the cognitive operations during this planning time between the presentation (or encounter) of a problem and the ﬁrst attempt to execute a solution remain elusive. Fine-grained analyses of eye movements in recent experiments on the Tower of London (ToL) planning task (Fig. 3.1) have provided ﬁrst evidence for dissociable

7As most contemporary eye-tracking systems are based on video oculography, the recordings usually also provide data on pupil size besides information on the spatio-temporal characteristics of gaze shifts that are of primary interest.

72 Introduction 3.1

A Search Depth B First Inspection

with intermediate move without intermediate move First Inspection on Goal State First Inspection on Start State

Goal Start at Start Start at

partial

- Bottom Top

full full Goal at Goal at

Start Goal Start Goal vertical

Top Bottom

Start Goal

full

partial partial Alignment

Start Goal Start Goal Start at Start at

Right Left

Tower Configuration Tower Goal at Goal at

partial

horizontal Left Right Goal Start Start Goal

partial partial Start Goal Start Goal

Figure 3.1. The ToL comprises start and goal states consisting of three pegs and three balls each. In panel (A), six ToL sample problems are depicted, each presenting a start state that should be altered to match the goal state while considering the following rules (Shallice, 1982): (i) only one ball can be moved at a time, (ii) only the topmost ball of each peg can be moved, (iii) balls can only be placed on a rod, (iv) problems shall be solved with three moves (in the current study). The six problems differ in two problem structures: Tower Configuration and Search Depth. Tower Configuration describes the three configurations of the balls for the start and the goal state. All balls are either on the same peg (full Tower Configuration) or two balls are on the same peg and one ball is on another peg (partial Tower Configuration). Search Depth describes whether one or no intermediate move has to be executed to solve the problem. Intermediate moves are moves which place the present ball in a position other than its final goal position. In the depicted problems with an intermediate move the gray ball has to be moved on the right peg (not its final goal position) so as to subsequently move the white ball and finally the gray ball in their final goal positions. However, in problems without an intermediate move all three balls can be immediately moved to their final goal positions. In panel (B) the same ToL problem (cf. Fig. 3.1-A; full–partial, with intermediate move) is depicted for the four presentation groups (SBGT, STGB, SRGL, SLGR). As illustrated, each presentation group had either a horizontal or vertical Alignment of the start and goal state. Given that initial shifts of gaze were predominantly directed towards the state at the left (SRGL, SLGR) or top (SBGT, STGB) (Nitschke et al., 2012; cf. Kaller et al., 2009), the First Inspections were either placed on the start state (STGB, SLGR) or goal state (SBGT, SRGL).

stages of cognitive processing during mental planning (Hodgson et al., 2000; Kaller et al., 2009; Nitschke et al., 2012). In line with previous concepts of an (at least partly) sequential order of processing during problem solving (Newell & Simon, 1972; Ward & Morris, 2005), two temporally distinct cognitive phases have been isolated: the initial phase of internalizing the problem, where an internal cognitive model of the present problem is created, and the subsequent phase of actual core planning, where the internalized model is manipulated to solve the problem (Kaller et al., 2009; Nitschke et al., 2012). This dissociation is based on an experimental manipulation of two structural problem parameters in the ToL task, namely Tower Conﬁguration and

73 TASK-DEMAND-SPECIFIC DISSOCIATING BY PUPILLOMETRY

Search Depth, which impose different demands on processes of internalization and core planning, respectively (Kaller et al., 2009; Nitschke et al., 2012, Fig. 1). Analysis of eye-tracking data revealed that higher demands on Tower Configuration and forming an internal problem representation are associated with an increased number of gaze alternations between start and goal state, but do not show any effect on the durations of these inspections of the states (Nitschke et al., 2012). Conversely, higher demands on Search Depth and actual planning in terms of mental manipulations of working memory contents coincide with a prolonged duration of the very last inspection of the start state (i.e., immediately preceding movement execution) but do not show an effect on the number of gaze alternations (Kaller et al., 2009; Nitschke et al., 2012). This dissociation in eye-movement data directly concurs with a corresponding double dissociation in functional MRI data on the differential neural contributions of the left and right mid-dorsolateral prefrontal cortex for processes of internalization and core planning, respectively (Kaller, Rahm, Spreer, et al., 2011; Ruh et al., 2012; cf. Byrd et al., 2011; Crescentini, Seyed-Allaei, Vallesi, & Shallice, 2012). Given these findings, the rationale of the present study was to use pupillometry as a complementary approach for validating dissociable stages of internalization and core planning. More specifically, by analyzing the pupillometry data recorded in the study of Nitschke et al.(2012), we expected to find an increase of pupil size for higher demands on Search Depth exclusively during the proposed phase of core planning, i.e. the very last inspection of the start state before the solution execution. Furthermore, although the internalization phase is considered a cognitively less demanding process of matching and comparing states for identifying the problem-relevant differences (Nitschke et al., 2012), even simple stimulus-response matching processes have been previously shown to impact on pupil dilation as well (Beatty & Wagoner, 1978; Poock, 1973). In consequence, we further explored whether Tower Configuration exerted differential effects on pupil dilation particularly during the internalization phase, i.e. during the first shifts of gaze between start and goal state after problem presentation.

74 Methods 3.2

3.2 Methods

The present analyses were based on the pupillometry data recorded in the eye- movement experiments with four groups of subjects reported by Nitschke et al.(2012).

3.2.1 Sample Description

In total, 64 right-handed participants (age: M = 22.51, SD = 2.36, 44 females) with normal or corrected-to-normal vision were examined after providing informed consent (for a detailed report on the sample see Nitschke et al., 2012). None of them reported a present medical treatment or a psychiatric or neurological history. Participants received 10C as compensation.

3.2.2 The Tower of London Task and Problem Parameters

The original ToL consists of two states, the start and the goal state (Fig. 3.1), with each state comprising three differently colored balls placed on three pegs of different heights (Shallice, 1982). Here, a slightly altered, computerized version of the ToL with three pegs of equal capacity was employed (cf. Kaller et al., 2009; Unterrainer, Rahm, et al., 2005; Ward & Allport, 1997). For solving a given ToL problem, the participants’ task was to transform the start state so that it matched the goal state within three moves (which corresponded to the minimum number of moves for all problems) while following several rules: (i) Only one ball could be moved at a time; (ii) only the topmost ball on each peg could be moved; and (iii) balls always had to be placed on pegs. The computer program precluded any rule breaks. In the present study, two structural problem parameters of the ToL, Tower Conﬁguration and Search Depth, were manipulated so as to dissociate cognitively distinct processes of internalization and core planning (Fig. 3.1-A; see above). The parameter Tower Conﬁguration describes the arrangement of the balls on the pegs of the start and the goal state (e.g. Heinze et al., 2014; Kaller et al., 2013, 2004; Nitschke et al., 2012; for review, see Kaller, Rahm, Köstering, & Unterrainer, 2011).

75 TASK-DEMAND-SPECIFIC DISSOCIATING BY PUPILLOMETRY

Three different levels of this parameter were used: (i) a full tower (all three balls on one peg) of the start state, a partial tower (only two balls on the same peg, the third ball on a different peg) of the goal state (full–partial Tower Configuration); (ii) a partial tower of the start state and a full tower of the goal state (partial–full Tower Configuration); (iii) partial towers of both the start and the goal state (partial–partial Tower Configuration; Fig. 3.1-A; cf. Nitschke et al., 2012). The parameter Search Depth describes the number of intermediate moves before the first goal move and the resulting interdependencies between moves (e.g., Borys, Spitz, & Dorans, 1982; Kaller et al., 2004, 2009; for a review, see Kaller, Rahm, Köstering, & Unterrainer, 2011). Intermediate moves occur when a ball is not placed in its final goal position, but has to be relocated to enable another ball to be placed in its final goal position. Three-move ToL problems feature either one or no intermediate move, resulting in two levels for the parameter Search Depth (Fig. 3.1-A; cf. Nitschke et al., 2012). Given twelve observations per cell of the 3×2 experimental design, present analyses are based on 72 three-move ToL problems per participant. For further details, see Nitschke et al.(2012).

3.2.3 Experimental Groups

In the study of Nitschke et al.(2012) the overall sample was divided into four equally sized groups (n = 16 per group) which were presented with different spatial Alignments of the ToL start and goal state. Group names corresponded to the respective type of Alignment: (i) SBGT with the (S)tart state at the (B)ottom and the (G)oal state at the (T)op; (ii) STGB with the (S)tart state at the (T)op and the (G)oal state at the (B)ottom, (iii) SLGR with the (S)tart state on the (L)eft and the (G)oal state on the (R)ight; and (iv) SRGL with the (S)tart state on the (R)ight and the (G)oal state on the (L)eft side of the monitor (Fig. 3.1-B). That is, two groups were presented with a vertical Alignment (SBGT, STGB) and two groups with a horizontal Alignment (SLGR, SRGL) of the start and the goal state (Fig. 3.1-B). However, given that Nitschke et al. (2012) found no differences in eye-movement patterns between groups with

76 Methods 3.2

horizontal and vertical Alignment, potential between-group differences in Alignment were modelled as a covariate of no interest here.

3.2.4 Procedure, Apparatuses, and Processing of Data

The ToL was implemented in the Presentation® 12.2 software package (Neurobe- havioral Systems, Inc., Albany, CA). Movement of balls for solution execution was controlled using a three-button computer mouse with buttons corresponding to the three pegs. A ball could be moved by two button presses: The first button press selected the topmost ball of the respective peg (e.g. left button for topmost ball of left peg) and the second button press determined the peg on which the ball was to be placed (e.g. middle button for middle peg). Handling of the computer mouse was practiced in 48 one- and two-move ToL problems. Participants were seated 57 centimeters distant to a 19 inch computer screen equipped with an iViewX™ HiSpeed eye tracking device (SensoMotoric Instruments GmbH, Teltow/Berlin, Germany). The examination started with a calibration of the eye tracker. The iViewX HiSpeed eye-tracking device samples at 350 Hz and provides data of single fixations including information on their durations, on the respective vertical and horizontal positions of gaze as well as on the vertical and the horizontal diameters of the pupil in pixels. Only Fixations longer than 40 ms were considered. Blinks were detected and erased automatically by the eye-tracking device. For the present analyses, data of vertical and horizontal pupil diameter were averaged. Concordance between vertical and horizontal pupil diameter was 99.1%. To minimize the potential effect of luminance change the background that covered more than 90% of the screen was of a constant middle gray during the whole experiment. Whenever multiple fixations on the same state occurred consecutively, the pupil diameters were averaged weighted by the duration of the respective fixations. Alternate gaze dwellings on the start and the goal state were considered as separate inspections. A main finding of Nitschke et al.(2012) was that, in the majority of trials, initially (i.e. immediately after problem presentation) participants looked either to the left state or the top state (dependent on the horizontal or vertical Alignment, respectively),

77 TASK-DEMAND-SPECIFIC DISSOCIATING BY PUPILLOMETRY

irrespective of whether this constituted the start or the goal (see also Kaller et al., 2009). Another regularity was found for the end of the planning phase (i.e. just before solution execution), where, again in the majority of the trials, participants looked at the start state (completely independent of its specific horizontal/vertical location) (Nitschke et al., 2012). As a consequence, in two groups (SBGT, SRGL) participants commonly performed four alternating inspections (cf. Fig. 3.2-A) of the start and goal state during the time from problem presentation to beginning of the solution execution, with the First Inspection directed towards the goal state on the top/left (Fig. 3.1-B). In the other two groups (STGB, SLGR) participants commonly performed five alternating inspections (cf. Fig. 3.2-B) of the start and goal states, with the First Inspection directed towards the start state on the top/left (Fig. 3.1-B). As is depicted in Figure 3.2, when the First Inspection occured for the goal state there were only four inspections (Fig. 3.2-A) for the data we have analyzed, whereas when the First Inspection occurred for the start state there were five inspections (Fig. 3.2-B ). For the present analyses of changes in pupil diameter, the pupil dilation during the First Inspection of every trial was used as the trial-specific baseline and subtracted from the consecutive inspections (see Causse et al., 2010, for a similar approach). Consequently, this baseline inspection was excluded from the analyses (cf. Fig. 3.3). Given that participants showed predominant patterns of five vs. four inspections in the STGB and SLGR vs. SBGT and SRGL groups (Nitschke et al., 2012; cf. Kaller et al., 2009), the present analyses in the STGB and SLGR groups included four inspections without baseline (i.e., without the 1st inspection), whereas analyses in the SBGT and SRGL groups comprised three inspections without baseline. As analyses of inspections were ordered with reference to the last inspection (LI), notations of inspections are as follows: For the STBG and SLGR groups, the baseline (or 1st inspection) corresponds

nd th to LI-4, the 2 inspection to LI-3, the 3rd inspection to LI-2, the 4 inspection to LI-1, and the 5th (and last) inspection to LI. For analyses in the SBGT and SRGL groups, the baseline (or 1st inspection) corresponds to LI-3, the 2nd inspection to LI-2, the 3rd inspection to LI-1, and the 4th (and last) inspection to LI. A schematic illustration of the notations is provided in Figure 3.2.

78 Methods 3.2

A First Inspection on Goal State

Goal State 1st inspection 3rd inspection (LI-3) (LI-1)

2nd inspection 4th inspection (LI-2) (last inspection, LI) Start State

0 500 1000 1500 2000

B First Inspection on Start State

Goal State 2nd inspection 4th inspection (LI-3) (LI-1)

1st inspection 3rd inspection 5th inspection (LI-4) (LI-2) (last inspection, LI) Start State

0 500 1000 1500 2000 Time Course in Milliseconds

Figure 3.2. A schema of the courses of gaze alternation between the start and goal state in the time interval between the problem presentation and the beginning of the solution execution. The schematic time courses are separately plotted for the groups that started their First Inspections either (A) on the goal state (SBGT and SRGL) or (B) on the start state (STGB and SLGR). Independent from the First Inspection, all groups ended their planning phase on the start state and have, therefore, four (A, SBGT and SRGL) resp. ﬁve (B, STGB and SLGR) inspections. Note that the depicted durations of individual inspections correspond to the averages of the respective observed durations: that is for First Inspection on Goal (M ± SEM), LI-3 (447 ± 7.7 ms), LI-2 (398 ± 5.8 ms), LI-1 (407 ± 5.4 ms), LI (580 ± 9.5 ms), and for First Inspection on Start, LI-4 (330 ± 5.3 ms), LI-3 (494 ± 6.5 ms), LI-2 (463 ± 6.4 ms), LI-1 (389 ± 4.8 ms), LI (592 ± 10.4 ms).

The selection of trials for the present analysis was the same as that used by Nitschke et al.(2012) in their analysis of inspection length. In detail, only trials were chosen (i) which were correctly solved within three moves, (ii) where exactly four (First Inspection on goal state, groups SBGT and SRGL) or ﬁve (First Inspection on start state, groups STGB and SLGR) inspections were made, respectively, and (iii) where the last inspection was on the start state. In total, 31.3% of all trials were selected. This ensured that pupillometry data were not confounded by inter-trial differences in solution accuracy (and related differences in the quality of cognitive processing) and in the number and pattern of gaze alternations. For more detailed information on the selection and resulting analyzed trials see Table 3.1 in the Supplement Materials.

79 TASK-DEMAND-SPECIFIC DISSOCIATING BY PUPILLOMETRY towards left/top gaze shift numbers* total trials in % total trials in % total trials in % partial start – partial goal 10.6 (0.3) 88.5 (2.7) 9.3 (0.5) 77.1 (4.5) 5.1 (0.7) 42.7 (5.6) . Detailed information on the factors used for selecting a homogenous, non-confounded sample of trials Every participant performed 72 trials in total resulting in 12 trials per factor combination. Values indicate mean values standard move withoutintermediatemove partial start – full full goal start – partial goal 10.8 partial (0.3) start – partial 11.6 goal (0.2) 89.6 (2.5) 96.4 10.1 (1.3) (0.5) 9.9 (0.4) 7.5 84.4 (0.7) (3.9) 82.8 (3.2) 8.8 (0.6) 62.5 (5.9) 4.6 (0.7) 3.7 72.9 (0.5) (4.6) 38.0 (5.7) 3.8 (0.4) 30.6 (4.1) 31.8 (3.3) intermediatemove partial start – fullwithout goalintermediatemove 10.9 partial (0.3) start partial – start partial – goal full full goal start 91.1 – (2.2) partialintermediate goal 10.1 (0.5)move 8.0 (0.6) 10.6 partial 83.9 (0.4) start partial (3.9) – start partial 11.3 – goal (0.3) fullwithout goal 62.5 (6.4) 88.5 9.4 (2.9) (0.7)intermediate 93.8 10.7 (2.6) (0.3) 5.4move (0.7) 7.8 (0.6) 78.1 11.1 partial 8.5 (5.5) 89.1 (0.2) start (0.7) partial (2.5) – start partial – goal full 45.0 full goal (6.0) 65.1 start 4.6 (5.1) 92.2 – (0.7) 9.2 (1.8) partial (0.5)intermediate goal 70.8 (5.5) 10.4 (0.3) 4.5move (0.6) 9.8 (0.6) 38.5 5.6 (5.7) 76.6 11.3 partial (0.6) (4.2) 87.0 (0.2) start partial (2.7) – start partial 10.9 – goal (0.2) fullwithout 37.2 goal (4.8) 81.8 4.7 (4.9) 94.3 (0.3) 10.1 (2.0) 46.4 (0.6)intermediate (4.7) 90.6 10.9 (1.8) (0.2) 4.6move (0.6) 10.3 84.4 (0.4) 39.4 (4.9) (2.8) 10.3 partial 9.1 91.1 (0.3) start (0.6) partial (1.9) – start partial – 85.4 goal full 4.1 38.5 (3.7) full goal (0.5) (5.1) start 85.4 – 10.1 (2.1) partial (0.5)intermediate goal 76.0 (5.3) 10.4 3.7 (0.3) (0.4) 33.9 8.3 (4.5) 84.4 (0.8) (4.5) 3.4 10.9 partial (0.5) 87.0 (0.3) start partial (2.5) – start 31.1 partial 10.9 – (3.5) goal (0.3) full 3.3 goal (0.5) 68.8 (6.3) 91.1 9.8 (2.5) 28.1 (0.7) (3.9) 90.6 10.7 (2.3) (0.3) 4.9 27.8 (0.7) 8.6 (4.0) (0.8) 76.6 10.2 9.6 (7.3) 89.1 (0.3) (0.7) (2.1) 40.6 (6.0) 71.4 4.6 (6.3) 84.9 (0.6) 9.6 (2.7) (0.6) 80.2 (5.8) 3.8 (0.6) 10.0 (0.4) 38.7 5.0 (5.1) 75.2 (0.7) (6.6) 83.3 31.5 (3.6) (4.6) 4.5 (0.5) 41.7 (5.6) 5.5 (0.6) 37.2 (4.2) 45.8 (4.8) Group Search DepthSBGT Tower Conﬁguration with Corretness full start – partial goalSTGB with 10.8 (0.3) First 89.6 Inspection (2.7) 9.3 (0.8) full start – Trials with partial predominant goal 78.1SRGL (6.7) 5.3 with (0.5) 11.1 (0.3) 44.4 (4.2) 92.2 (2.2) 9.4 (0.6) full start – partial goal 78.1SLGR (5.0) 3.6 with (0.4) 10.8 (0.2) 30.2 (3.4) 89.6 (1.9) 9.1 (0.7) full start – partial goal 76.0 (5.4) 5.1 (0.7) 11.3 (0.3) 42.2 (6.0) 93.8 (2.2) 7.9 (0.6) 65.6 (5.4) 4.4 (0.6) 36.7 (4.6) Table S3.1 for the pupil size analysis. Annotations. errors of mean (SEM)inspections in for brackets. the groups *The STGB predominant and SLGR. gaze shift numbers were four inspections for the groups SBGT and SRGL and ﬁve

80 Results 3.3

3.2.5 Statistical Analysis

Changes in pupil dilation were analyzed with the mixed model package lme4 (Bates, Mächler, Bolker, & Walker, 2014) and lmerTest (Kuznetsova, Brockhoff, & Christensen, 2014) in the open-source statistics software R (R Core Team, 2013). The method of calculating effect sizes for single effects and interactions in mixed models is an area of active development, hence, no effect sizes could be reported. The model was fitted including the main effects, all possible interactions of the between-subjects factor First Inspection as well as interactions of the within-subject factors Search Depth, Tower Configuration, and Inspection Number as fixed effects. Furthermore, intercepts for every subject, for the covariate Alignment and for the main effects of the within-subject factors were modeled as random effects8. For parameter estimation, the maximum likelihood approach was chosen due to its higher accuracy for fixed parameters (Field, Miles, & Field, 2012). Significant main effects and interactions were further analyzed with post-hoc one sample (against baseline), two-sample (First Inspection) and paired t-tests (Search Depth, Tower Configuration, and Inspection Number) and based on the predicted values of the model.

3.3 Results

3.3.1 Main Analysis

Changes in pupil dilation were analyzed using a mixed model with the three within- subject factors Search Depth (two levels: with intermediate move, without intermediate move), Tower Conﬁguration (three levels: full–partial, partial–full, partial–partial), and Inspection Number (four levels: LI-3, LI-2, L-1, LI; note that for First Inspection on the goal state, there was no LI-3 inspection) and the between-subjects factor First Inspection (two levels: First Inspection on start state vs. on goal state).

8See http://www.uni-kiel.de/psychologie/rexrepos/posts/anovaMixed.html#mixed-effects-analysis-4 for more detailed information on the model’s parameter settings.

81 TASK-DEMAND-SPECIFIC DISSOCIATING BY PUPILLOMETRY

A Search Depth × Inspection Number

2.6 Search Depth ● with intermediate move 2.4 ● without intermediate move 2.2 2.0 1.8

1.6 ● 1.4 1.2 1.0 0.8 ● ● 0.6 Pupil Diameter Change in Pixel 0.4 0.2 0.0 LI−3 LI−2 LI−1 LI Goal State Start State Goal State Start State Inspection Number/Respective State B Tower Configuration × First Inspection

2.2 First Inspection on Goal State 2.0 Start State 1.8 1.6 1.4 1.2 1.0 0.8 0.6 Pupil Diameter Change in Pixel 0.4 0.2 0.0 full start partial start partial start partial goal full goal partial goal Tower Configuration C First Inspection × Inspection Number

3.2 First Inspection on ● Goal State ● 2.8 Start State 4th 5th

2.4 ● ●

● 2.0

4th 1.6 Figure 3.3. (A) Interaction between Search 1.2 rd Depth and Inspection Number. (B) Interaction 2nd 3

0.8 ● ● ● ● between Tower Conﬁguration and the First In-

nd rd 0.4 2 3 spection. (C) Courses of pupil dilation changes Pupil Diameter Change in Pixel 1st ● ● for all Inspections dependent on the First In- 0.0 1st spection’s factor level. Bars and symbols indi-

−0.4 LI−4 LI−3 LI−2 LI−1 LI Start State Goal State Start State Goal State Start State cate mean (M) values whereas the whiskers Inspection Number/Respective State indicate the standard errors of mean (SEM).

82 Results 3.3

Table 3.2. Main effects and interactions on the change in pupil diameter. Effects Factor F df1 df2 p Main Effects Search Depth (SD) 1.010 1 48.7 .320 Tower Conﬁguration (TC) 0.533 2 116.5 .588 Inspection Number (IN) 158.821 3 163.7 <.001 First Inspection (FI) 7.106 1 66.4 .010

2-way Interactions SD × TC 1.594 2 4768.6 .203 SD × IN 3.872 3 4674.4 .009 SD × FI 2.163 1 51.0 .147 TC × IN 1.675 6 4662.4 .123 TC × FI 3.124 2 125.4 .047 IN × FI 51.770 2 162.8 <.001

3-way Interactions SD × TC × IN 0.210 6 4647.6 .974 SD × TC × FI 0.863 2 4789.5 .481 SD × IN × FI 0.684 2 4672.6 .560 TC × IN × FI 0.675 4 4659.3 .682

4-way Interactions SD × TC × IN × FI 0.367 4 4646.3 .832 Note. Table contains Satterthwaite approximation for degrees of freedom (df). Signiﬁcant effects are highlighted in bold font. SD, Search Depth; TC, Tower Conﬁguration; IN, Inspection Number; FI, First Inspection.

A complete overview on the inferential statistics for all main effects and interactions is provided in Table 3.2. The main effects of Inspection Number (p < .001) and First Inspection (p = .010) were significant. The two-way interactions of Inspection Number × Search Depth (p = .009, Fig. 3.3-A), of First Inspection × Tower Configura- tion (p = .047, Fig. 3.3-B), and of First Inspection × Inspection Number (p < .001, Fig. 3.3-C) were significant. None of the remaining main effects or interactions reached significance (Table 3.2).

3.3.2 Post-hoc Analyses

All signiﬁcant main and interaction effects were further explored by the post-hoc analyses reported in the following paragraphs (see also the respective subpanels in Fig. 3.3). Inspection Number (Fig. 3.3-A): Post-hoc tests revealed that the pupil sizes increased signiﬁcantly from LI-4 (baseline) to LI-3 (one-sample t-test: t(31) = 4.365, p

83 TASK-DEMAND-SPECIFIC DISSOCIATING BY PUPILLOMETRY

< .001), between LI-2 and LI-1 as well as between LI-1 and LI (both t(63) > 6.429, p <

.001). No increase was observed between LI-3 and LI-2 (t(31) = 0.314, p = .756). First Inspection (Fig. 3.3-C): The groups with the First Inspection on the start state showed a larger pupil dilation change overall (M ± SEM: 1.480 ± 0.190) than the groups with the 1st inspection on the goal state (M ± SEM: 1.123 ± 0.146). This effect is due to a general increase in pupil dilation as the trial progresses and, more specifically, the additional inspection (five vs. four inspections). Search Depth × Inspection Number (Fig. 3.3-A): A significant effect of Search Depth on pupil dilation was only evident in the last inspection (LI) before movement

execution (t(63) = 4.236, p < .001) but not in any of the preceding inspections (highest

t(31/63) = 0.412, lowest p = .682). Tower Conﬁguration × First Inspection (Fig. 3.3-B): The groups with the 1st inspection on the goal state showed larger pupil dilation changes for partial–full Tower

Conﬁgurations compared to full–partial (t(30) = -3.144, p = .004) and partial–partial

Tower Conﬁgurations (t(29) = -1.829, p = .078), whereas there were no differences

between problems with full–partial and partial–partial Tower Conﬁguration (t(30) = 1.523, p = .138). In contrast, no differential effects of Tower Conﬁguration were

observed in the groups with the First Inspection on the start state (highest t(31/63) = 1.566, lowest p = .127). First Inspection × Inspection Number (Fig. 3.3-C): In the groups with the 1st inspection on the goal state a change in pupil dilation was observed from LI-3 (baseline)

to LI-2 (one-sample t-test: t(31) = 6.461, p < .001) and for LI-1 compared to LI (t(31)

= 19.879, p < .001), but not between LI-2 and LI-1 (t(31) = 0.091, p = .928). The opposite pattern was observed for the groups with the 1st inspection on the start state, where pupil dilation size changed signiﬁcantly from LI-4 (baseline) to LI-3

(one-sample t-test: t(31) = 4.365, p < .001) and between LI-2 and LI-1 (t(31) = 3.665,

p < .001), but not between LI-3 and LI-2 (t(31) = 0.314, p = .756) or between LI-1

and LI (t(31) = 0.401, p = .691). Thus, as is evident from Fig. 3.3-C, pupil dilation increased systematically in a staircase-like pattern from the 1st inspection (baseline) to the 2nd inspection as well as between the 3rd and 4th inspection irrespective of whether participants ﬁrst looked at the start or the goal state.

84 Results 3.3

Table 3.3. Main effects and interactions on the change in pupil diameter locked on the ﬁrst inspection. Effects Factor F df1 df2 p Main Effects Search Depth (SD) 1.225 1 48.7 .274 Tower Conﬁguration (TC) 0.645 2 116.5 .527 Inspection Number (IN) 168.363 3 163.7 <.001 First Inspection (FI) 0.156 1 66.4 .694

2-way Interactions SD × TC 1.259 2 4768.6 .284 SD × IN 1.664 3 4674.4 .173 SD × FI 2.829 1 51.0 .099 TC × IN 1.074 6 4662.4 .376 TC × FI 4.561 2 125.4 .012 IN × FI 0.958 2 162.8 .386

3-way Interactions SD × TC × IN 0.436 6 4647.6 .855 SD × TC × FI 0.325 2 4789.5 .722 SD × IN × FI 3.830 2 4672.6 .022 TC × IN × FI 1.349 4 4659.3 .249

4-way Interactions SD × TC × IN × FI 0.025 4 4646.3 .999 Note. Table contains Satterthwaite approximation for degrees of freedom (df). Signiﬁcant effects are highlighted in bold font. SD, Search Depth; TC, Tower Conﬁguration; IN, Inspection Number; FI, First Inspection.

3.3.3 Control Analysis

Based on previous eye-movement analyses (cf. Kaller et al., 2009; Nitschke et al., 2012), effects of Search Depth were expected to occur specifically at the end of the planning phase corresponding to the last inspection of the start state before movement execution. Testing this hypothesis hence required examining the levels of the factor Inspection with reference to the last inspection (see Fig. 3.2) given the different number of inspections between start and goal state dependent on the First Inspection (i.e., four vs. five inspections if the First Inspection was on the goal state vs. on the start state, respectively). Due to the disparity of inspections numbers (four vs. five) between the two First Inspection groups and the special interest in the very end of the planning phase led to the decision of lock the groups on their last inspection. However, this choice might promote false artificial statistical artifacts such as (non-)interactions and (non-)main effects especially at the start. To test for the possibility of false positive and/or false negative effects, complementary control analyses were conducted with the reference

85 TASK-DEMAND-SPECIFIC DISSOCIATING BY PUPILLOMETRY

First Inspection × Inspection Number

3.2 First Inspection on ● Goal State ● 2.8 Start State goal start

2.4 ● ●

● 2.0 start 1.6 1.2 goal start

0.8 ● ●● ●

start

0.4 goal Pupil Diameter Change in Pixel start ●● 0.0 goal

−0.4 FI FI+1 FI+2 FI+3 FI+4

Inspection Number

Figure 3.4. Courses of pupil dilation changes for all Inspections as a function of the First Inspection. Thus, while the main analysis of the paper was locked on the last inspection (LI), the supplementary control analysis was locked on the ﬁrst inspection (FI). It becomes apparent that the Inspection Number × First Inspection of the main analysis was caused by a shift due to last inspection lock and that the course of the pupil dilation is indeed independent of the location of the First Inspection.

for the factor Inspection locked to the First Inspection. If this analysis revealed results that were unexpected from the main last Inspection locked analysis that would point to statistical artifacts. The respective results are listed in Table 3.3. As the last inspection in the current approach was in the 4th inspection for First Inspection on the goal state and in the 5th inspection for the First Inspection on the start state, an interaction of Search Depth × Inspection Number × First Inspection was expected and conﬁrmed by the analysis (Tab. 3.3). Moreover, it was revealed that the Inspection Number × First Inspection interaction of the main analysis was due to the shift of the First Inspection and that, indeed, all groups showed the same initial course of pupil dilation (see Fig. 3.4). The results of these control analyses hence underline the validity of the present ﬁndings.

3.4 Discussion

Previous analyses of eye movements suggest that information processing during human planning can be separated into a temporal sequence of internalization processes

86 Discussion 3.4

and subsequent processes of core planning (Kaller et al., 2009; Nitschke et al., 2012; see also Ruh et al., 2012). The aim of the present study was to validate these findings by exploiting the methodology of pupillometry as an additional valuable source of information on the temporal course of cognitive processing. To this end, task demands on processes of internalization and core planning were experimentally manipulated using a factorial variation of two structural problem parameters in the Tower of London task, namely Tower Configuration and Search Depth (cf. Nitschke et al., 2012). In the following, we will discuss the findings starting with the implications from the observed more general patterns and thereafter addressing the parameter-specific effects.

3.4.1 General effects on pupil dilation

First of all, results revealed a staircase-like pattern with increases and stable plateaus in pupil size across alternating inspections of the start and goal state (Figs. 3.3-C, S2) which clearly differed from a steady increase of pupil size across time. Thus, while a continuous increase across time could have been an indication for a merely duration- dependent rather than for a cognitively induced change in pupil size, the staircase-like pattern suggests a direct link between pupil dilation and cognitive processing (or at least a combination of both). Assuming that some kind of an internal representation of the problem is built-up during the first inspections (Kaller et al., 2009; Nitschke et al., 2012), we suggest that the staircase-like pattern may reflect the alternation between cognitively more demanding processes of comparing and matching information versus cognitively less demanding processes of gathering information. That is, when considering Figure 3.3-C, it may be the case that after information about the 1st inspected state is partially extracted, this information is subsequently compared to the information of the 2nd inspected state. This potentially results in the encoding of the first, directly attainable goal move, as reflected in a significant increase in pupil size. Afterwards, during the 3rd inspection (again on the 1st state), further information may be gathered (or a validation of the previous transformation occurs) while the pupil size remains constant.

87 TASK-DEMAND-SPECIFIC DISSOCIATING BY PUPILLOMETRY

The subsequent increase in pupil size during the 4th inspection again suggests that comparing, matching, and encoding of information occurs. Albeit speculative, this interpretation would be supported by previous ﬁndings (i) that matching and comparing information results in a pupil dilation (Beatty & Wagoner, 1978; Poock, 1973) and (ii) that information on the visuo-spatial conﬁguration of problem states is built up in working memory only after several successive inspections (cf. Ballard, Hayhoe, Pook, & Rao, 1997; Körner, 2011) so as to presumably minimize the complexity of the representations (Ballard, Hayhoe, & Pelz, 1995; Hayhoe, Bensinger, & Ballard, 1998; Kool, McGuire, Rosen, & Botvinick, 2010; Körner, 2004; Waldron, Patrick, Morgan, & King, 2007) and the overall cognitive load (Körner, 2004; Körner et al., 2014).

3.4.2 Parameter-speciﬁc effects of Tower Conﬁguration on pupil dilation and processes of internalization

Further evidence for a step-by-step processing of information may be seen in the effects of Tower Configuration which is assumed to exert differential demands on processes of internalization and, therefore, on building up a mental representation. In the eye-movement analyses of Nitschke et al.(2012) Tower Configuration impacted on the overall number of gaze shifts in the time between the problem presentation and the onset of the solution execution. Here, likewise, effects of Tower Configuration on pupil diameter were observed independent of individual inspections across the time course. That is, they were similarly expressed in all inspections, suggesting a continuing influence on cognitive processing. As balls of a full tower are more easily encodable at once (by the relative positions of balls on a single rod) than those of a partial tower (encoding the balls’ allocations to individual rods plus the balls’ positions on these rods) (cf. Nitschke et al., 2012), one might assume that a complete representation is more likely built up at the first inspection for full towers or respectively a more complete presentation is built up faster during the course of the trial. Then however, one would have expected a strong increase of pupil dilation during the first inspection of the goal state if this state was a full tower, as opposed to a temporally more distributed pattern in partial goal tower configurations that cannot

88 Discussion 3.4

unambiguously be coded at first sight. No such interaction of Tower Configuration and Inspection Number was observed, thus again suggesting step-by-step encoding of goal information. Furthermore, the effect of Tower Configuration was found to depend on the type of state (start vs. goal) that was focused on during the First Inspection: Participants fixating the goal state first showed a higher overall pupil dilation if this first fixated state was a full tower and not a partial tower irrespective of the configuration of the start state (Fig. 3.3-B). In contrast, participants fixating the start state first showed no significant effects of Tower Configuration. However, on a purely descriptive level, in these participants it also becomes apparent that pupil dilations were larger if the first fixated (start) state was a full and not a partial tower irrespective of the configuration of the goal tower (Fig. 3.3-B). Thus, like the stair-case pattern that did not depend on whether the goal or the start state was initially fixated, Tower Configuration indicated that participants used the information (i.e. start or goal state; cf. Berg et al., 2010) viewed during the first inspection to guide further processing. Otherwise, a state dependent stair-case pattern and a three-way interaction effect between Tower Configuration, First Inspection and Inspection Number would be expected. However, surprisingly, larger pupil diameters were found for the full tower states, i.e. for the type of configuration that was expected to be easier to process and thus to result in smaller effects on pupil dilation. This may be explained by the same mechanism that potentially accounts for the staircase-like pattern: In full towers, the ball that needs to be put into its goal position (if a goal state was fixated) or the ball that needs to be moved (if a start state was fixated) can earlier during the trial be encoded and transferred into working memory and used for goal-directed matching and comparing information across states, thus inducing cognitively demanding information processing. In partial states, on the contrary, this information is not readily available and participants may still be concentrating on identifying the balls and moves that are to be encoded via visual matching, thereby minimizing cognitive and memory load and inducing a less cognitively demanding type of information processing. In accordance with this view, Ballard et al.(1997) showed that humans will not exploit their working memory capacity to its full extent if information can be gathered immediately before

89 TASK-DEMAND-SPECIFIC DISSOCIATING BY PUPILLOMETRY

it is needed (see also, Körner, 2004; Waldron et al., 2007). Moreover, Droll and Hayhoe(2007) showed that working memory is less exploited if the course of a task is unpredictable, which in our study is more likely the case in partial tower configurations. In consequence, if pupil diameter was primarily driven by demands for encoding, matching, and comparing information, full tower configurations may lead to higher pupil dilation despite being easier to represent mentally. In either case, the larger uncertainty that can be assumed to be present in operating on partial tower configurations and which in consequence may have been expected to lead to larger pupil sizes in more difficult tower configurations (Lempert, Chen, & Fleming, 2015; Nassar et al., 2012; Preuschoff, ’t Hart, & Einhäuser, 2011) did seem to be of subordinate importance. A still unresolved question concerns the exact nature and contents of the suggested processes of encoding, matching, and comparing information. Besides a purely serial order of building up an internal representation and subsequent core planning, the present (and previous) analyses (cf. Kaller et al., 2009; Nitschke et al., 2012) would also accord with a more overlapping interplay between both. In other words, processes of comparing and matching information between start and goal state could also include the identification and encoding of potential goal moves and hence comprise a more comprehensive representation than of the state’s (or states’) mere spatial configurations and/or their differences. Notably, such identification of potential goal moves via simple perceptual matching-to-sample is an efficient means of problem solving only in problems without an intermediate move. Hence this differs from processes of core planning, as it does not require mentally simulating and evaluating possible moves and their outcomes to resolve the interdependencies between move alternatives and their consequences for subsequent moves (Kaller, Rahm, Spreer, Mader, & Unterrainer, 2008).

3.4.3 Parameter-speciﬁc effects of Search Depth on pupil dilation and processes of core planning

As another central ﬁnding of the present study, analyses revealed inspection-speciﬁc effects of Search Depth on pupil dilation (Fig. 3.3-A), thus corroborating previous

90 Discussion 3.4

interpretations from analyses of eye-movement (Kaller et al., 2009; Nitschke et al., 2012) and neuroimaging data (Ruh et al., 2012). That is, as hypothesized, with higher demands on Search Depth (problems with an intermediate move compared to problems without an intermediate move) and related processes of core planning pupil size increased specifically during the last inspection of the start state before solution execution (Fig. 3.3-A). The temporal localization of the Search Depth effects on pupil dilation is in line with the proposed serial chronology of an initial internalization and subsequent core planning (cf. Kaller et al., 2009; Nitschke et al., 2012; Ruh et al., 2012). But as emphasized above, this does not necessarily preclude that the identification of potential goal moves occurs earlier in the course of alternating inspections between start and goal state. Furthermore, although the present differential effect of Search Depth on the last inspection (higher planning demands result in higher pupil dilation; Fig. 3.3-A) conforms to the assumption of a serial order of processing with the internalization preceding core planning, several questions remain. Specifically, if the core planning processes are indeed mainly occurring during the last inspection before movement execution, one would also expect a general increase of pupil dilation from the penul- timate (LI-1) to the last inspection (LI) (cf. Fig. 3.3-C). This expectation was met in the groups with four inspections and the First Inspection on the goal state, whereas the groups with five inspections and the First Inspection on the start state showed a plateau between LI-1 and LI (Fig. 3.3-C). At first glance this observation to some extent contradicts the present finding and interpretation of the Search-Depth-specific increase in pupil dilation during the last inspection (Fig. 3.3-A) as well as previous eye movement data (Kaller et al., 2009; Nitschke et al., 2012). Besides the above formulated perspective that a strictly serial chronology of processes of internalization and core planning may not be the best model, another explanation could be that changes in pupil size reflect other aspects of cognitive processing that are complementary to the aspects of cognitive processing captured by eye movement analyses of the location, course, and duration of gaze inspections (cf. Nitschke et al., 2012). In any case, although the present data cannot resolve all questions, analyses of pupil size

91 TASK-DEMAND-SPECIFIC DISSOCIATING BY PUPILLOMETRY

were found to provide important information that both complement and also partly challenge interpretations derived from sole considerations of eye-movement patterns.

3.4.4 Limitations

Present as well as previous evidence for a serial order of internalization and planning processes is available for easy three-move ToL problems only, which–due to their simplicity–allow for the investigation of general mechanisms of cognitive processing by restricting the large intra- and inter-individual variation that is typically observed in planning longer sequences (Ausburn & Ausburn, 1978; Cheetham, Rahm, Kaller, & Unterrainer, 2012). It hence remains to be tested whether the conception of planning as a sequential order of distinct processing phases (Newell & Simon, 1972; Ward & Morris, 2005) is indeed generalizable to planning in more complex ToL problems (cf. Nitschke, Köstering, Weiller, & Kaller, 2017) as well as to planning in real-life situations (Burgess, Simons, Coates, & Channon, 2005, see also Burgess, 1997; Hayes- Roth & Hayes-Roth, 1979; Ormerod, 2005). In this respect, complementing analyses of eye movements during planning with data on the concomitant pupil dilation provides a valuable source of information to identify systematic patterns even in light of a substantially higher variance of the data, a usually lower number of observations, and a greater task impurity when investigating more complex cognitive tasks (Chan et al., 2008; Miyake et al., 2000).

3.4.5 Conclusion

Taken together, besides validating associated theoretical assumptions of a (at least to some extent) sequential order of processing during planning (Newell & Simon, 1972; Ward & Morris, 2005) with processes of internalization preceding processes of core planning (Kaller et al., 2009; Nitschke et al., 2012; Ruh et al., 2012), the present results also highlight the value of pupillometry as a physiological marker of varying demands on cognitive processing (e.g. Beatty & Lucero-Wagoner, 2000; Causse et al., 2010). Furthermore, the present ﬁndings leave several intriguing questions open and

92 Discussion 4.0

may hence guide and stimulate future research on the course and nature of cognitive processing in planning and other complex cognitive tasks.

4 General Discussion

4.1 Summary of the Studies

In the preceding chapters two studies were presented that expanded the understanding and knowledge of the neural and cognitive foundations of human planning. In chapter 1.2 a recent theory of Goel(2010) on the functional lateralization of hemispheres in human planning was introduced. The theory assumed a rather strict separation for the involvement of left and right dorsolateral prefrontal cortices (dlPFCs) for different types of planning. The left dlPFC was assumed to process well-structured whereas the right dlPFC was assumed to process ill-structured planning tasks. These assumptions were mainly based on a selection of preceding lesion studies and, hence, were based on the dysfunction of the cortex and allegedly connected impairment in planning behavior. With suitable 29 neuroimaging studies on the well-structured planning task Tower of London (ToL), a sufﬁcient number of studies for a meta-analysis was provided allowing to put the theory to test in healthy subjects. In chapter 2 the results revealed a bilateral dlPFC involvement in healthy subjects. Hence, a strict distinction between hemispheres for well-structured planning is not maintainable. Moreover, the possibility of a relative lateralization between left and right dlPFC was tested statistically, but could not be veriﬁed (see chapter 2.3). However, these relative comparisons have to be interpreted with caution given the small number of underlying studies. Due to the fact that the theory was based on selected lesion studies an extensive literature search on lesion studies was conducted to resolve the discrepancy between the results reported in chapter 2 and the theory of Goel(2010).

95 GENERAL DISCUSSION

However, a thorough literature review on lesion studies revealed that there are no coherent results in the literature supporting an unilateral association with impairment in well-structured planning tasks (see Tables 1.1, 1.2 and 1.3). Taken together, there is no sufﬁcient evidence from brain lesion studies to indicate a unilateral involvement and there is considerable evidence for bilateral involvement from functional imaging studies. Hence, a jointly bilateral dlPFC involvement for well-structured planning should be the foundation of future theories. Lesions studies are imprecise in their anatomical localization leading them to mostly relate the entire prefrontal cortex (PFC) with planning tasks. Neuroimaging studies are anatomical more precise but still suffer from several localization distortions leading to a connection between planning tasks and the whole dlPFC (a subregion of the PFC, see Fig. 1.2, BA 9, 9/46 and 46). An additional result of the meta-analysis of chapter 2 was the further speciﬁcation and demarcation of a sub-area from the dlPFC, the mid-dlPFC (BA 46), that is crucial for human planning. In chapter 1.3 a sequential model of planning was introduced that assumes two distinct sub-phases, the initial representation creation and the subsequent sequence generation. Further it was detailed how both sub-phases were validated by different eye tracking techniques and the systematical manipulation of structural parameters of the ToL planning task (cf. Hodgson et al., 2000; Kaller et al., 2009; Nitschke et al., 2012). However, the application of eye tracking in more general tasks and in everyday life is rather limited. To overcome these limitations an easy-to-use and broadly established methodology – pupillometry – was employed to validate and generalize the former insights into the sub-phases of planning and increase the applicability. As anticipated, a general increase in pupil dilation was observed during the planning of the problems. This increase occurred in a staircase-like pattern, which was not hypothesized but revealed further insights into the framework of planning. Moreover, as expected a higher pupil dilation was revealed for more demanding trials only at the very end of the general planning phase during the sequence generation. Additionally, an effect during the representation creation was observed that is to some degree contrary to the implications of the sequential model of planning as well as to former studies.

96 The Neural Lateralization of Planning 4.2

4.2 The Neural Lateralization of Planning

4.2.1 Different Perspectives on the Neural Functional Lateraliza- tion

Goel(2010) argued in favor of an absolute dissociation between well- and ill- structured tasks and regarding dlPFC lateralization (see also Goel, 1995; Goel & Grafman, 1995; Goel et al., 1997, 2007, 2013; Goel, 2015). Well-structured tasks such as the ToH and the ToL were assumed to exclusively invoke the left dlPFC. Ill-structured planning tasks were assumed to involve the right dlPFC (Goel, 2010). However, as demonstrated in chapters 1.2 and 2.1, evidence from brain lesion studies is at most equivocal if not even speaking against a left lateralization for the well- structured planning tasks ToH and ToL. Although such an absolute dissociation is not maintainable, there are different models and explanation approaches that ascribe differential roles to a unilateral dlPFC. They satisfy the bilateral dlPFC involvement in well- and ill-structured planning tasks but also provide explanation approaches on reported lateralization for brain lesioned patients to different degrees. These theories will be summarized and compared in the following.

In the original ToL study, Shallice(1982) used his theory of the Supervisory Attentional System (SAS) to explain the planning impairment found specifically for left frontally lesioned patients. The SAS is hypothesized to be located in the PFC (Shallice, 2002; Shallice & Gillingham, 2013) and is based on the assumption that highly overlearned actions and skills are represented in action and thought schemas (Shallice, 1982; see also Norman & Shallice, 1980). Schemas are automatically triggered by appropriate inputs, with the most appropriate schema for a given routine situation selected by the process of contention scheduling. However, in non-routine or difficult situations, the activation or inhibition of schemas needs to be controlled by the SAS so as to regulate behavior in a deliberate, purposeful manner and to avoid inappropriate or persever- ating actions (Shallice, 1982; see also Norman & Shallice, 1980). Shallice(1982) suggested that the function of the SAS during planning is compromised specifically

97 GENERAL DISCUSSION

in left frontally lesioned patients leading them to fail the ToL task, notwithstanding that this lateralization could not be replicated later (Shallice, 1988, p. 347). In later work, sub-processes of the acquisition of suitable schemas (task-setting) are assumed to be left lateralized (Shallice & Gillingham, 2013; Vallesi, McIntosh, Crescentini, & Stuss, 2012), whereas the identiﬁcation of a mismatch between the favored goal and the actual state in terms of active monitoring or checking (Shallice & Cooper, 2011; Shallice & Gillingham, 2013) is presumed to be right lateralized within the PFC (Shallice & Gillingham, 2013). Both processes, task setting and active monitoring, are not dependent on each other but act in parallel and can therefore be separately compromised (Table 5.1) (Shallice & Cooper, 2011; Shallice & Gillingham, 2013), which would be in accord with differential roles of the left and right mid-dlPFC in planning (see also Crescentini et al., 2012). As another approach on prefrontal functioning, Grafman postulated Structured Event Complexes (SECs) to be located in the PFC (Grafman, 1989, 1995; Grafman et al., 2005). Within the SEC framework, the knowledge of a person is organized in scripts or SECs, which represent goal-oriented sets of events that comprise "thematic knowledge, morals, abstractions, concepts, social rules, event features, event bound- aries, and grammars" (Wood & Grafman, 2003, p. 142). The lateral prefrontal cortex is responsible for generating and sorting appropriate script sequences (Grafman et al., 2005), which is in contrast to the SAS where a suppression of inappropriate action schemas is a central point. Grafman(2007, p. 257; see also Wood & Grafman, 2003) attributes the focus of the left PFC "on the speciﬁc features of individual events [...] that make up a plan, whereas the right PFC mediates the integration of information across events" (Table 5.1). Moreover, sequential dependencies like sub-goaling (i.e. the necessity to devise actions that are not directly leading to goal attainment, but are nevertheless essential for it, such as intermediate or counterintuitive moves on the ToL) as well as propositional and event information are ascribed to the left PFC (Table 5.1). In contrast, thematic abstraction, the acquisition of features and meaning of plans across multiple events (including moral aspects), order sequences as well as plan and event time information, that is, information pertaining to the "macro-plan level" (Grafman et al., 2005, p. 192) are assumed to be right-lateralized (Wood &

98 The Neural Lateralization of Planning 4.2

Grafman, 2003). In summary, the SEC framework suggests that depending on the type and context of non-social planning, both the left and/or right dlPFC can be involved (Grafman et al., 2005; Wood & Grafman, 2003). However, in performance of tower tasks, which might be interpreted to represent single SECs that typically require sub-goaling, the left dlPFC should be predominantly involved (Grafman et al., 2005; Grafman, 2007). On the other hand, mediating the integration of information across events attributed to the right dlPFC (Grafman et al., 2005) might also be associated with sub-goaling processes as these (with respect to the ToL) require to integrate the interdependencies resulting from different move alternatives into a coherent action sequence for optimal solution (cf. Kaller, Rahm, Spreer, et al., 2011). Morris et al.(1997a, 1997b) tested a population of left and right frontally lesioned patients with the ToH and found that problem solutions of right lesioned patients were associated with a higher number of excessive moves compared to controls and left lesioned patients. However, left frontally lesioned patients were specifically impaired in dealing with goal-subgoal conflicts (Morris et al., 1997a; for similar results in both left and right frontal lesion patients, see Goel, 1995), which arise when a correct move increases the perceived distance to the goal via moving an object away from its goal peg. The seeming disparity between these results in terms of a general deficit for right lesioned and a specific deficit for left lesioned frontal patients was explained by assuming a lateralization of sub-processes such that the left dlPFC is crucial for adequately dealing with novelty in terms of inhibiting routinized actions in favor of the less prepotent, but correct action (Morris et al., 1997a), whereas the right dlPFC is necessary for sequence formation during planning (Morris et al., 1997b) (Table 5.1). Moreover, both studies show that often neglected structural problem parameters beyond the minimum moves to solution have a crucial influence on planning performance (cf. Kaller, Rahm, Köstering, & Unterrainer, 2011). Newman et al. (Newman et al., 2003, 2009; Newman & Pittman, 2007) investigated the cognitive and neural foundations of the ToL by systematically manipulating such structural problem parameters. The fMRI findings led them to the conclusion that the left dlPFC is associated with control processes as well as the representation and maintenance of general task demands. The right dlPFC is responsible for integration,

99 GENERAL DISCUSSION

manipulation, and maintenance of information within working memory which serve the formulation of a sequence for solving a given problem (Newman et al., 2009; cf. Newman & Pittman, 2007) (Table 5.1). Despite the similar terminology for the processes attributed to the right dlPFC (integration of information and sequence formation), Newman et al.(2003) conclusions are quite different from Grafman(2007) in that according to Newman et al.(2003), sub-goaling should mainly be attributed to the right dlPFC, whereas in the SEC framework, this process is thought to be subserved by the left dlPFC. Kaller, Rahm, Spreer, et al.(2011) examined the functional lateralization of the mid-dlPFC using a comparable approach of manipulating structural problem parameters of the ToL in an fMRI experiment. The concept underlying the experiment was that the overall planning process dissociates into two distinct sub-phases. The first phase is the internalization (cf. representation creation) phase where information about the start and the goal states of the ToL is transferred into working memory. The subsequent second phase is the core planning (cf. sequence generation) phase where the start state is mentally manipulated until its matches the goal state (cf. Newell & Simon, 1972). For problems with a constant number of three minimum moves, Kaller, Rahm, Spreer, et al.(2011) manipulated internalization via the tower configuration of problems (cf. Kaller, Rahm, Köstering, & Unterrainer, 2011; Nitschke et al., 2012), with an internal representation of the problem being more difficult to build up for goal states featuring a flat configuration (i.e. all balls on different pegs) than for goal states with a full tower configuration (i.e. all balls stacked on a single peg). Core planning was tapped into by variations in the search depth (Borys et al., 1982; Spitz et al., 1982; see also Kaller, Rahm, Köstering, & Unterrainer, 2011), which was either one or zero, i.e. problems demanded to devise one versus no intermediate move, thus varying in whether sub-goaling was required for the optimal problem solution or not. It was found that variations in tower configuration elicited stronger activation of the left mid-dlPFC, whereas search depth elicited stronger activation in the right mid-dlPFC, thus revealing a double-dissociation between internalization versus core planning and contributions of the left versus right mid-dlPFC (Kaller, Rahm, Spreer, et al., 2011). Moreover, a re-analysis of the time course revealed that the left mid-dlPFC activated

100 The Neural Lateralization of Planning 4.2

earlier during each trial and that tower configuration (i.e. internalization) had an effect only initially (Ruh et al., 2012). In contrast, during the end of the planning phase especially the right mid-dlPFC activated and search depth (i.e. the core planning processes) had an exclusive effect (Ruh et al., 2012). These results are in line with Newman et al.’s (2003) explanation approaches of left lateralized task-representation and right-lateralized plan generation processes (Table 5.1). In a comparable focus regarding the time course of cognitive sub-processes during planning, Byrd et al.(2011) conducted an EEG experiment with the number of minimum moves as the manipulation of problem difficulty. The temporal course of the planning phase was separated into three parts. In the first part, a higher EEG signal over the left lateral PFC but no difficulty effect was observed. This was interpreted as bottom-up stimuli processing (Byrd et al., 2011) and is in line with the results and interpretation of Kaller, Rahm, Spreer, et al. (Kaller, Rahm, Spreer, et al., 2011; Ruh et al., 2012). Especially the absence of an initial difficulty effect supports the assumption of sequential processes. In the second part, a slightly higher EEG signal over the right lateral PFC and a difficulty effect was reported, leading Byrd et al.(2011) to the conclusion that the stimuli were being processed by the (right) dlPFC. This suggests a pattern comparable to the results at the end of the planning phase of Ruh et al.(2012) and may indicate planning. However, during the last part of the planning phase, again a higher EEG signal over the left PFC and a difficulty effect were observed. Goel(2010) differentiated left-lateralized and right-lateralized PFC functions by considering the structuredness of planning tasks, that is, by taking into account differences between well- and ill-structured tasks in the systematic search of the problem space. In a more general review on the lateralization of the hemispheres, Goel(2015) argued about the different abilities to cope with determinacy and indeterminacy. The left hemisphere should pursue determinacy with the help of (prior) knowledge whereas the right hemisphere is expected to maintain and enhance indeterminacy. For planning tasks that would imply that well-structured tasks feature a high degree of determinacy and should, hence, involve mainly the left dlPFC. The ToL is a prototypical well-structured planning task in that the start and the goal state is known as well as the operators (high degree of determinacy) that are applicable to transform one ToL

101 GENERAL DISCUSSION

problem state into another (i.e., by moving a ball from one peg to another peg). In contrast, ill-structured planning tasks feature a low structuredness and determinacy (but also contain well-structured components, see Goel, 2010 and Goel, 2015) by not precisely providing a goal and/or possible actions to reach the goal. In general, Goel(1995) proposed that problem solving tasks involve four sub-phases: problem structuring ("a collection of statements that serve to solicit or generate information to structure the problem", p. 240), preliminary design ("a collection of statements that result in the initial generation and exploration of some aspect of the design", p. 240), design refinement ("a collection of statements that serve to elaborate and further the commitment to a previously generated design idea or element", p. 240), and detail specification ("collection of statements that serve to detail, and give the final form to, some aspect of the design" p. 240). Problem structuring and preliminary design9 are considered lateral transformations , which are particularly critical in ill-structured tasks, as they demand a widening of the problem space and enhancing indeterminacy, whereas design refinement and detail specification are considered vertical transformations, which are especially important in well-structured tasks, as they require a deepening the problem space (Goel, 1995; cf. Goel, 2010, 2015). In terms of left- versus right lateralized PFC functions, Goel and Grafman(1995) found no difference between ToH performance of left and right frontally lesioned patients. However, Goel and Grafman(2000) compared a right frontally lesioned architect with a healthy architect in an ill-structured architectural task. The patient was able to structure the problem, but unable to make the transition to problem solving. This was attributed to an insufficient coping with the preliminary design (Goel & Grafman, 2000). Further, Goel et al.(2007) examined PFC-lesioned patients in a task requiring relations to be analyzed, with trials being either determinate (providing all necessary information) or indeterminate (necessary information was withheld). A double dissociation was found, showing left lesioned patients to perform worse in determinate trials and right lesioned patients worse in indeterminate trials (Goel et al.,

9From Goel(2010): "A lateral transformation is one where movement is from one idea to a slightly different idea rather than a more detailed version of the same idea. [...] A vertical transformation is one where movement is from one idea to a more detailed version of the same idea." (p. 617)

102 The Neural Lateralization of Planning 4.2

2007). These findings led to the conclusion that right dorsolateral prefrontal cortex is involved in conflict and inconsistency as well as the construction and maintenance of vague and ambiguous representations (Goel et al., 2007). Left PFC was assumed to be involved in constructing precise representations of the world (Goel, 1995). Goel et al.(2013) found no differences between left and right frontally lesioned patients in the sub-phases of planning an ill-structured task (Travel Planning Task), but they found that right lesioned patients generated much more concrete plans that were of poorer quality. In an fMRI study with healthy subjects performing a divergent thinking task assumed to require lateral transformations, Goel and Vartanian (2005) found that left mid-dlPFC (BA 46) and right ventral PFC (BA 47) evidenced general task-related activity, left BA 9 and right BA 47 were related to accuracy of problem solutions, and right mid-dlPFC (BA 46) activity was specifically correlated with the number of generated solutions (cf. chapter 2.4). From these results, it was concluded that while the left dlPFC is involved in generating lateral transformations and the right dlPFC is responsible for maintaining these transformations or solutions in working memory, the right ventral PFC is the critical region for generating accurate lateral transformations (Table 5.1). Based on these studies, Goel(2010) formulated a framework of PFC function in planning/problem solving assuming that the left PFC subserves the generation of vertical transformations, particularly in well-structured tasks, whereas the right PFC is mainly responsible for the cognitive processes necessary for generating lateral transformations, which is crucial in ill-structured tasks.

4.2.2 An Approach on a Uniﬁed Neural Theory on Planning

In an attempt to summarize the positions of the different researchers, it becomes apparent that the explanation approaches run on different levels of theoretical gen- erality vs. speciﬁcity: The highest level applies more generally to the involvement of the PFC in complex human behavior, the middle level to different types of planning (such as well- versus ill-structured tasks), and the lowest level to speciﬁc cognitive sub- processes of planning. On the highest level, Shallice(1982) and Grafman et al.(2005) proposed computationally inspired frameworks that try to explain a broader part of

103 GENERAL DISCUSSION

Table 5.1. Overview of different theoretical positions on left and right dlPFC functions in planning. Functions ascribed to Researcher References left dlPFC right dlPFC Shallice Shallice, 1982, 1988, 2002; – task-setting– monitoring (SAS) Shallice & Cooper, 2011

Grafman Grafman, 1989, 1995, 2007; – features of single events that – integration across multiple (SEC) Grafman et al., 2005; Wood & make up a plan events Grafman, 2003 – sub-goaling; sequential dependencies

Morris Morris et al., 1997a, 1997b– novelty processing– strategy formation

Newman Newman & Pittman, 2007; – representation and mainte- – integration / manipulation of Newman et al., 2003, 2009 nance of task demands information – strategy formation & plan generation

Kaller Kaller, Rahm, Spreer, et al., – internalization & structuring – integration / manipulation of 2011; Ruh et al., 2012 of features information, core planning

Goel Goel, 1995, 2010, 1995; Goel – well-structured problems – ill structured problems & Grafman, 2000; Goel & Var- – vertical transformation: deep- – lateral transformation: widen- tanian, 2005; Goel et al., 2007, ening the problem space ing the problem space 2013 – design reﬁnement & detailing – preliminary design & problem phase structuring – precise representations – vague and ambiguous representations

human behavior. Hence, they utilized relatively abstract terms of ’action schemata’ and ’structured event complexes’ and are not specifically aimed at providing an account of the precise involvement of the dlPFC in planning on disc-transfer tasks like the ToL. As a case in point, Shallice’s explanation of left-lateralized task-setting and of right-lateralized output monitoring does not account for how exactly a plan for the solution of the ToL is generated. Likewise, in Grafman’s SEC framework that assumes the left dlPFC to be associated with features of single events and the right dlPFC with the integration of multiple events (Grafman, 2007), it is difficult to deduce particular predictions for the differential roles of the left and right dlPFC in performing the ToL. On a medium level of specificity, (Goel, 2010) assigned well-structured tasks such as the ToL to the left and ill-structured tasks to the right PFC. However, he noted that ill-structured tasks also include well-structured processes (Goel, 2010, 2015). The presented meta-analyses in chapter 2 demonstrated the bilateral involvement of mid-dlPFC in the well-structured planning task ToL. Therefore, it could be assumed that well-structured planning tasks also involve lateral transformations that serve to broaden the problem space or other components of ill-structured tasks, otherwise the involvement of the right mid-dlPFC cannot be explained. Hence, an absolute

104 The Neural Lateralization of Planning 4.2

lateralization of dlPFC activity regarding well- and ill-structured planning tasks is not maintainable, but rather a relative lateralization seems probable. The focus of the most specific level of explanations lies on particular sub- processes of planning. Newman et al.(2003) and Kaller, Rahm, Spreer, et al.(2011) both postulated that the left mid-dlPFC is responsible for extracting task-relevant information (i.e., the comparison of information across problem states in order to identify behaviorally relevant differences) and building up (and possibly maintaining) a mental representation of the current problem, whereas the right mid-dlPFC is responsible for the manipulation of information (e.g., resolving interdependencies between move alternatives; Ruh et al., 2012) and, hence, the generation of plans. Morris et al.’s explanation of left-lateralized novelty processing and right-lateralized sequence formation is in line with this explanation and also the findings of Byrd et al.(2011) of a lateralized temporal shift corroborate these explanations for the most part. However, Goel(2010) interpretation of problem structuring and preliminary design (right PFC) as well as design refinement and the detailing phase (left PFC) are contrary to these accounts. In the frameworks of Newman et al.(2003) and Kaller, Rahm, Spreer, et al.(2011), problem structuring would be left-lateralized and especially pronounced at the beginning (cf. Byrd et al., 2011), whereas preliminary design, refinement, and the detailing phase would integrate into the right-lateralized sequence formation (cf. Morris et al., 1997b, 1997a), which is increasingly operative towards the end of the planning phase (cf. Byrd et al., 2011). For example, Goel and Grafman(2000) found the right-lesioned architect to be able to structure the problem, but unable to make the transfer to the actual problem solving. This result is also explainable by the accounts of Newman et al. and Kaller, Rahm, Spreer, et al., with an intact problem representation relying on activity of the left dlPFC and sequence formation being impaired by the lesioned right dlPFC. However, Goel and Grafman (2000) interpreted the results as demonstrating that the preliminary design creation subserved by the right dlPFC was impaired, so that the subsequent steps of refinement and detailing, subserved by the left dlPFC, could not be carried out, hence confirming the assumed critical role of the right dlPFC in ill-structured tasks. This example also serves to illustrate that given the differences in the methods employed (lesion studies,

105 GENERAL DISCUSSION

fMRI, EEG) and in the theoretical specificity – arguing on a more specific or more general level – the models explain the same empirical results with different or even opposed cognitive mechanisms. Therefore, only the different theoretical positions can be pointed out but no overall unified model of the role of the (mid-)dlPFC in human planning on the ToL is extractable. However, theories derived from imaging studies feature a recognizable high commonality.

4.2.3 Limitations of the Meta-analysis on Neural Correlates of Planning

In chapter 2 a meta-analysis on the neural basis of the ToL was presented. Meta- analyses are an important methodology for reliably estimating effects and their effect sizes (see chapter 1.4.1). They have a rich tradition especially in clinical studies (O’Rourke, 2007). Meta-analyses estimate effects more robustly and reliable than single studies but are nevertheless not free of caveats. The major problem of meta- analyses is the publication bias which is a term for the fact that studies that yielded signiﬁcant effects are more likely to be published (Dickersin, 2005; L. Hedges, 1984, 1989). Thus, in total even meta-analyses may overestimate effects. A separate line of research has emerged approaching the publication bias (e.g. Easterbrook, Gopalan, Berlin, & Matthews, 1991; Dickersin, Min, & Meinert, 1992; Dickersin & Min, 1993). For traditional meta-analyses there are several precautions to detect a publication bias, for example the funnel plot (Light & Pillemer, 1984; Light, Singer, & Willett, 1994). In a funnel plot every study is represented by a marker in a graph with the effect size and the sample size (or the standard error) as axes. The graph peaks approximately at the mean effect size and should be symmetrical. Asymmetries indicate a publication bias. With the funnel plot one methodology for detecting biases is available in classical meta-analyses (Rothstein, Sutton, & Borenstein, 2005). Conducting meta-analyses for neuroimaging studies, however, is only a recent attempt which is due to several statistical obstacles that had to be overcome ﬁrst (Eickhoff et al., 2012). With the activation likelihood estimation (ALE) one methodology was introduced that allows a quantita-

106 The Neural Lateralization of Planning 4.2

tive meta-analysis on neuroimaging studies (A. Laird et al., 2005; P. E. Turkeltaub et al., 2002; Wager et al., 2007) and which was utilized in the study reported in chapter 2. Imaging studies suffer most likely from this publication bias as well, as studies are published with a higher probability if an effect was observed for a certain expected brain area (Jennings & Van Horn, 2012), e.g. the dlPFC for the ToL. However, there is no comparable methodology to the funnel plot for imaging studies available yet due to the multi-dimensionality of imaging data (Jennings & Van Horn, 2012). Distortions for the presented meta-analysis have to be assumed but the magnitude of the distortion is not assessable at present. To improve neuroimaging meta-analyses in general the publication bias has to be addressed in the future. As soon as this methodology is available all published results from meta-analyses on neuroimaging studies have to be reviewed for systematical distortions, including the results reported in chapter 2. Nevertheless, present quantitative meta-analyses on imaging studies constitute an improvement compared to individual imaging studies and qualitative reviews. The main aim of chapter 2 was to validate a theory (Goel, 2010) on the hemispheric lateralization within the PFC of human planning. The main result was the falsification of this theory because left and right prefrontal areas of the PFC were found to be involved. This theory was only the most recent approach on assigning specific functions like planning to preferably only one unilateral brain area. The neural lateralization of planning started with development of the ToL. Shallice(1982) not only developed and introduced the ToL in his study but concurrently presented results that only left frontally lesioned patients were impaired in the ToL. If anything, this fact as well as the numerous former theories on lateralization should favor a publication bias in favor of an exclusive left PFC involvement. However, in chapter 2 a bilateral involvement was demonstrated which opposes an expectable publication bias in terms of exclusive left dlPFC involvement. Thus, even with (yet unavailable) corrections for the publication bias, no significant change in results and the main conclusion is expected. Nevertheless, to meet the scientific standards a systematic review still remains necessary. Another limitation of study reported in chapter 2 was that only one component of the theory was tested. Goel’s (2010) theory assumed a left lateralization for well-

107 GENERAL DISCUSSION

structured tasks but also a right lateralization of ill-structured planning tasks. The left lateralization for well-structured planning tasks could not be verified and, thus, the whole theory in its present state is not maintainable. However, concerning the assumed right lateralization of ill-structured tasks no empirical conclusion is derivable yet. Although i) in the light of the presented results (the meta-analysis as well as the literature review of lesion studies) and ii) commonalities of other lateralization theories (see chapters 4.2.1 and 4.2.2) a strict lateralization only for ill-structured tasks seems unlikely, this part of the theory remains to be reviewed, but proves difficult for several reasons. The ToL is the most often employed planning task (Kaller, Rahm, Köstering, & Unterrainer, 2011) and although with 29 studies there is a sufficient number for a meta-analysis, this number is not extensive. Other planning tasks that are by far less frequently employed are rather unlikely to provide sufficient study numbers for a quantitative meta-analysis in the near future, rendering the review for other tasks such as ill-structured tasks impossible. In general, neuroimaging studies on ill-structured tasks hold high heterogeneity and are very rare due to extensive efforts and complexity in analysis leaving this research field yet elusive. Another caveat of the findings and interpretations described in the chapters 2.3, 2.4, 4.2.1, and 4.2.2 is that the mid-dlPFC was found to be involved in a great variety of tasks as a part of the multiple demand network (Duncan, 2010; Duncan & Owen, 2000) instead of being activated solely in planning-related tasks. That is, the very specificity of planning processes that is ascribed to the left and right dlPFC fades in view of their general involvement in complex cognitive tasks. Such as does the concept of highly distinct cortex areas in the face of the sheer infinity of human’s thoughts and behavior (Duncan, 2013). Thus, the precise characteristics of neural processes underlying human planning may have to be abstracted and generalized from specific examples towards domain-independent cognitive processes which are processed by the dlPFC. The exact functional principle of the mid-dlPFC remains an issue for further investigations regarding its specialization in different tasks. In the chapters 2.3, 2.4, 4.2.1, and 4.2.2 the focus was on the mid-dlPFC. However, several other areas were also found to be activated during performing the ToL which was

108 Pupillometry and the Cognitive Framework of Planning 4.3

discussed rather brieﬂy in comparison (see chapters 2.3 and 2.4.3). The functional interactions of all these and additional brain areas are still to be understood in detail.

4.3 Pupillometry and the Cognitive Framework of Planning

4.3.1 Integration of Present Results

In the study reported in chapter 3 pupillometry was used to monitor cognitive sub- processes during planning. As expected from the rich literature on cognitive processes and pupil dilation (e.g. Beatty, 1982b; Beatty & Wagoner, 1978; Kahneman & Beatty, 1967; Prehn et al., 2011; Siegle et al., 2008), a general pupil dilation throughout the planning process was observed in all of the four groups as well as in the control analysis. Thus, pupillometry constitutes a methodology that will allow the future generalization of former cognitive insights (cf. Hodgson et al., 2000; Kaller et al., 2009; Nitschke et al., 2012). As the general increase was found independent of the presentation mode, i) this manipulation can be neglected in the further discussion of the (partially unexpected) results, and ii) this ﬁnding has important implications for the application of pupillometry in ill-structured planning tasks with their uncontrollable and chaotic environments. From the literature there were no direct insights into well-structured planning and pupillometry available. However, there were different expected time courses of pupil dilation derivable from the related pupillometry literature and the sequential model of planning. The expectable effect of sequence generation was relatively unam- biguous derivable from the literature. It was comprehensively shown that cognitive demanding processes lead to dilated pupil (e.g. Beatty & Lucero-Wagoner, 2000; J. L. Bradshaw, 1968; Goldwater, 1972; Payne et al., 1968; Peavler, 1974). Sequence generation is indisputable a cognitively highly demanding sub-phase (Dehaene & Changeux, 1997; Hodgson et al., 2000; Kaller et al., 2009; Nitschke et al., 2012) which should hence lead to an increase of the pupil diameter between the second to last inspection and the very last inspection. However, how cognitively demanding the

109 GENERAL DISCUSSION

representation creation sub-phase is remains yet to be examined (see chapter 1.3.3.2). Subsequently, a priori expectable time courses regarding representation creation are illustrated and substantiated as well as the different implications of them presented. Two different fine-grained time courses of the pupil dilation were expectable based on representation creation being either a cognitively stressful or unstressful process. The first possibility is that cognitive demands during representation creation are negligible due to a rather visual than cognitive process and, thus, during the time course no or only little pupil dilation should be observed between all but the very last inspection (cf. chapter 1.3.3.2). The second possibility is that during the representation creation sub-phase working memory is significantly stressed which was already shown to result in dilated pupils (Ariel & Castel, 2014; Elshtain & Schaefer, 1968; Heitz, Schrock, Payne, & Engle, 2008; Kahnemann & Beatty, 1966; Van Gerven et al., 2004) and which should result in relative consistently increasing pupil sizes during the time course (cf. chapter 1.3.3.2). Contrary to both plausible time courses, a staircase-like pupil dilation pattern was observed with alternating pupil size increases and plateaus. In chapter 3.4 an explanation approach on the yielded pattern was introduced. Unfortunately, the results leave an explanation that is to some degree circular: i) on the one hand, as cognitively demanding processes dilate the pupil, it was expected and to be shown that planning dilated the pupil but ii) on the other hand results revealed alternating (expectable) increases and (unexpectable) plateaus, therefore, different or additional cognitive processes next to the expected ones may take place. That explanation was generated post-hoc to provide a new working model and has to be verified and expanded in future studies to overcome circularity. Corresponding to the two previously described a priori expectable time courses and given the validity of the sequential model of planning with a cognitive undemanding process of representation creation no significant effect of Tower Configuration on the pupil diameter was expected. In contrast, such an effect was found to occur independently of the planning phase or specific inspections, but was dependent of the initially fixated state and resulted in overall increased pupil dilation. In chapter 3.4 an explanation regarding the typical working memory usage is provided. However,

110 Pupillometry and the Cognitive Framework of Planning 4.3

as the effect was unexpected the explanation was created post-hoc and there is an imperative need to replicate and further investigate this effect. The literature of pupillometry is noticeably homogeneous in their reported effects: higher cognitive demands result in higher pupil dilation which adverts robust and rather large effect sizes. Sequence generation is assumed to be a highly cognitively demanding process. Therefore, trials were systematically manipulated in their demands regarding sequence generation. As expected only at the very end of the trials the more demanding problems resulted in higher pupil dilation. However, particularly in the light of the robust pupil dilation effects reported in the literature the observed effect size was rather small. Regarding the application of pupillometry in more general domains as well as in ill-structured planning tasks this issue has to be addressed in the future. A possible explanation is the chosen modeling approach of the pupil time course. A standard analysis approach is the wavelet analysis which potentially boosts effect sizes signiﬁcantly (Daubechies & Laboratories, 1988; Debauchies, 1993; Ogden, 1997). However, in chapter 3 the same statistical approach as Nitschke et al.(2012) was employed to ensure a direct comparability of results. Future studies might address and directly compare both approaches to resolve this discrepancy.

4.3.2 The Potentials and Limitations of Pupillometry as an Indicator for Cognitive Processes

Connections between impairments in the ToL performance and several mental disorders were established, e.g. for schizophrenia (for reviews and meta-analyses, see Greenwood, Wykes, Sigmundsson, Landau, & Morris, 2011; Knapp, Viechtbauer, Leonhart, Nitschke, & Kaller, 2017; Sullivan et al., 2009), Parkinson’s disease (e.g. Altgassen, Phillips, Kopp, & Kliegel, 2007; Culbertson, Moberg, Duda, Stern, & Wein- traub, 2004; Hanes, Andrewes, Smith, & Pantelis, 1996; Hodgson, Tiesman, Owen, & Kennard, 2002; McKinlay et al., 2009; Morris et al., 1988; Owen et al., 1998; Rektorova, Srovnalova, Kubikova, & Prasek, 2008; Weintraub et al., 2005), depression (Arnett, Higginson, & Randolph, 2001; Elliott et al., 1997; Elliott, Sahakian, Michael, Paykel, & Dolan, 1998; Goethals et al., 2005; Purcell, Maruff, Kyrios, & Pantelis, 1997;

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Rektorova et al., 2008; Stordal et al., 2004; Watts, MacLeod, & Morris, 1988), mania (Clark, Iversen, & Goodwin, 2001), attention deficit hyperactivity disorder (Murphy, 2002; Riccio, Wolfe, Romine, Davis, & Sullivan, 2004), Huntington’s disease (Hanes et al., 1996; Lange, Sahakian, Quinn, Marsden, & Robbins, 1995), and obsessive compulsive disorder (den Braber et al., 2008; Cavedini, Cisima, Riboldi, D’Annucci, & Bellodi, 2001; Mataix-Cols et al., 1999; Purcell et al., 1997; Veale, Sahakian, Owen, & Marks, 1996). Especially for Parkinson’s disease (PD) first insights were already obtained. McKinlay et al.(2008) compared effects of Search Depth and Goal Hierarchy (cf. chapter 1.3.2) of the 5-move ToL on accuracy in PD patients and matched healthy controls. The PD patients’ accuracy decreased significantly with higher goal ambiguity, that is, when the necessary move sequence was not derivable from the goal configuration. No differential effects of Search Depth were found, that is, the number of intermediate moves that had to be planned ahead did not affect PD patients and controls differently. McKinlay et al.(2008) concluded that PD patients have no difficulties to follow a chain of thoughts, thus, cognitive stability remains intact whereas many degrees of freedom have a negative impact, thus, cognitive flexibility in PD patients is impaired (see also Köstering, McKinlay, Stahl, & Kaller, 2012). Hodgson et al.(2002) compared the gaze fixation patterns of PD patients with healthy controls in the ToL. The problem difficulties were manipulated via minimum number of moves. Healthy controls showed prolonged fixations of the start state with increasing difficulty, presumably indicating the mental processing of different solution approaches (Hodgson et al., 2002). In contrast, PD patients showed no prolongation in any fixation but continuing gaze shifts between start and goal state. Hodgson et al.(2002) inferred that PD patients have difficulties in maintaining current goals, hence contradicting the interpretations of McKinlay et al.(2008) who found cognitive stability unimpaired. However, the approaches on difficulty of the ToL differed widely and, thus, did the amount of different sub-processes. A study that combined both – manipulating structural parameters for different minimum number of moves – would be an instructive endeavor for the future. In a first eye tracking study in patients suffering from schizophrenia and schizoaf- fective disorder aberrant gaze patterns were observed (Huddy et al., 2007). Patients

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showed prolonged fixations on both start and goal state which was interpreted as working memory impairments (Huddy et al., 2007). However, more elaborated studies are necessary to further dissect these patterns in schizophrenia. In general, a combination of manipulating structural parameters (cf. McKinlay et al., 2008) and eye tracking (cf. Hodgson et al., 2002) similar to Kaller et al.(2009) and Nitschke et al.(2012) as well as pupillometry (cf. chapter 3) constitutes a promising approach on expanding the understanding of PD, schizophrenia but also other mental diseases that were linked to planning impairments. It is worthwhile to deepen our understanding regarding the diseases themselves and the struggles patients suffer from on a daily basis to eventually provide strategies to enhance their quality of life. A general rather than a study-specific limitation concerns the sequential model of planning. It was sufficiently shown to be valid in rather simple planning tasks such as the 3-move ToL. However, the degree of its applicability and its validity in more demanding well-structured planning tasks (e.g. 5-move ToL) as well as in ill-structured tasks (e.g. financial planning task, Goel et al., 1997, or travel planning task, Goel et al., 2013) is still an open question. But even in simple well-structured planning tasks there are cognitive components and processes to consider in the future to provide a more general model (e.g. working memory, inhibitory control, flexibility, individual differences, Diamond, 2013; Hammond, 1990; Köstering et al., 2012). Taken together, the sequential model of planning could not be verified unre- servedly by pupillometry. However, in view of the presented results of generally increasing pupil diameters and the confirmed sequence generation effect, its validity remains unchallenged. The dilation of pupils is dependent of numerous factors (see chapters 1.3.4 and 1.4.3), hence, another yet unidentified dilation-inducing process might be involved that caused the unexpected effects. This will have to be addressed in future studies. Despite all caveats, a first important step was accomplished towards establishing pupillometry as a methodology suitable to identify specific latent processes as well as establishing its future potential for planning research.

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4.3.3 A Critique of a Sequential Model of Planning

Planning is considered one of the highest and most complex human abilities (see chapter 1.1). To overcome this complexity, previous research broke this extensive construct down to more concrete components that were approached via several cognitive architectures and models (see chapter 1.3.3). One example – that was especially relevant for the present thesis – is the sequential model of planning. It assumes two sub-phases of planning, the representation creation and the sequence generation. Hodgson et al.(2000), Kaller et al.(2009), Nitschke et al.(2012) as well as the study reported in chapter 3 reported evidence for the validity of a sequential course of planning sub-phases. However, in this section a major criticism on the sequential model and how it was derived is illustrated. In a general criticism of the methodology of eye movements studies Viviani (1990) stated a point that is vital for the present thesis regarding the inevitability of sequentiality of derivations from gaze fixations. All gaze fixations of the eye are at any point in time on exactly one spot in the world and are, therefore, always strictly sequential: The eye can only fixate one thing. Hence, all evidence and theories derived from gaze fixation can also only be strictly sequential as well (Viviani, 1990). Thus, potential parallelisms of cognitive processes cannot be detected by eye movements (Viviani, 1990). The sequential model for planning for the ToL in this thesis was based on the eye movement studies of Hodgson et al.(2000), Kaller et al.(2009), Nitschke et al.(2012), which all suffer from this constraint, and, therefore, has to be reviewed critically. In chapter 3, a study employing pupillometry was presented. Seen individually, the criticism regarding sequentiality concerns pupillometry as well, as the pupil size can also have only one state at any given time and, thus, can concurrently indicate only one cognitive state at any given time. This state might indicate one predominant cognitive process but it could also be an amalgam composed of several parallel processes that influence the pupil size simultaneously which is plausible given the vast list of factors that influence the pupil’s dilation (cf. chapter 1.4.3). Thus, Viviani’s (1990) critique equally concerns gaze fixations and pupillometry.

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The combination of gaze fixations and pupillometry constitutes a potential approach to overcome this valid criticism of Viviani(1990). Even combined both methodologies still suffer from strict sequentiality. However, if both methodologies would indicate different sub-processes the combination would be able to indicate two processes simultaneously. Therefor two requirements have to be met: i) gaze fixations and pupil dilation have to be mostly independent from each other, i.e. a genuine second independent dimension is added to gaze fixations by pupillometry, and ii) pupillometry has to be influenced by only one of the cognitive processes (otherwise no distinct inference is possible). The first requirement i) can be considered met as the gaze can move without the pupil dilation have to change. This was verified in chapter 4.3.1 as the gaze fixations durations remained relatively stable (cf. Kaller et al., 2009; Nitschke et al., 2012) whereas the pupils dilated throughout the planning. In the light of the results reported in chapter 3, however, the second requirement ii) was not verified. As in chapter 4.3.1 illustrated, one expected course of pupil dilation was no pupil size change at the start of the planning during representation creation but an exclusive increase at the end during sequence generation. If this course would have been found, first evidence for the pupillometry as additional dimension besides the gaze fixation would have been provided. However, the increase during sequence generation was not exclusive as increases during representation creation were also found. Thus, the results reported in chapter 3 show the value of pupillometry for future studies on the cognitive sub-processes of planning. However, pupillometry is not sufficient to overcome the critique regarding sequentiality. As sequentiality is an insurmountable problem in eye movement and pupillometry methodologies, other methodologies are required to substantiate the validity of a sequential model of planning. First evidence is available from neuroimaging studies on the ToL planning task. Kaller, Rahm, Spreer, et al.(2011) found Tower Configuration associates with solely the left dlPFC whereas sequence generation was associates with the right dlPFC (for more details, see chapter 4.2.1), hence, the sub-processes of planning representation creation and sequence generation can be localized within the brain. Neuroimaging methodologies, such as EEG and fMRI, have the advantage that they measure the different parts of the brain simultaneously. Therefore, they do not

115 GENERAL DISCUSSION

suffer from the caveats regarding sequentiality and would be suitable to validate a sequential model of planning. In an EEG study, Byrd et al.(2011) found an early activation of the left dlPFC and a later activation of the right dlPFC. Similarly, Ruh et al.(2012) employed fMRI and reported a former left dlPFC and a subsequent right dlPFC activation (for more details on both studies, see chapter 4.2.1). To sum up, there is non-confounded evidence available on the validity of assuming that the two sub-processes representation creation and sequence generation occur in a sequential fashion in well-structured planning tasks. A caveat of a sequential model of planning might be that it is a very simplistic model of a very complex process and, therefore, might be limited to easy well- structured tasks. The presented sequential model of planning separates the two sub- processes temporarily strictly. A temporarily rather relative shift seems also plausible. For example, at the start mostly processes for creating the representation occur that are already intertwined with single sequence generation actions which allow to efficiently recognize crucial configurations. In contrast, at the very end the sequence generation process dominates. Dead ends in a generated sequence require to recall former stored states from the working memory. As the memory decays and suffers from interference from similar memorized states (e.g. Ballard et al., 1997), an entire discard of the present stored sequence and a partial representation creation process through associative memory activation is thinkable during sequence generation (Altmann & Trafton, 2002). The generalization of this model to more difficult tasks (e.g. ToL with more moves of more balls) as well as more complex tasks (e.g. ill-structured tasks) will be a goal for future research.

4.4 Outlook on Linking the Cognitive and Neural Foun- dations of Planning

A meta-analysis on the neural basis of human planning was presented in chapter 2. Chapter 3 introduced pupillometry as one methodology to gain insights into cognitive sub-processes of planning that are otherwise not directly assessable. Consequentially,

116 Outlook on Linking the Cognitive and Neural Foundations of Planning 4.4

the next step would be the combination of both as yet relatively separated research fields. First study results employing this approach are already available. Kaller, Rahm, Spreer, et al.(2011) manipulated the structural parameters Search Depth and Tower Configuration of the 3-move ToL and investigated the resulting neural patterns within the dlPFC employing fMRI. They found a double dissociation where the left dlPFC was linked to the representation creation phase whereas the the right dlPFC was linked to the sequence generation phase (see chapter 4.2.1 for a more detailed description). According to a sequential model of planning the representation creation phase should occur before the sequence generation phase. In a re-analysis of the data, Ruh et al.(2012) found a temporal shift between left and right dlPFC activation: left dlPFC was activated earlier whereas the right dlPFC was activated later during the planning period, thus further substantiating the validity of the sequential model. However, fMRI has a poor temporal resolution and is hence unsuitable for fine-grained temporal analyses given that average planning times in 3-move ToL are below three seconds (e.g. Nitschke et al., 2012) and even a fast fMRI repetition time (TR) of 1.5 seconds results in only two measurement points per trial in most cases (Kaller, Rahm, Spreer, et al., 2011). Therefore, other imaging techniques are more suitable. As in chapter 3 it was interpreted the cortical brain area mid-dlPFC is key during planning, thus, surface measuring techniques are suffice. Electroencephalography (EEG) is the most common cortical measuring technique that has an unparalleled temporal resolution. As described in detail in chapter 4.2.1 Byrd et al.(2011) conducted a temporal analysis of the planning period in the ToL employing EEG. They found three separate phases instead of the two expected by the sequential model, an initial left dlPFC activation, a subsequent right dlPFC activation and a final left dlPFC activation. Likewise, Hodgson et al.(2000) found three phases in eye movement pattern analysis which is attributable to their variant of the ToL that required no solution execution (for a detailed description see chapter 1.3.3.2). For that reason after the hypothesized representation creation and sequence generation an additional verification phase took place. Byrd et al.’s (2011) design also required no actual solution execution. Thus, the first two phases found by Byrd et al.(2011) align with the initial left and the subsequent right activation of Ruh et al.(2012).

117 GENERAL DISCUSSION

However, the differences between the two or three phases found in different studies must not only be addressed argumentatively but also evidence-based in the future. The ramifications for the comparability of studies that are considered to be equal at present are vast and accordingly theories in both neural and cognitive domains will have to be reviewed. Although EEG and fMRI both measure the cortical activity there are certain caveats to their comparability (Laufs et al., 2003; Grova et al., 2008). Another cortical methodology is functional near-infrared spectroscopy (fNIRS) that emits harmless infrared light from optodes on the participants’ heads and measures the reflected light (for a review see Irani, Platek, Bunce, Ruocco, & Chute, 2007). The temporal resolution is significantly slower than EEG but with 10 to 100 Hz abundant for neural temporal analyses (Irani et al., 2007). Like fMRI fNIRS measures the blood-oxygen-level dependent (BOLD) response and, therefore, constitutes an intermediate methodology between fMRI and EEG that potentially will be helpful to clarify differences in results between both methods, e.g. the discrepancy between two against three phases of planning. In the study reported in chapter 2 the rostrolateral PFC (rlPFC) was also established to be activated during planning across studies, although its involvement remained inconclusive and its role is neglected in most planning studies. However, most included studies employed fMRI which has strict limitation for the measurement of rostrolateral areas due to their proximity to the air-filled sinuses (Wilson et al., 2002). Therefore, the role of the rlPFC for planning in the literature might be under- estimated. fNIRS does not suffer from these limitations. An important line of research in the future will be the role of the rlPFC which has an auspicious methodological approach available by combining fNIRS, eye-tracking and pupillometry.

Additionally to the mere understanding of the connections between neural and cognitive processes the potential for neural and cognitive enhancements or therapeutic approaches for neurological and mental diseases holds a lot of promise. One promising methodology is transcranial magnetic stimulation (TMS) (Lisanby, Kinnunen, &

118 Outlook on Linking the Cognitive and Neural Foundations of Planning 4.4

Crupain, 2002; Wassermann & Lisanby, 2001). In TMS an electric coil that produces a strong magnetic field which interferes with cortical currents is applied to a surface area of the brain by applying it to the subject’s skull. TMS can be used to increase or decrease the excitability of a stimulated brain area depending of the applied frequency (Chen, 2000; Pascual-Leone et al., 1998; Speer et al., 2000). Kaller et al.(2013) employed continuous theta-burst-stimulation (cTBS) above left and right dlPFC as an inhibitory TMS protocol on healthy subjects while they performed easy 3-move ToL problems. Subjects stimulated with inhibitory TMS over the left dlPFC showed decreased planning times whereas subjects with TMS applied over the right dlPFC showed increased planning times. Kaller et al.(2013) also manipulated Search Depth and Tower Configuration but observed no specific effects for both. Srovnalova, Marecek, Kubikova, and Rektorova(2012) provided first insights into the potential of stimulation in pathological groups that were mostly in line with Kaller et al.(2013). They employed excitatory repetitive transcranial magnetic stimulation (rTMS) either over left or right dlPFC in PD patients. They found decreased planning times for the right stimulated group. Only a descriptive but statistically not significant decrease was observed for the left stimulated group. Van den Heuvel et al. (2013) employed inhibitory rTMS exclusively over the left dlPFC of subjects that performed ToL problems with increasing minimum number of moves. RTMS over the left dlPFC lead to increasing error rates for the most difficult ToL problems with five moves. The results of Kaller et al.(2013) and Srovnalova et al.(2012) seemingly contradict the results of van den Heuvel et al.(2013). However, the studies are only comparable with certain reservations due to their heterogeneity. Van den Heuvel et al. (2013) only tested six subjects per group and Srovnalova et al. (2012) only five per group whereas Kaller et al.(2013) tested 26 per group, which constitutes an enormous power difference between the employed designs. All studies used different ToL problems and different types of difficulty. Moreover, the employed stimulation protocols (rTMS vs. cTMS as well as inhibitory vs. excitatory) may differ massively in their functionality and effectiveness. It is plausible that stimulation is only beneficial to a certain degree (e.g. in frequency, amplitude, stimulation period) before

119 GENERAL DISCUSSION

becoming disadvantageous which might explain seemingly contradicting effects that are, however, just on different flanks of a u-shaped function. At this point, the number of studies are too sparse and their designs are too heterogeneous to draw a robust conclusion and there are even studies that found no stimulation effects at all (e.g. Kim, Kim, Chun, Yi, & Kwon, 2010). Beside an increasing number of studies the results especially from chapter 2 might be helpful. Thus far stimulation was always applied somewhere over the dlPFC which is broadly accepted as a key region for planning. However, van den Heuvel et al.(2013) only applied rTMS to the left dlPFC following a theory that neglected the role of the right dlPFC that was established in chapter 2. As Srovnalova et al.(2012) and Kaller et al. (2013) already noted, future studies on the subject should always include the dlPFC bilaterally. Additionally, although TMS is locally rather imprecise, the dlPFC is a large brain area (cf. Fig. 1.2) and future studies might benefit from the finding in chapter 2 that the mid-dlPFC (BA46) is the key area rather than the whole dlPFC. Therefore, studies with stimulation over varying sub-areas of the dlPFC might be of different effectiveness, which also constitutes an explanation why studies observed varying stimulation effects. Future studies should shift the focus of the stimulation area to the mid-dlPFC to ensure comparability and to maximize effectiveness. The approach of a temporary disruption of a brain areas by TMS benefits from the advantages of lesion studies, i.e. the specific impairment of a component of a system and the observation of the direct consequences. Simultaneously, TMS lesions remain locally circumscribed and, hence do not suffer from traditional lesion study disadvantages, i.e. the locally wide-spread and widely confounded lesions from head injuries or strokes examined in most lesion studies. Thus, TMS opens the chance to further understand the interactions between other surface brain areas and specific sub-processes. Another cortical stimulating methodology is transcranial direct-current stimulation (tDCS). TDCS uses electrodes that are attached to the skull over the surface brain areas which shall be stimulated by delivering low currents. There is one anodal stimulation electrode which increases excitability and cathodal stimulation electrode which decreases excitability usually mounted on opposing brain areas. The results

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about tDCS are listed separately from the TMS ﬁndings because there are several caveats regarding their comparability (Stagg, Wylezinska, et al., 2009; Stagg, Best, et al., 2009; Ziemann et al., 2008). TDCS is much less common than TMS and there are only few studies employing it as yet and only two in association with planning. Dockery, Hueckel-Weng, Birbaumer, and Plewnia(2009) applied tDCS above the left and right dlPFC but observed no direct planning effects in the ToL. Different learning effects across multiple sessions were observed dependent on the stimulation. However, to what extent that still indicates planning effects rather than only applying learned strategies remains disputable. Heinze et al.(2014) manipulated the structural parameters of the ToL while measuring eye movement data and applying tDCS above both dlPFC areas. An isolated effect of a shortened sequence generation phase for right anodal stimulation was reported, thus emphasizing the right dlPFC allocation for the sequence generation phase by Kaller, Rahm, Spreer, et al.(2011). However, no effect for the representation creation phase and a left anodal stimulation was found that would be expectable from Kaller, Rahm, Spreer, et al.(2011).

4.5 Conclusion

The present thesis presented one step towards a better understanding of human planning. However, for a full comprehension there is still much to investigate. In the previous paragraphs neural measurement methods, that allow to distinct temporal sequences, and neural stimulation methods, that allow to speciﬁcally disrupt brain areas and linked cognitive processes, were introduced. The combination of the neural (e.g. EEG, fNIRS, TMS, tDCS) and cognitive (e.g. eye movement measurement, pupillometry) methodologies constitutes the next most promising steps to gain comprehensive insights into the cognitive and neural foundations of planning.

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6 Appendix

145 APPENDIX

Dieser Teil enthält persönliche Daten und ist deshalb nicht Bestandteil der Veröffentlichung.

146 Curriculum Vitae 6

147 APPENDIX

148 Publication record 6

Publication record and scientiﬁc work

Journal articles

Nitschke, K., Martin, M., Weiller, C., Willmes, K., & Kaller, C. P. (forthcoming). The NIX-Tool: Assessing non-parametric interaction effects in voxel-based lesion symptom mapping analyses. Nitschke, K., Rahm, B., Köstering, L., Weiller, C., & Kaller, C. P. (submitted). Dissoci- ating task-demand-specific differences in cognitive processing during planning and problem solving: A validation approach using pupillometry. Frontiers in Psychology. Knapp, F., Viechtbauer, W., Leonhart, R., Nitschke, K., & Kaller, C. P. (accepted). Plan- ning performance in schizophrenia patients: A meta-analysis of the influence of task difficulty and various clinical and sociodemographic variables. Psychological Medicine. Nitschke, K., Köstering, L., Finkel, L., Weiller, C. & Kaller, C. P. (2017). Activation likelihood estimation of functional brain imaging results in the Tower of London task: A meta-analysis on the neural basis of planning and the role of the mid- dorsolateral prefrontal cortex. Human Brain Mapping, 38(1), 396-413. Asbrand, J., Blechert, J., Nitschke, K., Tuschen-Caffier, B., & Schmitz, J. (2016). Aroused at home: basic autonomic regulation during orthostatic and physical activation is altered in children with social anxiety disorder. Journal of Abnormal Child Psychology, 1-13. Dressing, A., Nitschke, K., Kümmerer, D., Bormann, T., Beume, L., Schmidt, C. S. M., ... Martin, M. (2016). Distinct contributions of dorsal and ventral streams to imitation of tool-use and communicative gestures. Cerebral Cortex, 1-19. Martin, M., Nitschke, K., Beume, L., Dressing, A., Bühler, L. E., Ludwig, V. M., ... Weiller, C. (2016). Brain activity underlying tool-related and imitative skills after major left hemisphere stroke. Brain, 139, 1497-1516. Umarova, R. M., Nitschke, K., Kaller, C. P., Klöppel, S., Beume, L., Mader, I., ... Weiller, C. (2016). Predictors and signatures of recovery from neglect in acute stroke. Annals of Neurology, 79(4), 673–686. Köstering, L., Nitschke, K., Schumacher, F. K., Weiller, C., & Kaller, C. P. (2015). Test- retest reliability of the Tower of London planning task (TOL-F). Psychological Assessment, 27(3), 925-931. Heinze, K., Ruh, N., Nitschke, K., Reis, J., Fritsch, B., Unterrainer, J. M., ... Kaller, C. P. (2014). Transcranial direct current stimulation over left and right DLPFC: Lat- eralized effects on planning performance and related eye movements. Biological Psychology, 102, 130-140. Nitschke, K., Ruh, N., Kappler, S., Stahl, C., & Kaller, C. P. (2012). Dissociable stages of problem solving (I): Temporal characteristics revealed by eye-movement analyses. Brain and Cognition, 80(1), 160-169.

149 APPENDIX

Scientiﬁc Talks

Determinants of Post-Stroke Outcome: Insights from Voxel-Based Lesion Symptom Mapping (Annual Brain Links Brain Tools Meeting, 2016)

Congress Contributions

Testing Dissociations in Lesion-Symptom Mapping: A Tool for Non-Parametric Interac- tion Effects (NIX) (Human Brain Mapping, 2016) Linking Inter-Individual Differences in Verbal Fluency Performance to the Hierarchical Organization in Prefrontal Cortex (Deutsche Gesellschaft für Neurologie, 2014) A Quantitative Meta-Analysis on Functional Imaging Results in the Tower of London Task (Human Brain Mapping, 2014) Predicting Inter-Individual Differences in Verbal Fluency Performance from Rostro- Caudal Directed Interactions in Prefrontal Cortex (Psychologie und Gehirn, 2014) The Neural Basis of Planning - A Meta-Analysis with Activation Likelihood Estimation (ALE) of Functional Imaging Results (Deutsche Gesellschaft für Psychiatrie und Psychotherapie, Psychosomatik und Nervenheilkunde, 2013) A Quantitative Meta-Analysis on the Neural Basis of Planning - Activation Likelihood Estimation (ALE) of Functional Imaging Results in the Tower of London Task (Psychologie und Gehirn, 2013) Planungsleistung in komplexen kognitiven Aufgaben: Blickbewegungsmuster im Tower of London (Deutsche Gesellschaft für Neurologie, 2012) Augenbewegungen und visuell-räumliche Planungsleistungen: Evidenz für unter- schiedliche Phasen kognitiver Verarbeitung (Gehirn und Psychologie, 2010)

150