Creativity Through the Eyes Arousal and the Prediction of Creative Task

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Creativity through the eyes

Arousal and the prediction of creative task performance by Locus Coeruleus-norepinephrine

modes.

Matthijs Dekker U1262091 / 991987

Master thesis Communication- and Information Sciences Corporate communication and digital media

Faculty Humanities Tilburg University

Supervisor: dr. K.A. de Rooij Second reader: H.K. Schraffenberger BEng MsC

January, 2017 CREATIVITY THROUGH THE EYES 2

Abstract In order to develop creative ideas, which are original as well as effective, several cognitive processes are required involving divergent and convergent thinking. Previous research suggested that creative task performance is influenced the release of noradrenaline, causing arousal, in the Locus Coeruleus-norepinephrine system (LC-NE). This LC-NE system mediates the changes between exploitation and exploratory control states. Previous studies suggested that activity in the LC-NE system is indicated by pupil diameter. Tonic pupil sizes are associated with an exploratory control state, whereas phasic pupil are associated with an exploitative control state. This study measured the pupil diameter of participants while they performed a creative task. Different phases in the experiment are characterized by divergent and convergent thinking. It is examined whether tonic and phasic pupil sizes can predict the creativity of generated ideas during divergent and convergent thinking. It was found that (i) phasic pupil sizes are linear and quadratic predictors of effectiveness during divergent thinking; (ii) tonic pupil sizes are linear predictors of originality during divergent thinking. These findings suggest that creativity can be predicted during divergent thinking in a creative process.

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Creativity through the eyes Creativity among individuals and cultures can lead to happier, fuller and healthier lives (Richards, 2010). It benefits the adaption to an unpredictable future, and supports shaping this future to be beneficial. For example, creativity is highly important in today’s world with constant changes and hypercompetitive markets (Kaufman & Sternberg, 2010; Harvey & Novicevic, 2002). Creative task performance strengthens innovative performance, problem solving capabilities and is required to avoid failure (Kotler, Armstrong, Saunders, & Wong, 2014; Proctor, 1991; Damle, 2015). Currently there is a limited understanding of the factors that enable creative thought. Therefore, this study aims to provide an understanding about factors that predict creative task performance during the creative process. In order to be creative, both originality and effectiveness are required. Namely, something which is original can be useless if it is not effective (Runco & Jaeger, 2012; Stein, 1953). Creative task performance, the degree to which individuals develop ideas that are both original and effective, involves cognitive processes interacting in a dynamic fashion (Isaksen, Dorval, & Treffinger, 2011; Mumford, Madeiros, & Partlow, 2012). These processes involve variation in divergent and convergent thinking, organized in the creative problem solving framework by Isaksen et al. (2011) and Mumford et al. (2012). Divergent thinking generates ideas by combining disparate forms of information in new ways, while convergent thinking generates one single solution to a problem (Addis, Pan, Musicaro, & Schacter, 2016). Divergent thinking leads to original ideas, but in order to become effective, convergent thinking is necessary to analyze and evaluate these original ideas (Baas & van der Maas, 2015). Creative task performance during divergent and convergent thinking is influenced by arousal. Arousal can be defined as vigilance and alertness while being awake (Carter, de Lecea, & Adamantidis, 2013). An inverted U-shape relationship between arousal and creative task performance reveals that very low or extremely high levels of arousal prohibit people from efficient creative activities, whereas moderate levels of arousal enhance creativity (Kim, 2006; de Dreu, Baas, & Nijstad, 2008; Seo, Bartunek, & Barrett, 2010). Arousal is caused by the release of noradrenaline and dopamine in the cortical circuitry due to a link between working memory function and the Locus Coeruleus-norepinephrine system (LC-NE) (Dreisbach & Goschke, 2004). During periods of extremely high arousal, attentional selection becomes so restricted that particular cues cannot be processed, while periods of very low arousal lead to drowsiness and inattention (Gilzenrat, Nieuwenhuis, Jepma, & Cohen, 2010). CREATIVITY THROUGH THE EYES 4

Instead, focusing on the LC-NE modes during different control states can identify the multifaceted effects of arousal on creative performance (Gilzenrat et al., 2010). Recent studies found evidence that LC-NE activity mediates changes between exploitation and exploratory control states (Jepma & Nieuwenhuis, 2011; Gilzenrat et al., 2010; Hayes & Petrov, 2016). An exploitation control state is accompanied by enhanced performance and focus on a current task, whereas an exploratory control state enhances engagement on non-task related information. The adaptive gain theory implies that an exploitation state is accompanied by the phasic mode of the LC-NE, whereas an exploratory control state is accompanied by the tonic mode of the LC-NE (Jepma & Nieuwenhuis, 2011; Gilzenrat et al., 2010; Hayes & Petrov, 2016; Aston-Jones & Cohen, 2005). A tonic LC-NE mode is characterized by an increased baseline pupil diameter and enhanced engagement on non-task related information (i.e. exploration). Conversely, a phasic LC-NE mode is characterized by a decreased baseline pupil diameter and enhanced engagement in the current task (i.e. exploitation). Despite these findings, no studies examined whether creative performance could be predicted based on LC-NE modes via pupil diameters. It could be assumed that phasic and tonic LC-NE modes can provide a new perspective in the role of arousal during creative thought. An exploratory control state, predicted by a tonic LC-NE mode, contributes to divergent thinking by disengaging from the current task and increasing cognitive flexibility (Jepma & Nieuwenhuis, 2008). Evaluating and planning original ideas is a convergent process in which individuals convert several original ideas into one effective plan. An exploitation control state, predicted by a phasic LC-NE mode, contributes to convergent thinking by focusing and increasing performance on the current task. Preliminary research showed that reduced activity in the LC-NE predicted divergent thinking, whereas increased activity in the LC-NE predicted convergent thinking (Heilman, 2016; Heilman, Nadeau, & Beversdorf, 2003). However, to the knowledge of this author no previous studies have shown whether LC- NE modes, monitored by pupil diameters can predict creative task performance. It is assumed that changes in the LC-NE modes predict the creative task performance of an individual. Consequently, the following research question underlines this study:

“Do phasic and tonic LC-NE modes predict creative task performance during divergent and convergent phases in the creative process in different ways?” CREATIVITY THROUGH THE EYES 5

This research is deemed scientifically relevant for the following reasons. First, this research seeks to fill the gap of knowledge between the synergy of the LC-NE and creative task performance. To date, there has been research on examining and identifying individuals’ creative task performance (Mumford et al., 2012; Kuncel, Hezlett, & Ones, 2004; Baas, Roskes, Sligte, Nijstad, & de Dreu, 2013). It is revealed that LC-NE activity is related to the diameter of an individual’s pupils. None of these studies examined the prediction of tonic and phasic LC-NE modes on divergent and convergent phases in the creative process via pupil diameters. This study aims at providing evidence for the involvement of LC-NE modes on creative task performance as well as introducing an additional measure for creative task performance. Second, this study is conducted within a context of high external validity. As the contribution to society is an important aspect of creativity, this study is based on a realistic corporate issue. Therefore, the practical effectiveness of the solutions is addressed in this study. This research also has societal relevance as creative task performance is highly required in hypercompetitive markets (Kaufman & Sternberg, 2010; Harvey and Novicevic, 2002). Therefore, it is important to understand creativity and how it can be strengthened and assessed. By understanding the link between LC-NE activity and creativity, people can be trained to utilize creative task performance in order to increase competitiveness and become more valuable for organizations. In addition, organizations are already assessing potential candidates on their creative task performance (Oldham & Cummings, 1996). This study aims to assess creativity more effectively and can therefore be useful to organizations in order to find employees who can contribute to the competitiveness, problem solving capabilities and growth (Kotler et al., 2009). This thesis is structured in the following way. The theoretical framework provides an in-depth exploration of the key concepts ‘creativity’, and the ‘creative process’. Furthermore, literature regarding ‘exploration and exploitation control states’ and the relation with ‘phasic and tonic modes’ will be explored and applied. The third chapter explains the method for this research in terms of chosen samples, experimental design, materials and procedure. The fourth chapter will statistically present the results and subsequently the hypotheses will be confirmed or rejected. The fifth chapter concludes the findings and answers the research question. Furthermore, it discusses the theoretical and practical implications of this research and provides suggestions for further research.

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1 Theoretical framework 1.1 Creativity In order to define creativity, a bipartite definition is required. The first part of the definition involves originality referring to the uncommonness and innovativeness of an idea (Baas & van der Maas, 2008). An uncommon and innovative idea can be based on existing knowledge and information, but the final idea should contain new elements compared to a previous idea (Stein, 1953). However, an original idea can be useless, as there is the possibility that nobody ever introduced it because it did not contribute to society. Therefore, a second part is included in the definition. Balkin (1990) stresses that the contribution to society is what differentiates creativity from merely originality. In order to contribute, a creative idea should be practically useful, fit the context and be appropriate (Runco & Jaeger, 2012). Stein (1953) highlights the importance of time and people in terms of society. In today’s world with constant changes and hypercompetitive markets, yesterday’s creative ideas can be ineffective today (Harvey & Novicevic, 2002; Kaufman & Sternberg, 2010). Furthermore, these ideas should be considered useful or satisfying by a group of people and the benefits should outweigh the costs (Balkin, 1990; Rubenson & Runco, 1992; Stein, 1953). This contribution to society is referred to as effectiveness, which leaves the bipartite definition as a combination of originality and effectiveness (Montag, Maertz, & Baer, 2012; Runco & Jaeger, 2012).

1.2 Creative process The creative process is strengthened by a combination of divergent and convergent thinking (Jones & Estes, 2015). Both types of thinking contribute differently to the process of creative thought. Whereas divergent thinking focuses on generating original ideas by combining disparate forms of information in new ways, convergent thinking narrows several original ideas into one optimal creative idea (Addis et al., 2016; Jones & Estes, 2015). This entire process of creative thought contains several phases which are each related to divergent and convergent thinking. The framework showing the several stages within the process of creative problem solving are displayed in Figure 1 (Isaksen et al., 2011, p. 31) created a framework showing the several stages within the process of creative problem solving (Figure 1).

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Figure 1. Creative problem solving framework. Reprinted from Creative approaches to problem solving: A framework for innovation and change (p. 31), by S.G. Isaksen, K.B. Dorval, and D.J. Treffinger, 2011, London, England: SAGE publications. Copyright 2011 by SAGE publications, Inc.

This framework is based on three process components and one management component, each consisting of different phases which are related to divergent or convergent thinking (Isaksen et al., 2011). The first component, “understanding the challenge”, involves the preparation and stating of the problem. The phases included in this component are constructing opportunities, exploring data, and framing problems. These phases assist in developing a clear definition of the problem. The second component, “generating ideas”, involves generating many original ideas to solve a problem. Only one phase is included in this component with a similar name. The third component, “preparing for action”, involves the convergence of the many original ideas into an effective and creative idea which can solve the problem. This component consists of the phases developing solutions and building acceptance. Figure 2 gives an overview of the components addressed in this study, belonging to the divergent and convergent phases. The width of the graph shows that the divergent phases lead to a high number of original ideas, whereas the convergent phases lead to a small number of creative ideas. The “planning your approach” component is referred to as a management component which involves keeping track of thinking processes during the creative problem solving process. Furthermore, choices about tools and locations are involved in this component.

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Figure 2. Divergent to convergent thinking

The framework by Isaksen et al. (2011) is constructed in a dynamic fashion which allows for cycling back to previous phases in cases of failure and dissatisfaction. For example, when the original ideas generated with divergent thinking prove insufficient to construct a creative idea, more information and knowledge can be gathered. This dynamic process enhances the creativity of ideas by providing a greater range of options (Connelly et al., 2000; Mumford, Baughman, Threlfall, Supinski and Costanza, 1996). The number of times cycling back can differ per individual, as the strength of convergent and divergent thinking differs per person. An individual’s ability to perform a creative task depends the behavior and cognitive skills during the execution of the different phases of the creative process. Cognitive flexibility, extraversion, openness to new experiences and the ability to disengage from a current task predict an individual’s divergent thinking abilities (Baas et al., 2008; de Dreu, Nijstad, Baas, Wolsink, & Roskes, 2012; Kuncel et al., 2004). In contrast, self-confidence, planning, perseverance and the ability to focus on a current task predict an individual’s convergent thinking abilities (Balkin, 1990; de Dreu et al., 2012; Kuncel et al., 2004). Balkin (1990) stresses that the coherence of these skills and characteristics determine the strength of creative task performance. For example, intelligence is necessary in order be creative, however, solely intelligence does not make an individual creative (Karwowski et al., 2016). Creative people are able at making connections were these connections did not exist previously (Balkin, 1990). In short, each phase requires certain cognitive skills and behavior in order to perform effectively. The combination of these phases, associated cognitive skills, and personal characteristics contribute to the emergence of creative ideas.

1.3 Arousal Some of the cognitive skills required in a creative process (e.g. working memory capacity, comprehension, planning) are associated with arousal (de Dreu et al., 2008; Kim, 2006; Seo et CREATIVITY THROUGH THE EYES 9 al., 2010). Arousal is defined as the degree of vigilance and alertness during periods of wakefulness (Carter et al., 2013). Wakefulness is referred to as a conscious state in which individuals can perceive and interact with their environment. The cue utilization hypothesis states that increased levels of arousal lead to enhanced selection and processing task-relevant information. For example, Martindale and Greenough (1973) found a significant relation between high arousal and creative performance, whereas low and medium levels of arousal showed non-significant results. This study was conducted via a Remote Associates Test (RAT) which focuses on convergent thinking abilities. Similar findings were retrieved via an Alternate Uses Task (AUT) which focuses on divergent thinking abilities (Dreu et al., 2008). The effect of arousal on these cognitive processes can be explained by the release of noradrenaline and dopamine in the cortical circuitry, and in particular a connection between the working memory function and the Locus Coeruleus-Norepinephrine (LC-NE) (Dreisbach & Goschke, 2004). The LC-NE system controls the cognitive processes moderating the level of attention and behavioral performance of an individual on a current task (Sara, 2009). Furthermore, it is the only source of norepinephrine, a neurotransmitter modulating arousal, in the cortex (Heilman, 2016; Howells, Stein, & Russell, 2012) Seo et al. (2010) found that increased levels of arousal enhance focus and attention on a current task. This is partially supported by Yerkes and Dodson (1908) who revealed an inverted U-shape relationship concerning arousal and creative task performance (Figure 3). The study by de Dreu et al. (2008) supported this relationship by conducting a brainstorming experiment for product improvement. During this experiment, participants with very low or extremely high arousal performed less creative behavior. These extreme levels of arousal shut down cognitive systems which are required to perform creative behavior. For example, extremely low levels of arousal lead to drowsiness and inattention to a task, which hinders optimal convergent thinking. Extremely high levels of arousal lead to highly restricted attentional selection and a rigidity of focus, which hinders optimal divergent thinking (Gilzenrat et al., 2010). Participants with a moderate level of arousal performed the most enhanced levels of creative behavior due to an active working memory capacity and functioning of the LC-NE (de Dreu et al., 2008). Focusing solely on low, intermediate, and high levels of arousal in the LC-NE could restrict the multifaceted effect of arousal in the creative process. The cue utilization hypothesis states that increased levels of arousal enhance convergent thinking by restricting selection impact performance by restricting selection of extant information. However, this restricted selection could hinder divergent thinking. Therefore, in order to investigate the CREATIVITY THROUGH THE EYES 10 multifaceted effect of arousal, this study focuses on different LC-NE modes and their effect on exploratory and exploitative states of mind.

Figure 3. Inverted U-curve of arousal

1.4 Exploration and exploitation The previously described creative problem solving framework comprises of both divergent and convergent thinking, in which extant knowledge and information are first explored and subsequently exploited to generate creative ideas (Isaksen et al., 2011). The divergent thinking phases are associated with an exploratory state of mind, involving certain characteristics described in section 2.2. Subsequent convergent thinking phases are associated with an exploitative state of mind, involving the characteristics described in section 2.2. The interaction between these states of mind contribute to the production of creative ideas. The interaction between the states of mind affect the creative task performance of an individual. The ambidexterity hypothesis states that an individuals’ ability to switch appropriately between exploration and exploitation, determines the effectiveness of cognitive processes which enhance creative task performance (Gupta, Smith, & Shalley, 2006; Laureiro-Martínez et al., 2015). Research by Laureiro-Martínez et al. (2015) showed that the LC-NE is more activated during exploring compared to exploiting. This was measured by conducting a four-armed bandit test in which participants chose between slot machines to receive the highest pay-out. Participants explored different slot machines or exploited only one. Whereas the total number of exploratory- and exploitative choices did not predict the most advanced decision-making performance, the switching from between the states of mind did. Especially switching from exploration to exploitation predicted the most effective CREATIVITY THROUGH THE EYES 11 decision-making performance. This provides evidence that individuals with a strong ability of switching between these control states will explore the best opportunities and exploit them until better options are explored (Laureiro-Martínez et al., 2015). The study by Laureiro-Martínez et al. (2015) showed that the switching between the exploitative and exploratory states of mind is controlled by the LC-NE. In their study, they measured the activity, associated to switching, by using a functional magnetic resonance imaging (fMRI) scanner. These previous discussed findings reveal the relationship between divergent/convergent thinking and exploratory/exploitative states of mind. Hayes and Petrov (2016) revealed, by measuring pupil diameters, that the switching between the states of mind is mediated by two different modes of the LC-NE. This relationship between pupil diameters and the states of mind is highlighted in the adaptive gain theory by Aston Jones and Cohen (2005).

1.5 Adaptive gain theory Preliminary studies examined the correlation between LC-NE activity and pupil diameter. For example, Gilzenrat et al. (2010) and Jepma and Nieuwenhuis (2011) found that pupil diameter predicts exploratory and exploitative control states by conducting relatively simple oddball discrimination and four-armed bandit tests. Hayes and Petrov (2016) showed, by conducting a more advanced analogical reasoning task, a decrease in pupil diameter during an exploitative state of mind and an increase during an exploratory state of mind. These studies, measuring pupil diameters, support the adaptive gain theory. The adaptive gain theory by Aston Jones and Cohen (2005) implies that the LC-NE modes control the switching between exploitative and explorative control states. This switching is mediated by two modes of LC-NE activity: phasic and tonic. Tonic firing is characterized by an increased baseline activity of the LC-NE (Jepma & Nieuwenhuis, 2011). Tonic firing occurs in periods of wakefulness and is associated with increased arousal (Howells et al., 2012). The tonic mode predicts a decreased response to task-related information which facilitates disengagement and reduced performance on a current task, associated with an exploratory state of mind. Phasic firing of the LC is characterized by an intermediate baseline activity in the LC-NE and occurs during activities which require increased attention and focus on a current task. (Jepma & Nieuwenhuis, 2011). The phasic mode predicts increased responses to task-related information and knowledge and enhances performance and attention on a current task. This is associated with an exploitative state of mind. Howell et al. (2012) states that the synergy between tonic and phasic LC-NE modes is CREATIVITY THROUGH THE EYES 12 required for optimal performance and attention. According to the adaptive gain theory, the different states of mind can be predicted via pupil diameters and therefore also divergent and convergent thinking phases. Subsequently, it can be assumed that pupil diameters can reveal the process and creative thought and predict the creativity of ideas.

1.6 Synthesis Generating creative ideas involves passing through a creative process, consisting of several phases. The first phases are associated with divergent thinking which involves gathering and recombining extant knowledge and information to provide a basis for original ideas. Succeeding phases, involving convergent thinking, involve evaluation and planning of the original ideas in order to become effective. The combination of originality and effectiveness define these ideas as creative. The phases of divergent thinking involve the exploration of extant knowledge and information, concerning a variety of contexts and obtained via multiple sources, to generate original ideas (Baas et al., 2015; Madore, Addis, & Schacter, 2015). Divergent thinking, associated with an exploratory state of mind, is characterized by cognitive flexibility and disengagement from the current task. This enables the gathering and restructuring of extant information and knowledge to generate original ideas. This state of mind is associated with a tonic LC-NE mode which is characterized by increased baseline activity in the LC-NE and subsequently an increased baseline pupil diameter. In contrast, an exploitative state of mind which is characterized by selection refinement, efficiency, and increased focus on a current task enhance the effectiveness of an idea (Baas et al., 2015; Laureiro-Martínez et al., 2015). This state of mind is associated with a phasic LC-NE mode. As the tonic LC-NE mode, predicting an exploratory state of mind and subsequently originality of ideas, the following is hypothesized: H1a: A tonic, rather than a phasic LC-NE mode positively predicts an enhanced originality of creative ideas during divergent thinking in a creative performance task.

In contrast to a tonic LC-NE mode, the characteristics of the phasic LC-NE enhance the effectiveness of ideas. This phasic LC-NE mode is characterized by a reduced baseline LC-NE activity as well as the baseline pupil diameter. As a phasic LC-NE mode predicts an exploitative state of mind, enhancing effectiveness of the ideas, the following is hypothesized: H1b: A phasic, rather than a tonic LC-NE mode positively predicts an enhanced effectiveness of creative ideas during divergent thinking in a creative performance task. CREATIVITY THROUGH THE EYES 13

Converging original ideas into creative ideas is associated with convergent thinking which involves the evaluation and planning of original ideas to become effective (Runco & Acar, 2012). Convergent thinking is associated with an exploitative state of mind (Baas et al., 2015; Laureiro-Martínez et al., 2015). A phasic LC-NE mode predicts an exploitative state of mind, characterized by a reduced baseline LC-NE activity as well as baseline pupil diameter. As a phasic LC-NE mode predicts an exploratory state of mind, which enhances the convergent thinking capabilities, the following is hypothesized: H2a: A phasic, rather than a tonic LC-NE mode positively predicts an enhanced effectiveness of creative ideas during convergent thinking in a creative performance task.

Similar to the first hypotheses, a tonic LC-NE mode, characterized by an explorative state of mind, is expected to enhance the originality of ideas. Therefore, the following is hypothesized: H2b: A tonic, rather than a phasic LC-NE mode positively predicts an enhanced originality of creative ideas during convergent thinking in a creative performance task.

1.7 Conceptual model The conceptual model (Figure 4) gives overview of the relationships in this research.

Figure 4. Conceptual model.

The conceptual model depicts how the LC-NE system affects creative task performance of an individual. The modes of the LC-NE are influenced by the levels of arousal. These modes predicts that divergent and convergent thinking enhance different phases of the creative process. Moreover, each LC-NE modes is expected to enhance originality or effectiveness rather than the opposite LC-NE mode. This study will verify the relation of LC-NE modes on CREATIVITY THROUGH THE EYES 14 convergent and divergent thinking in creative task performance using an experimental design that attempts to answer the research questions: ‘Do phasic and tonic LC-NE modes predict creative task performance during divergent and convergent phases in the creative process in different ways?’

2 Method 2.1 Participants A total of 78 people (55 women; aged 17-32 years; M=23.24, SD= 3.46) participated in the study. Participants were bachelor and master students (66 bachelor) from Tilburg University. Professional marketing experience of the participants was measured on a 5-point Likert scale ranging from 1 (very experienced) to 5 (not experienced). Participants were moderately experienced (M=3.79, SD=1.11). The research was carried out by conducting a quantitative experimental study. The sampling method used for this research was convenience sampling which involves a non-probability sampling technique by selecting participants based on their availability (Treadwell, 2013). In order to promote and invite participants to the study, a participation pool was used. Furthermore, the internet was used to invite participants in order to reach a large enough sample.

2.2 Materials 2.2.1 Apparatus. A PC running OpenSesame 3.0.6 presentation software was used to present the stimuli to participants. OpenSesame is a graphical, open-source, cross-platform experiment builder for social science studies (Mathôt, 2012). This operating system of this PC was Microsoft Windows 7, and the presentation was displayed on a 22 inch monitor, type P2210 Dell with a 1680x1050 resolution. Participants used a USB-connected Dell Keyboard, type KB212-B and a USB-connected Dell mouse. OpenSesame supported, via a plug-in, the eye tracker used to obtain data for this study. Pupil diameters were recorded by an EyeLink II video based eye tracker, worn by participants as a helmet. Pupil diameters were measured by two eye cameras at 500 Hz. As make-up in the eye area and glasses can occlude the participant’s eye from the cameras, participants were notified that these were not allowed during the experiment (Hansen & Hammoud, 2007). A sensor, integrated in the headband, measured the point of gaze to CREATIVITY THROUGH THE EYES 15 confirm respondents’ attention to the presentation screen. The helmet was equipped with two adjustment straps, located on the top and back of the head, to enhance comfort and fit on the participant’s head. The cable, connecting the eye tracker to the computer, was attached to roof of the soundproof research booth to enhance comfortability and prevent the participant’s head from being pulled backwards. The computer connected to the eye tracker was running EyeLink Data Viewer software on a Windows 7 operating system to collect the data. The experiments took placed in a soundproof research booth, slightly illuminated by two sticks of LED-lights slightly. One stick was placed behind the participants and the other stick behind the monitor. The screen of the computer running the EyeLink Data Viewer Software was turned off during the experiment. The screen providing the stimuli was slightly dimmed to 75%.

2.2.2 Stimuli. 2.2.2.1 Layout. Stimuli were presented with an OpenSesame presentation, containing a medium grey background and black letters during the entire experiment in order to reduce the eye stress caused by bright colors (Jepma & Nieuwenhuis, 2010; Zhang & MacKenzie, 2007). Furthermore, during fixation crosses and moments of thinking the mouse cursor was hidden to reduce visual distraction (Zhang & MacKenzie, 2007). The stimuli used in this study are displayed in consecutive order in Appendix I.

2.2.2.2 Definition of the problem. Participants were presented with a problem for which they had to generate ideas. This problem was presented to them with a text on screen. The problem in the practice task was different from the problem in the experiment. In the practice task, participants were instructed to generate ideas for new courses in their study program at the Tilburg University. In the experiment, participants were instructed to generate ideas how an online bicycle shop can attract more customers. This problem was the same for each phase as convergent thinking involves restructuring extant knowledge as described in section 2.2.

2.2.2.3 Measuring pupil sizes. In order to measure the baseline of the participants’ pupils, a black fixation cross was displayed in the middle of the screen to which they were instructed to look at. CREATIVITY THROUGH THE EYES 16

To measure the tonic pupil size, participants were instructed to look at a fixation cross while relaxing and not thinking about any new ideas. To make sure participants did not think about ideas, they confirmed this by clicking ‘yes’ or ‘no’ after each fixation cross. In order to measure phasic pupil sizes, participants were instructed to indicate the time they were thinking about an idea. During the period between clicking the mouse button when they started thinking and clicking again when they had generated an idea, their phasic pupil sizes were measured. During the ‘thinking periods’ a blank screen was presented.

2.3 Procedure The procedure of the study involves the preparation regarding the eye tracker and specific requirements, a practice task and the real experiment. The entire process is visualized in Appendix I and explained in this subchapter.

2.3.1 Preparation. After welcoming the participants to the experiment and signing the consent form and attendance list, they were checked with regards on the specific requirements (i.e. make-up, glasses, caffeine consumption). Participants were not allowed to consume caffeine containing- and intoxicating products prior to the experiment as this increases levels of arousal and subsequently pupil diameters (Doyle, Lutz, Pellegrino, Sanders, & Arent, 2016). In cases of violation regarding make-up and glasses, participants were given the opportunity to remove these. Make-up remover and make-up supplies for after the experiment were provided. In cases of violation of caffeine or intoxicating products consumption, a new appointment was planned. After checking for violations, participants were requested to take a seat in the soundproof research booth and position themselves in a comfortable position, with a distance of 60 centimeters from the monitor. They were instructed to remain in this position during the experiment. The EyeLink II was placed on the participants’ head and adjusted by the participant using the two straps. The researcher checked whether it fit by requesting participant to shake their head and the confirming that the helmet remained in position, and the headband was located just above the eyebrows. The cameras, measuring the pupils, were adjusted to the right position (in the middle of the measurement screen) and focus. After locking the eyes, OpenSesame calibrated and validated the participants’ eyes by a simple eye movement task.

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2.3.2 Practise task. After calibration, the researcher left the research booth and the participant engaged in the practise task. A short instruction including the problem of the practise task was displayed on screen. After confirming to understand the instructions, a fixation cross appeared on screen, followed up by a screen on which the participants could indicate whether they thought of ideas during the fixation screen. After indicating, participants were instructed to left-click the mouse when they thought about a new idea. After indicating that they had an idea, a textbox appeared to insert the idea. This process was repeated two times ending with the instruction to call the researcher for additional information and questions.

2.3.3 Experiment. After answering all questions, the experiment began with a fixation cross, displayed for two minutes, to measure the baseline pupil diameter. After measuring the baseline, the first phase of the experiment began. Participants were presented with general description to generate short and concise ideas for the defined problem. After confirming to understand the instructions, a black fixation cross appeared in the middle of the screen for five seconds. Afterwards, participants could indicate whether they thought about ideas during the fixation cross. After indicating, participants were instructed generate short and concise ideas and indicate the time they were thinking. Afterwards, a textbox to insert ideas appeared in the middle of the screen. In case participant could not come up with anymore ideas, they could abort the first part of the experiment by inserting ‘stop’ as an idea. This process was repeated for a maximum of 10 times, unless the participant aborted earlier. The second phase of the experiment started with the instruction to generate elaborated and detailed ideas based the ideas from the first phase. Similar to the first phase, participants first relaxed and looked at a fixation cross for five seconds, succeeded by indicating whether they thought about new ideas. After indicating, participants generated ideas and indicated periods of thinking by left-clicking the mouse. Ideas could be inserted in a text box. Similar to the first part, participants could abort the second part of the experiment by inserting ‘stop’ as an idea. This process was repeated for a maximum of 3 times, unless the participant aborted earlier. After the second phase, another fixation cross was displayed for 2 minutes to measure the baseline pupil diameter. Once the experiment was finished, the participant called the researcher again to remove the helmet and provide a survey containing sociodemographic questions (i.e., age, gender, educational level and professional marketing experience) in order to identify CREATIVITY THROUGH THE EYES 18 participants. After finishing the survey, additional questions or remarks were answered and participants were thanked for participating in the study.

2.4 Data analysis 2.4.1 Pupil analysis. Pupil diameter data was processed and analyzed using EyeLink Data Viewer software. Before analyzing, the data was filtered from noise by a 5 Hz low pass Butterworth filter (Butterworth, 1930). Additionally, linear interpolation was used to filter out missing data (e.g. participants closing their eyes) and a correction on peaks in the data caused after blinking the eyes was performed. To reduce between subject variations due to individual differences in pupil size, pupil size measurements were normalized by calculating Z-scores by using the values from the phasic and tonic measurements, standardized using the mean and standard deviation from the baseline measurement recorded before the task. Furthermore, extreme values were assumed to reflect measurement error, and were removed using Tukey’s methods (k=3.0). The baseline pupil diameter was assessed during the periods of looking at the fixation cross, as well as periods of thinking about creative ideas. To determine pupil diameter, the data in the period of 2.5 to 0.5 seconds before ending periods of thinking and looking at fixation crosses were averaged. These periods are based on finding by Jepma and Nieuwenhuis (2010), who noticed an increase in pupil diameter starting 0.5 seconds before clicking. Whereas the average pupil diameter of periods looking at the fixation cross represented the tonic LC-NE activity, the phasic LC-NE activity was calculated by subtracting the average pupil diameter of looking at the fixation cross from the average pupil diameter of the thinking period. The preliminary study by de Dreu et al. (2008) showed a quadratic (inverted U-curve) relationship between arousal and creative task performance. Therefore, the pupil diameter results were analyzed as quadratic as well as linear variables in a linear mixed model.

2.4.2 Idea analysis. The ideas, generated by participants, were rated by expert judges with marketing experience. Ideas with similar characteristics were placed in an overarching cluster with a label consisting of a summarized description of the ideas. Ideas from the divergent (N=501) and convergent (N=186) were placed in 97 and 57 clusters respectively. Clusters from the divergent phase were rated separately from the clusters of the convergent phase. CREATIVITY THROUGH THE EYES 19

The expert judges rated the ideas based on originality and effectiveness separately. In an online survey, the judges were presented with 5 cluster labels each time. Each individual cluster was presented two time within different combinations. These combinations were selected based on a random sampling method. The expert judges rated the clusters based on a MaxDiff scale, indicating the most original/effective and least original/effective cluster of each combination. After combining the data from the expert judges, a cluster was considered original or effective when it was rated positively 1 or 2 times. Similar, a cluster was considered not original/effective when it was rated negatively 1 or 2 times.

2.4.3 Synthesis analysis. In order to measure whether phasic and tonic LC-NE modes predict the creative task performance during divergent and convergent phases, it was measured whether the average tonic and phasic activity in the LC-NE modes predicted the originality or effectiveness of an idea. A linear mixed model was chosen to analyse the data because of the repeated repeated measurements for each participant and the multiple measurement per idea cluster. Moreover, the chosen approach led to missing data which can be handled by mixed linear models in contrast to other repeated measurement techniques (Field, 2013).

3 Results 3.1 Control variables Cohen’s κ was run to determine the agreement of the judges in their ratings on originality and effectiveness of the ideas. For originality, the judges scored a fair agreement on their ratings (κ=.233, p<.01). A slightly higher, but still fair, agreement was found the effectiveness ratings of the judges (κ=.323, p<.01). Table 1 shows the means, standard deviations, and Spearman correlation coefficients for the means of the variables related to the tonic and phasic pupil sizes. It can be seen that there is a large significant correlation between the tonic and phasic pupil sizes (ρ=.447, N=78, p<.01). Furthermore, it is shown that there is a significant weak negative correlation between the originality and effectiveness of the ratings by the expert judges (ρ=-.144, N=78, p<.01). This assumes that the more original an idea is, the less effective it is.

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Table 1. Means, standard deviations, and Spearman correlation coefficients Dependent variable Mean (SD) 1. 2. 3. 4. 1. Tonic pupil size .34 - (1.28) 2. Phasic pupil size 1.53 .447* - (2.10) 3. Originality ratings -.15 .037 -0.04 - (.92) 4. Effectiveness -.03 .053 .035 -.144* - ratings (.83) * p<.01

3.2 Predicting creativity during divergent and convergent thinking To test whether tonic and phasic LC-NE activity can predict creative task performance, linear and quadratic (squared) tonic and phasic pupil size, and creative process phase (divergent/ convergent) were submitted as linear and quadratic terms to a mixed linear model. We specified the creative process phase as a repeated measures factor; and using the scaled identity covariance matrix; which was chosen by trying different covariance structures to minimize the -2 log likelihood information criterion (Field, 2013). The dependent variables in these tests were the originality and effectiveness of the ideas rated by expert judges. The results are shown in Table 2. As can be seen from these results, phasic pupil size is no significant linear, (b=-.063, t(602.27)=-1.36, p=.175) or quadratic (b=.010, t(621.46)=1.44, p=.151) predictor of originality during divergent thinking. However, tonic pupil size is a significant positive linear predictor (b=.095, t(494.50)=2.17, p=.030) which is in conjunction with a negative quadratic (b=-.016, t(639.41)=-1.02, p=.311) predictor of originality during divergent thinking. This suggests that a tonic, rather than a phasic LC-NE mode positively predicts originality of creative ideas during divergent thinking while performing a creative task. However, whereas the linear results suggest that a tonic LC-NE mode is a significant predictor of originality, the quadratic results do not significantly support the hypothesis. This supports hypothesis H1a. Besides the results of originality, Table 2 shows the results regarding the effectiveness of the ideas generated in the divergent thinking phases of the creative process. It can be seen that phasic pupil size is a significant positive linear predictor (b=.110, t(555.58)=2.66, p=.008) which is in conjunction with a negative quadratic (b=-.018, t(575.78)=-3.03, p=.003) predictor of effectiveness during divergent thinking. This suggest that there is a significant inverted U-curve relationship (Farh, Lee, & Farh, 2010). By contrast, tonic pupil size is no CREATIVITY THROUGH THE EYES 21 significant linear (b=.019, t(447.67)=.473, p=.636) neither a quadrative (b=.-018, t(597.06)=- 1.21, p=.227) predictor of the effectiveness during divergent thinking. This suggests that phasic pupil size is a significant predictor of the enhanced effectiveness of the ideas according the results. This is shown for linear as well as quadrative results. This supports hypothesis H1b. The results of the convergent phases are shown in in Table 2. These results in this table show that phasic pupil size is no significant linear (b=.076, t(667.45)=1.11, p=.266) nor quadratic (b=.011, t(668.29)=-1.11, p=.268) predictor of enhanced originality of the ideas generated during the convergent thinking phases. Similarly, the results show that linear (b=- .045, t(657.27)=-.721, p=.471) and quadratic tonic pupil sizes (b=-.005, t(662.36)=-.226, p=.821) are no significant indicators for originality of the ideas generated during the convergent thinking phases. Hypothesis H2a assumed that a tonic rather than a phasic LC-NE mode predicts enhanced originality during convergent thinking phases. However, the results show that tonic nor phasic LC-NE modes can significantly predict originality in the convergent thinking phases of a creative process. Therefore, hypothesis H2a is rejected. With regards to the effectiveness of the ideas generated in the convergent thinking phases, Table 2 shows that the phasic pupil size is no significant linear (b=-.026, t(633.34)=- .42, p=.676) or quadrative predictor (b<.001, t(635.69)<-.01, p=.998) predictor of effectiveness. These results are comparable with the tonic pupil size. It is shown that tonic pupil sizes are no significant linear, b=-.003, t(620.72)=-.05, p=.958, or quadratic, b=.015, t(626.96)=.80, p=.426 predictors of effectiveness. These results reject hypothesis H2b, which assumed that a phasic LC-NE mode, rather than a tonic LC-NE mode would predict the effectiveness of ideas in the convergent thinking phases of a creative process.

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Table 2. Estimates of fixed effects during divergent thinking Originality Effectiveness

Intercept -.133 (.094) -.102 (.086) [-.317 .052] [-.272 .067]

Divergent Phasic pupil size Linear -.063 (.046) .110 (.041) ** [-.154 .028] [.029 .192] Quadratic .010 (.007) -.018 (.006) ** [-.004 .023] [-.030 -.006] Tonic pupil size Linear .095 (.044) * .019 (.040) [.009 .182] [-.059 .097] Quadratic -.016 (.016) -.018 (.015) [-.048 .015] [-.046 .011] Convergent Phasic pupil size Linear .076 (.068) -.026 (.063) [-.058 .210] [-.151 .098] Quadratic -.011 (.010) <-.001 (.009) [-.030 .008] [-.018 .018] Tonic pupil size Linear -.045 (.062) -.003 (.057) [-.168 .078] [-.114 .108] Quadratic -.005 (.020) .015 (.018) [-.044 .035] [-.022 .051]

Repeated .791 (.046) .643 (.038) measures VAR (AVG) Intercept .046 (.022) .023 (.016) (subjects=ID) VAR * p<.05 * p<.05 ** p<.01 a Set to zero because of redundancy

3.3 Conceptual model To conclude, Figure 5 provides an overview of the significant predictors according to the conceptual mode. The results assume that tonic and phasic LC-NE modes are predictors of originality and effectiveness respectively in the divergent thinking phases of a creative process. However, for the convergent thinking phases of a creative process, no significant results were found in the prediction of creativity and effectiveness. CREATIVITY THROUGH THE EYES 23

Figure 5. Conceptual model including results.

4 Discussion & conclusion Creativity can contribute to happier and fuller lives of people and enhance the welfare of a society (Kaufman & Sternberg, 2010, Richards, 2010). Are there is a limited understanding of the factors that enable creative thought, this study helps to identify these factors so individuals and groups can enhance their creative thinking processes. The aim of the analysis was to answer the following research questions: ‘Do phasic and tonic LC-NE modes predict creative task performance during divergent and convergent phases in the creative process in different ways?’ By conducting an experimental study consisting of a within subject factorial design, insights in the factors which enable creative thought were obtained.

4.1 Main findings Hypothesis H1a was partially supported by the findings in this study. This hypothesis posed that a tonic LC-NE mode would predict an enhanced originality of the creative ideas, generated in the divergent thinking phases, rather than a phasic LC-NE mode. This assumption was partially confirmed by the data obtained from the eye tracking experiments and expert judges. Linear results supported the assumption, but the quadratic results did not. These results are similar to findings in preliminary studies by Gilzenrat et al. (2010) and Jepma and Nieuwenhuis (2011). These studies showed that divergent thinking is associated with an explorative state of mind and a tonic LC-NE mode. This explorative state of mind enhances disengagement from the current task, experimentation, flexibility, discovery and CREATIVITY THROUGH THE EYES 24 subsequently the originality of creative ideas (Daw, O’Doherty, Dayan, Seymour, & Nolan, 2006; Laureiro-Martínez, Brusoni, Canessa, & Zollo, 2015). In hypothesis H1b, it was expected that the phasic LC-NE mode would predict an enhanced effectiveness of the ideas generated in the divergent thinking phases of a creative process. This prediction arose from findings by Jepma and Nieuwenhuis (2011) that a phasic LC-NE mode predicts an exploitative state of mind. This is characterized by an increased response to task-related information and knowledge and enhanced performance and attention on a current task. The assumption of H1b was both linear and quadratic supported by the data. The effectiveness of creative ideas can thus be predicted by the phasic LC-NE mode. H2a and H2b posed similar assumption to the predictive abilities of tonic and phasic modes as hypotheses H1a and H1b. However, as these hypotheses focused on the convergent thinking phases of a creative process different results were obtained. Based on studies by Gilzenrat et al. (2010) and Jepma and Nieuwenhuis (2011), H2a predicted that a phasic, rather than a tonic LC-NE mode positively predicts an enhanced effectiveness of creative ideas. In addition, H2b predicted that tonic, rather than a phasic LC-NE mode predicts an enhanced originality of the creative ideas. Both hypotheses were rejected by the data. During the current study, a few surprising findings were obtained. It was expected that both originality and effectiveness of ideas generated in the divergent and convergent thinking phases could be predicted by tonic and phasic LC-NE modes respectively. This expectation was confirmed by the results from the divergent thinking phase. The tonic LC-NE mode predict the originality of the ideas, whereas the phasic LC-NE mode predict the effectiveness. However, no significant results were found for the convergent thinking phases. This possibly deviates from previous findings. For instance, in the framework of creative problem solving, Isaksen et al. (2011) described that the first phases of the process of creative thought are associated with divergent thinking, whereas the final phase is associated with convergent thinking. It has been shown that divergent thinking enhances originality and convergent thinking effectiveness of the ideas (Addis et al., 2016; Jones & Estes, 2015). Therefore, it is likely that especially originality could be predicted in the divergent phases and effectiveness in the convergent phase. However, the results of this study show that the effectiveness of the ideas cannot be predicted in the convergent phases of the process of creative thought, whereas it can be predicted in the divergent phases of the process. The results showed that the phasic LC-NE is a linear as well as quadratic predictor of effectiveness in the divergent phase. The quadrative results suggest a positive inverted U- curve relationship which is similar to the relationship between arousal and creative task CREATIVITY THROUGH THE EYES 25 performance by Yerkes and Dodson (1908). With regards to the tonic LC-NE mode predicting originality in the divergent thinking phases, only a significant effect was found for linear results. Even though, the positive linear and negative quadrative results suggest a positive, downward curve as well. The relationship by Yerkes and Dodson (1908) was supported by de Dreu et al. (2008) by conducting a divergent thinking experiment. The similar downward curves for these studies suggest that there is a relationship between the arousal and creativity during the divergent thinking phases of creative thought.

5.2 Limitations 5.2.1 Experience. The aim of this study was to examine whether tonic- and phasic LC-NE modes could predict the creative task performance. Inconsistencies in the relationship between LC-NE activity and creative task performance can be explained by the difference in professional marketing experience among participants. In the current study, participants generated ideas to solve a problem in a marketing context. Similar ideas from different participants were combined into one cluster. However, the effort and creative process it took to generate a similar idea could differ per participant. Bowden and Jung-Beeman (2007) relied on qualitative reports, given by participants about their creative process, to measure the creativity of an idea. Based on these reports, it could be determined whether insight or analytical thinking had occurred during the generation of a creative solution. Bowden and Jung-Beeman (2007) believed that insight, which involves coming up with a novel solution at a sudden moment, is an act of creativity. Insight only occurs when a solution is personal creative, meaning that the solution did not arose in the mind of a person before. However, it could be that many other people had the same idea before. In contrast, analytical problem solving is a step-by-step procedure to come up with an idea. Chamakiotis, Dekoninck, and Panteli (2013), showed that experience related knowledge is a major enhancer of creativity in virtual design teams. Experience related knowledge can be obtained via education and prior experiences, and can differ among individuals. As insight can occur due to prior experiences and knowledge, this means that the experience of participants with the context of the study could have caused differences in the effort and creative process it took to generate a similar idea. Thus, these differences may explain the differences between pupil sizes within a single cluster.

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5.2.2 Repeated testing. The inconsistent findings between the significant results in the divergent phase, and non- significant results in the convergent phase of the experiment might be related to the specific task that was used in this study. The problem (i.e. attracting more customers to the online bicycle shop) was similar for both phases of the experiment, which could cause a high learning rate and issues concerning repeated testing. Repeated testing could cause boredom which is a negative predictor of cognitive performance caused by reduced attention and engagement (Haager, Kuhbandner, & Pekrun, 2016). Furthermore, during the experiment, participants were given the opportunity to abort both the divergent and convergent phase before they reached the maximum number of repetitions. This could have caused that participants with reduced attention and engagement aborted the experiment before an effective mix of divergent ideas was generated before continuing to the convergent phase. However, it was expected that forcing participants to be creative and insert a certain number of ideas, could cause invalid results. Thus, repeating the problem could explain the inconsistencies between the different phases of the creative process.

5.2.3 Pressure of time. There were also inconsistent findings for linear and quadratic relationships between LC-NE activity and creative task performance. This can partly be explained by the use of time pressure in previous experiments. In the current study, no pressure of time was involved. Participants were given all the time they needed to generate ideas. According to Baer and Oldham (2006), the pressure of time can influence the creativity in a quadratic relationship, similar to the relationship between creativity and arousal. Moderate levels of time pressure enhance the level of creativity the most. Adding moderate levels of time pressure in an experiment could cause significant results and raises the ecological validity of the study as activities in today’s society are commonly affected by pressure of time. Thus, the lack of time pressure may explain inconsistencies in finding a quadratic relationship in the current study

5.2.4 Relationship between LC-NE and pupil diameter. Although significant results were found in predicting creativity from pupil diameters, the conclusions should be taken with care. The evidence for a relationship between pupils and LC-NE activity is still limited. According to Jepma and Nieuwenhuis (2011), it is unlikely that the LC-NE system is the only system involved in explorative and exploitative thinking. Preliminary studies showed that dopamine influences the levels of explorative thinking which CREATIVITY THROUGH THE EYES 27 could cause a spurious relationship on the results of the current study (Chermahini & Hommel, 2010; Frank, Doll, Oas-Terpstra, & Moreno, 2009).

5.3 Future work Future work can address some of the interesting limitations uncovered about the current study. First, conducting a qualitative study can control for inconsistencies between pupil sizes within the same cluster as discussed in section 5.2.1. Qualitatively analyzing the creative thinking process could measure whether insight or analytical processes occurred in order to generate a creative idea. Interviews provide the possibility to question participants about their experiences during the process of creative though. Furthermore, Woodman and Schoenfeldt (1989) found that creative individuals approach problems with a greater intensity, reflection and persistence compared to non-creative individuals. These findings assume that creative performance can differ among people and subsequently, the creative effort of generating a similar idea can differ between individuals. In the current study, ideas were rated on creativity by expert judges who did not took the individual differences between participants into account. A study in which participants self-report the creativity of their ideas can measure the creativity on an individual level. Thus, qualitatively measuring the different thinking processes (i.e. insight, analytical thinking) can identify differences within clusters. In addition, self-reporting creativity can identify personal differences in perceived creativity. Second, inconsistencies that could have been caused by repeated measurement as discussed in section 5.2.2, can be mitigated in future studies. In the current study, only one problem was addressed and repeated several times. It is expected that conducting an experiment with various problems and less repetitions per problem could cause less issues regarding repeated testing. Furthermore, Van Hooff and van Hooft (2016) found that rewards can prevent boredom as well. Offering students the possibility to present the best ideas to a marketing agency and execute the ideas is a reward that could prevent boredom in the experiment. Third, as discussed in section 5.2.3, inconsistent findings between linear and quadratic results could also be explained by differences in time pressure. The study by Baer and Oldham (2006) showed that moderate levels of time pressure enhance creativity. As this study was conducted in an experiment with no time pressure involved, it can be assumed that moderate time pressure could lead to different results in a similar experiment. A future study can be conducted with a time limit instead of a certain maximum number of repetition per experimental phase. Furthermore, no possibility to abort the experiment prior to completing CREATIVITY THROUGH THE EYES 28 the time and requiring a certain number of ideas within the time limit could raise the pressure of time in an experiment. As ideas become more creative, it can be expected that results become more significant for both divergent- and convergent thinking. Fourth, the difference in consistent and inconsistent findings can be explained by a limited evidence for a relationship between pupil diameter and LC-NE activity (Gilzenrat et al., 2010; Jepma & Nieuwenhuis, 2011). Therefore, the results assuming that LC-NE activity can predict originality and effectiveness should be taken with care. Additional neurophysiological studies need to be conducted in order to provide more evidence for this relationship.

5.4 Conclusion In conclusion, this study aimed to reveal the predictive capabilities of LC-NE modes on creative task performance. It was found that pupil diameters can predict creativity in the divergent thinking phases of a creative process. The results demonstrated that phasic pupil sizes are a linear as well as quadratic predictor of effectiveness during divergent thinking phases. Moreover, tonic pupil sizes are a linear predictor of originality during the divergent thinking phases. These findings are as expected, considering that a phasic LC-NE mode is related to cognitive processes enhancing effectivity, whereas a tonic LC-NE mode is related to cognitive processes enhancing originality. It can therefore be assumed that, with regards to divergent thinking: ‘Creativity -can be predicted- through the eyes’.

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Appendix I: Flow of stimuli during experiment