Testing the Dual-System Theory of Decision-Making: A Pilot Study Using Electroencephalography (EEG)
Lisa Marieke Kluen Integrated Program In Neuroscience Douglas Research Institute McGill University, Montreal
October 2014
A thesis submitted to McGill University in partial fulfillment of the requirements of the degree of Masters of Science
© Lisa Marieke Kluen, 2014
1 Table of Contents
Abstract (English, French)
Acknowledgements
1. Introduction 1.1 Background: decision-making 1.2 Measuring decision-making: The Iowa Gambling Task (IGT) 1.3 Models of decision-making 1.3.1 The Somatic Marker Hypothesis (SMH) 1.3.2 The Dual-System Theory 1.4 Measuring implicit processes of decision-making using electroencephalography (EEG) 1.4.1 EEG and Decision-Making 1.5 Decision-preceding negativity (DPN) as a marker of implicit anticipation processes of decision-making 1.6 Objectives of the study and hypotheses
2. Materials and Methods 2.1 Participants 2.2 Socio-demographic and clinical assessments 2.3 Experimental Task 2.4 EEG Recording 2.5 Data Analysis 2.6 EEG Analysis
3. Results 3.1 Behavioral Data 3.2 EEG Data
4. Discussion
5. Limitations
5. Future Directions
7. Conclusion
8. Bibliography
2 Abstract
Making decisions is an essential part of life. The capability to discriminate between risky and safe or the ability to foresee long-term consequences of actions and thus change expectations through experience, are crucial in the process of making advantageous and strategic decisions in everyday life (Rothkirch, Schmack, Schlagenhauf, & Sterzer, 2012). Moreover, impaired decision-making has been found in patients suffering from lesions in the ventromedial prefrontal cortex as well as in different psychiatric disorders (Adida et al., 2011; Cella, Dymond, & Cooper, 2010; Malloy-Diniz, Neves, Abrantes, Fuentes, & Correa, 2009; Murphy et al., 2001). Improving our understanding of the mechanisms of normal and pathological decision-making is, therefore important.
Different models have been developed to research and explain decision- making behavior. The somatic marker hypothesis (Bechara, 1997) proposed that, to be able to make advantageous decisions in uncertain situations, unconscious and automatic bodily signals (the so-called somatic markers) are critical (Bechara, 1997). More generally, the dual-system theory of decision- making (Evans, 2008; Kahneman, 2011) postulates that decision-making relies on two brain systems, one being unconscious, implicit and fast, and the second being conscious, explicit and effortful (Evans, 2008; Kahneman, 2011)
The aim of the current work was to explore the implicit processes guiding decision-making, notably those immediately preceding a willed risky decision. To this aim, we used EEG and the Iowa Gambling Task in a group of 35 healthy participants. We identified a decision-preceding negativity (DPN) in the frontal and central area of the cortex. The results of the current study support the somatic marker hypothesis as well as the assumption that implicit processes support and guide decision-making behavior (Bechara, Damasio, Tranel, & Damasio, 2005). Additionally, these markers might be beneficial in future research investigating aberrant decision-making behavior in clinical populations.
3 Résumé
La prise de décision est un élément essentiel de la vie. La capacité de discrimination entre risque et sécurité ou la capacité de prévoir les conséquences à long terme des actions et donc de modifier les attentes grâce à l'expérience sont essentiels dans le processus de prise de décision avantageuse et stratégique dans la vie quotidienne (Rothkirch , Schmack , Schlagenhauf , et Sterzer , 2012). En outre, une prise de décision désavantageuse a été observée chez les patients souffrant de lésions du cortex préfrontal ventromédian, ainsi que dans différents troubles psychiatriques (Adida et al, 2011; Cella, Dymond, & Cooper, 2010; Malloy - Diniz, Neves, Abrantes, Fuentes, et Correa , 2009; Murphy et al, 2001). L'amélioration de notre compréhension des mécanismes de prise de décision normal et pathologique est donc important. Différents modèles ont été développés pour expliquer la prise de décision. L’hypothèse des marqueurs somatiques (Bechara, 1997) a proposé que, pour être en mesure de prendre des décisions avantageuses dans des situations incertaines, des signaux corporels automatiques et inconscients (les marqueurs dits somatiques) sont essentiels. Plus généralement , la « théorie du système duel » de la prise de décision (Evans, 2008; Kahneman, 2011) postule que la prise de décision repose sur deux systèmes cérébraux , l'une étant inconscient , implicite et rapide , et le second étant conscient, explicite et nécessite un effort. L'objectif des travaux présentés ici était d'explorer les processus implicites guidant la prise de décision, notamment ceux précédant immédiatement une décision risquée volontaire. Dans ce but, nous avons utilisé l'électroencéphalographie (EEG) et l’Iowa Gambling task chez 35 participants sains. Nous avons identifié une onde négative nommée « decision-preceding negativity » (DPN) dans les régions préfrontales et centrales du cortex. Les résultats de l' étude appuient l'hypothèse des marqueurs somatiques notamment que des processus implicites guident la prise de décision (Bechara et al., 2005). En outre, ces marqueurs pourraient être utiles dans de futures recherches pour étudier le comportement de prise de décision aberrante dans les populations cliniques.
4
Acknowledgements
I would like to thank my supervisor Dr Fabrice Jollant as well as my committee members Dr Mathieu Brodeur and Dr Sylvain Baillet. I am grateful to have received their support and that I could benefit and learn from their expertise.
I would like to also thank Yang Ding and Dr Stéphane Richard-Devantoy for their help and nice words during my time at McGill.
5 1. Introduction
1.1 Background: decision-making
Advantageous decision-making is critical for survival and adaptation. Patients with decision-making deficits show numerous problems in everyday-life
(Bechara 1994), notably in complex situations where options are numerous and in social situations (Bechara, Tranel, & Damasio, 2000; Bianchin &
Angrilli, 2011), leading them to experience severe disability and negative consequences (F. Jollant et al., 2005).
Such inabilities have been reported initially among patients with lesions of the ventromedial prefrontal cortex (Bechara, Tranel, et al., 2000; Eslinger &
Damasio, 1985). Various psychiatric disorders have also been linked to disadvantageous decision-making, including mood disorders (Adida et al.,
2011; Cella, Dymond, & Cooper, 2010; Malloy-Diniz, Neves, Abrantes,
Fuentes, & Correa, 2009; Murphy et al., 2001), histories of suicidal acts (F.
Jollant et al., 2005), borderline and antisocial personality disorders (Bazanis et al., 2002; Blair, 2001), and schizophrenia (Sevy et al., 2007), among others. These patients tend to make riskier decisions based on immediate reward and seem to disregard possible long-term outcomes (Knoch et al.,
2006). Understanding mechanisms of normal and pathological decision- making may shed light on ways to improving this important cognitive function when it is altered.
6 Decision-making relies on a number of interrelated processes, including the assessment of risk (i.e the probability of negative outcomes relative to positive outcomes) and the ability to learn from previous experience (Bianchin &
Angrilli, 2011). Options are often numerous and the underlying rules (e.g. the risk associated with each option) usually unknown (Bechara, Tranel, et al.,
2000; Bianchin & Angrilli, 2011). To be able to effectively reach desired outcomes when making a decision, it is necessary to evaluate and balance the options and related outcomes (value-outcome association) (Bechara,
Tranel, et al., 2000). It is also necessary to learn form previous experience and change expectations and decision strategies when needed (reversal learning) (D'Cruz, Ragozzino, Mosconi, Pavuluri, & Sweeney, 2011).
In this context of uncertainty it is known that human behavior is regulated through emotional anticipation mechanisms that enable us to recognize and avoid negative situations (danger) and anticipate but also identify positive situations. These abilities offer an opportunity to choose among alternatives and make decisions with beneficial results that ultimately aid survival
(Bianchin & Angrilli, 2011). Also, memory enables us to keep track of previous experiences, good or bad, and use it to guide future decisions, when common features can be detected (Zeithamova, Dominick, & Preston, 2012).
In summary, decision-making relies largely on mechanisms of risk assessment, learning and the formation of anticipatory mechanisms (Bechara,
2004; Wang, 2008). These processes will be the focus of this study.
7 1.2 Measuring decision-making: The Iowa Gambling Task (IGT)
The ability to anticipate the outcome of a decision, whether positive or negative is a crucial part of the decision-making process. The most widely recognized method to examine decision-making behavior together with risk anticipation and learning in a laboratory setting is the Iowa Gambling Task
(IGT), a test developed by Bechara and colleagues in the 1990s at the
University of Iowa (Bechara, Damasio, Damasio, & Anderson, 1994).
This test has been utilized primarily to explain decision-making impairment in patients with brain lesions and has subsequently been used in various clinical populations (Gorlyn, Keilp, Oquendo, Burke, & John Mann, 2013; Kim, Lee, &
Kim, 2006). The IGT has been developed as a model for economic decision- making that comprises financial gains and losses in conditions of uncertainty, which is especially true in the first part of the game when knowledge is limited
(Bechara et al., 2005; Bechara, Tranel, et al., 2000). The IGT incorporates both implicit and explicit aspects of learning (Bechara et al., 2005). The majority of gambling tasks used to assess decision-making behavior lack this learning element (e.g. the Cambridge Gambling Task, in which the risk with each option is known (Rogers et al., 1999)). Using the IGT, it is therefore possible to examine anticipation mechanisms associated with implicit learning
(Bechara et al., 2005).
8 When completing the IGT, participants are asked to pick cards from 4 different decks, one at a time, while their goal is to win as much money as possible.
Participants are informed that when they pick a card, they can win or lose money depending on the deck chosen. What they do not know however is that two of the decks can be considered safe decks as they yield small wins but even smaller losses, leading to a long-term net gain. The other two decks are risky decks as they yield high wins but even higher losses, leading to a long-term net loss. As participants are unaware of the characteristics of each deck, they should learn throughout the task to make advantageous choices.
What is to be noted also, is the fact that the task is specifically set up for subjects to feel uncertain and therefore avert conscious and rational calculation of wins and losses (Bechara, Damasio, & Damasio, 2000;
Bechara, Tranel, et al., 2000; Bianchin & Angrilli, 2011).
The implicit component present in anticipation of choosing from a deck is suggested to reflect the emotional guidance of behavior (Bechara, Damasio,
Tranel, & Anderson, 1998). This ‘emotional factor’ was previously quantified through skin conductance responses (SCRs) that develop throughout the task to eventually manifest in anticipation of bad choices. These SCRs seem to guide decision-making as they are correlated with final performance (Bechara et al., 2005). Other groups have confirmed these findings (Guillaume et al.,
2009). These emotional factors are also termed ‘hunches’ (Bechara et al.,
1998; Brand, Recknor, Grabenhorst, & Bechara, 2007; F. Jollant et al., 2010;
Lawrence, Jollant, O'Daly, Zelaya, & Phillips, 2009).
9 Bechara et al (2005) have proposed that it is possible to contrast between different phases throughout the task: a pre-hunch period after approximately the first 10 choices, a hunch period after the first 50 choices and the conceptual or explicit period after the first 80 choices, on average (Bechara et al., 2005; Brand et al., 2007). Results obtained indicate that many healthy controls started to develop SCRs in the pre-hunch period and showed a choice behavior that clearly indicated a preference for safe decks before even being able to explicitly identify the safe decks, suggesting that implicit processes are in play before any conscious knowledge. SCRs were also more pronounced in anticipation of choosing from risky decks. This pattern was observed throughout the task and reached a plateau while participants developed an explicit understanding of the game (Bechara et al., 2005).
Even when explicit understanding was not acquired by healthy participants,
SCRs were often shown to be present to bias choice behavior. Contrasting these observations in healthy subjects are the results obtained from patients with ventromedial prefrontal cortex damage. Even in the situation where they acquired some explicit understanding, they showed low performance
(Bechara et al., 2005). Hence, three observations can be stated. The implicit mechanism quantified through SCRs plays a crucial role in guiding decision- making behavior in an advantageous direction in conditions of uncertainty.
Secondly, this implicit mechanism seems to be absent or dysfunctional in patients with ventromedial prefrontal cortex damage and thirdly, explicit understanding may not be sufficient in the absence of implicit signals
(Bechara et al., 2005).
10 When considering impaired decision-making in the IGT, it is necessary to also discuss ‘unimpaired’ decision-making, as previously done by Cella and colleagues (2007) (Cella, Dymond, Cooper, & Turnbull, 2007). What complicates the study of impaired/unimparied decision-making as well is the fact that not only certain patient groups show abnormalities in their decision- making behavior on the IGT, but that the same is true for healthy controls. In the latter case, some healthy controls fail to indicate an understanding of the
IGT and fail to present a preference for good decks over the bad decks (Cella et al., 2007). In other words, there is an important variability that crosses boundaries between patients and healthy controls, a variability that is important to investigate. Further, it seems necessary to identify factors underlying decision-making behavior that lead to beneficial outcomes in the
IGT in healthy control groups. These findings can then be taken further to inspect the differences in patient behavior in the IGT (Cella et al., 2007). In the current study, we will therefore focus on decision-making in healthy individuals using the IGT.
11 1.3 Models of decision-making
Various theories have attempted to model choice behavior. These include psychological, neurobiological and economic theories (Ernst & Paulus, 2005).
1.31 The Somatic Marker Hypothesis (SMH)
One of the earliest and most prominent neurobiological theories is the
‘somatic marker hypothesis’ (SMH) developed by Bechara and colleagues in
1994 (Bechara et al., 1994). The SMH was aimed at modeling decision- making behavior on the basis of the IGT and offering a clarification of the processes involved in successful decision-making behavior (Bechara, Tranel, et al., 2000).
The SMH initially attempted to explain the aberrant decision-making behavior that can be seen in patients with lesions of the ventromedial prefrontal cortex when performing the IGT (Bechara et al., 1994; Bechara, Damasio, et al.,
2000). The SMH proposes that automatic and intuitive signals are crucial to guide choice behavior and ensure advantageous decision-making in uncertain conditions. These automatic bodily signals were termed ‘somatic markers’
(Bechara, Tranel, et al., 2000) (Bechara, Damasio, et al., 2000).
12 It is further suggested that a possible explanation of impaired decision-making involves a deficiency in these automatic bodily signals. Additionally is it stated that various structures and processes are necessary to allow advantageous decision-making, which in turn would allow the individual to obtain a beneficial outcome (Bechara & Damasio, 2005; Bechara et al., 2005).
Cognitive operations in the context of decision-making behavior are reliant on processes such as attention, emotion and working memory (Bechara et al.,
2005). It is presumed that the availability of knowledge about situations, options, outcomes and processes is crucial for any reasoning and decision- making operation. This knowledge is suggested to be mainly located in higher order cortical areas as well as few subcortical structures (Bechara, Damasio, et al., 2000). Additionally, this knowledge is assumed to be stored in what is termed ‘dispositional’ format. ‘Dispositional’ is defined as not topographically organized and can also be considered as implicit knowledge (Bechara,
Damasio, et al., 2000; Damasio & Damasio, 1994). Implicit knowledge can be transformed into explicit knowledge or actions through motor responses, covert emotional responses as well as images. There are further classifications, however these are presented elsewhere (Bechara, Damasio, et al., 2000).
13 As already discussed, the ventromedial prefrontal cortex appears to be an important structure that, among others, facilitates processing of knowledge and is thus beneficial for decision-making. Further, it provides an arrangement of links and connections amid bioregulatory states and different classes of factual knowledge. One important role of these regions is the value-outcome association, linking the value of options to previous outcomes (Behrens,
Woolrich, Walton, & Rushworth, 2007). These connections are held implicitly, and allow the possibility to reactivate an emotional state through activity of the appropriate brain structures (Bechara, Damasio, et al., 2000).
While the model above states that both implicit and explicit knowledge are necessary to guide and bias decisions (Bechara, Tranel, et al., 2000), other authors have proposed that the presence of explicit knowledge is enough to advance decisions in the right direction (Dunn, Dalgleish, & Lawrence, 2006;
Maia & McClelland, 2004). Utilizing the IGT, it was possible to confirm that in healthy individuals, a successful performance was possible when participants could show explicit understanding of the task (Guillaume et al., 2009).
However, these findings do not contradict the preeminent role of implicit processes as no interaction between explicit and implicit knowledge has been explored to date.
14 For instance, explicit knowledge may be possible only when adequate implicit markers are present. This is hypothesized to explain the observation that suicide attempters who understand the adequate strategy do not perform better than attempters who do not understand, contrary to patient controls and healthy controls (F Jollant, Guillaume, Jaussent, Bechara, & Courtet).
More research has to be conducted in this relationship between explicit and implicit processes, however it is necessary to first identify reliable implicit markers.
1.32 The Dual System Theory
Apart from the SMH, the discrimination between implicit and explicit system has been modeled in the dual-system theory (Evans, 2008). This theory proposes that reasoning as well as decision-making processes involve the activity of two different but interacting systems (Osman, 2004). These systems have been extensively studied by Kahneman and Tversky (1974) and have been named System 1 (for the implicit system) and System 2 (for the explicit system) (Kahneman, 2011).
System 1 and System 2 have different roles and functioning. Specifically,
System 1 is described as the system that operates without conscious control.
It is able to operate quickly and automatically. System 2 on the other hand underlies conscious control and operates more slowly than system 1
(Kahneman, 2011).
15 It is involved in direct attention to mental tasks and facilitates the computation of complicated problems, hence requiring a higher level of cognitive control and concentration (Kahneman, 2011). Moreover, what allows us to be conscious about ourselves, develop reasoning behind judgments made, our acts and beliefs are related to system 2. System 1 allows us to have intuitive feelings, implicit thoughts and perform instinctive acts (Kahneman, 2011).
Morewedge and Kahneman (2010) explain the functioning of system 1 and system 2 as ‘operating systems’ in the brain. Both systems demonstrate parallel workings and allow ‘data transfer’ among them (Morewedge &
Kahneman, 2010). The implicit system 1 is known to generate impressions based on what has been learned, on associative memory, or intuitions.
System 2 controls system 1, specifically: the impressions and intuitions produced by system 1. Through the interaction and communication of the two systems, we are able to learn, advance conscious and complex thoughts as well as react on intuitions (Evans, 2008; Morewedge & Kahneman, 2010). A variety of studies has utilized the dual system theory to investigate implicit and explicit learning and memory but also to consider other higher cognitive functions (Evans, 2008).
16 The current study relies on both the SMH and the dual system theory. We have focused on the study of implicit processes and the identification of implicit markers of decision-making. Among these implicit markers of decision-making, anticipatory signals that immediately precede choices are taken into account to assess whether these signals guide decision-making into an advantageous direction.
1.4 Measuring implicit processes of decision-making using electroencephalography (EEG)
To measure and quantify the implicit processes of decision-making, a reliable method with a high temporal resolution is necessary. In previous studies the measurement of skin conductance responses (SCRs) has been utilized.
However, this technique is prone to artifacts and does not allow to make clear predictions about areas in the brain active during the decision-making process, though it does offer the measurement of general arousal before choices are made (Bechara & Damasio, 2005). Considering functional MRI; this technique offers the possibility to obtain a better spatial resolution, however with limited temporal resolution. Due to these limitations, electroencephalography (EEG) has been chosen for this study as it enables measurements with a high temporal resolution and minimal artifact development (Bianchin & Angrilli, 2011).
17 EEG is a method that has been used extensively in clinical and research studies for over 80 years (Whittingstall & Logothetis, 2009). EEG is an inexpensive, non-invasive and generally safe method that offers the possibility to achieve recordings with a high temporal resolution of neural oscillations in the brain, which later allows for quantification and localization of the underlying active brain regions (Whittingstall & Logothetis, 2009). Due to these properties, this technique makes it possible to record very short time intervals during the complex decision-making process (Bianchin & Angrilli,
2011).
Few studies have considered decision-making processes using the IGT together with recordings of event related potentials (ERPs) using EEG. Three studies have utilized a similar set-up to the current study. All three studies are discussed below and differences to the current study are pointed out.
18 1.41 EEG and Decision-Making
Neural Correlates of Decision-Making on a Gambling Task
Carlson et al (2009) Child Development, June-Aug:80(4): 1076-98
This study by Carlson and colleagues considers individual variability found in the decision-making process of 74 8-year old children (38 boys and 36 girls).
Participants were asked to complete the Hungry Donkey gambling task that is similar to the IGT, while ERPs were measured using 21 electrodes. Several measurements were taken into account: response to feedback, anticipation effects prior to feedback and prior to choices (Carlson, Guthormsen, & Zayas,
2009).
As with the IGT, performance on the hungry donkey task necessitates a number of different skills and learning abilities. Participants have to be able to understand which door provides rewards and which door leads to higher losses. Within the learning process, children need to constantly update their working memory and simultaneously restrain from making impulse selections of doors, hence staying away from immediate high rewards. This task also requires the ability to develop a long-term thinking (Carlson et al., 2009).
19 The results obtained in this study provide an overview of what to expect when quantifying anticipation effects that immediately precede a choice. In their participant population, the authors reported anticipation effects prior to selections associated with short and long-term losses (vs gains), measures that were correlated with verbal abilities and cognitive performance. This study therefore suggests that anticipatory processes can be captured using
EEG, and are present in school-aged children (Carlson et al., 2009).
Neural Correlated of uncertain decision making: ERP evidence from the
Iowa Gambling Task
Cui et al (2013) Frontiers in Human Neuroscience, Nov: 15-7-776
Cui et al (2013) employed an adapted version of the IGT in 26 healthy participants and utilized 64 EEG electrodes. Results following the recording of
ERPs led to the assumption that anticipation effects before safe choices produced P3 waves with a larger amplitude in the left hemisphere. In contrast, anticipation effects prior to bad choices elicited larger P3s in the right hemisphere (Cui, Chen, Wang, Shum, & Chan, 2013).
To adequately describe further results it is necessary to know the precise set- up of their modified IGT. There are still four decks of cards in the game, however, participants do not decide to choose individually, rather they are offered to play or pass a decision made by the computer on either of the four decks (Cui et al., 2013).
20 Thus, in contrast to the original game, there are only two decisions to be made. Between these two decisions, Cui et al were able to find more negative potentials preceding a ‘pass’ compared to when participants chose ‘play’.
Hence, it was possible to also state that ERPs representing anticipatory effects bias and guide decisions in an advantageous direction. These results can also be taken as support for the Somatic Marker Hypothesis, stated above (Cui et al., 2013)
Decision Preceding Negativity in the Iowa Gambling Task: An ERP study
Bianchin M., Angrilli A., Brain and Cognition (2011) Apr:75(3):273-80
The task set-up in this study is very similar to the current study.
Bianchin and Angrilli (2011) have utilized the IGT and EEG to quantify the anticipatory effects prior to good and bad decisions. Specifically, they assessed a slow negative potential, that is termed decision-preceding negativity (DPN) that immediately precedes a voluntary (risky) decision
(Bianchin & Angrilli, 2011). This potential was assumed to be associated with a group of potentials termed ‘Readiness’ Potentials or ‘Bereitschafts’
Potential. Initially this potential has only been considered when motor action was involved, thus it was only expected to be found in areas activated during motor action. In the context of the investigations of Bianchin and Angrilli, this potential has been linked to willed economic decisions and has therefore led to analyses targeting not only in the area of motor activity but also in the frontal part of the brain, which is linked to higher cognitive action (Bianchin &
Angrilli, 2011).
21 In contrast to other previous studies (e.g. (Carlson et al., 2009; Cui et al.,
2013; Wessel, Haider, & Rose, 2012), Bianchin and Angrilli only consider a specific phase during the IGT, during which it is proposed that participants have already developed an understanding of the task and are able to make decisions explicitly, therefore losses or gains are already anticipated by participants (Bianchin & Angrilli, 2011). Considering our interest in implicit decision-making, it will be crucial to also take into account the early phase during which participants have not yet developed a more explicit knowledge of the IGT.
1.5 Decision-preceding negativity (DPN) as a marker of implicit anticipation processes of decision-making
The results obtained in the above-mentioned study present and discuss a negative potential (Decision-Preceding Negativity, DPN) that is present in anticipation of a choice that is actively made by a participant. It was found to be more negative prior to choosing from bad decks when compared to good decks (Bianchin & Angrilli, 2011). Activation maps, show higher activity
(represented as more negative potentials) in right frontal areas and less activity (represented as less negative potentials) in left central brain areas.
Negative potentials indicate a higher processing activity and the recruitment of more resources (Bianchin & Angrilli, 2011).
22 Activation in right frontal areas therefore matches these facts, indicating that bad choices necessitate the utilization of more resources (Bianchin & Angrilli,
2011) and more attention (Bianchin & Angrilli, 2011; Brunia & van Boxtel,
2001). Additionally, it can also be stated that findings of the study by Bianchin and Angrilli (2011) support the somatic marker hypothesis and provide additional information on the findings obtained by Bechara et al (1994, 2000) and Clark et al (2003) (Bechara et al., 1994; Bechara, Damasio, et al., 2000;
Clark, Manes, Antoun, Sahakian, & Robbins, 2003).
Though there are only a small number of studies that have used EEG in combination with the IGT to examine anticipatory decision-making processes,
DPN seems to be a valuable measure of anticipation effects that allow us to capture implicit learning and decision-making (Bianchin & Angrilli, 2011).
DPN is suggested to precede a willed risky decision and is present at
(between) -300ms to 0 and -200ms to 0 (Bianchin & Angrilli, 2011). The DNP is associated with a group of movement-related cortical potentials, comprising the Bereitschaftspotential (Jahanshahi & Hallett, 2003). The
Bereitschaftspotential was initially discovered by Kornhuber and Deecke in
1965 to describe changes in brain potential associated with passive movements contrasting with changes in brain potential associated with voluntary movements (Imhof & Fangerau, 2013). It was assumed that these potentials appear to reflect changes in activity located in the motor cortex, apparent about 1 to 1.5ms previous to any initiated movement (Imhof &
Fangerau, 2013).
23 It is therefore believed that these potentials indicate anticipation of movement.
However, there is a difference between both variations of movement indicating potentials (Jahanshahi & Hallett, 2003).
The Bereitschaftspotential is present when movement is self-paced and voluntary, the previously mentioned DPN on the other hand is apparent when movements appear as a result of a cue or signal (Jahanshahi & Hallett, 2003).
In the context of the investigations of Bianchin and Angrilli (2011) this potential has been linked to willed economic decisions and was found not only in the area in the brain underlying motor activity but also in the prefrontal part of the brain, which is linked to higher cognitive functions (Bianchin & Angrilli,
2011)
24 1.6 Objectives of the study and hypotheses
Based on previous results and the characterization of this potential, it is possible to consider the DPN a marker of crucial mechanisms implicated in the decision-making process. This can be reasoned as there seems to be a component of the DPN that is located in areas of higher cognitive processing
(Bianchin & Angrilli, 2011) and could thus be correlated with the SMH of decision-making.
In the current study we therefore aim to:
- Replicate previous findings of DPN
- Investigate how DPN influences decision-making
- Assess the mechanisms modulating DPN
This study in healthy individuals will be a first step before the investigation of patients with decision-making impairment. It is necessary to develop a better understanding of the processes involved in normal decision-making in order to comprehend aberrant decision-making behavior that is present in specific pathological conditions.
25 To meet our aims, we raised four main questions:
1. Can we identify a DPN in the brain? Is there a difference between DPN
preceding a risky vs safe option? – Based on previous results, we
hypothesized the existence of a DPN, with a difference between DPN
for risky vs safe choices.
2. Does DPN change over the course of the task? – It was hypothesized
that DPN is related to learning and changes over time.
3. Does DPN influence final decision-making performance? We
hypothesize that DPN is a crucial mechanism guiding choices and,
therefore, is associated with final decision-making performance.
4. Is DPN influenced by previous feedback (win/loss)? – It was
hypothesized that DPN is related to previous outcomes as part of the
learning process.
26 2. Materials and Methods
2.1 Participants
Thirty-five right handed healthy participants were recruited in this study (17 males and 18 females, mean age: 27.31 years old (males: 29.53y, females:
25.2), highest education level: Graduate Diploma obtained: 6 participants, additionally, University Undergraduate Education (current/complete): 33 participants, High School diploma complete/incomplete: 2 participants).
For the analysis performed addressing question 2, two participants had to be excluded due to an insufficient number of trials in individual blocks. For the analysis performed addressing question 4, eight participants had to be excluded due to coding problems.
All participants were informed about the aim and procedure of the study before starting and signed the consent form. This study has been approved by the Douglas Institute Research Ethics Board.
Participants were recruited through an add in McGill Classifieds. Eligibility to take part in the study was determined through pre-screening on the phone or through e-mail and in the interview session during the experiment.
27 Inclusion criteria:
- Men and women.
- aged between 20 and 50 years old to limit the heterogeneity due to
different developmental processes (e.g. maturation in adolescents and
aging in older population).
- right handed (according to the Edinburgh handedness inventory
(Oldfield, 1971).
- English speaking
Non-inclusion Criteria:
- personal lifetime history of major psychiatric disorders as measured by
the lifetime MINI 6.0. according to DSM-IV-TR criteria (Sheehan et al.,
2006) including depressive disorders, bipolar disorders, schizophrenia,
alcohol or substance abuse/dependence.
- personal history of either central nervous system disorders or major
head trauma.
- current pharmacological treatment that may interfere with cognitive
processes.
28 2.2 Socio-demographic and clinical assessment
The following information was collected from a short interview, specific questionnaires and tests. All these tools are routinely used in studies conducted by the McGill Group for Suicide Studies.
- Socio-demographic: age, gender, level of education, ethnicity;
- Current physical status: height, weight, smoking status, current
menstrual phase;
- The National Adult Reading Test (NART) for the assessment of verbal
IQ (Willshire, Kinsella, & Prior, 1991);
- Hamilton Rating Scale for Anxiety (HAM-A)(Hamilton, 1959);
- Barratt’s Impulsivity Scale (BIS-11)(Barratt, 1965);
- Spielberger State trait Anger Inventory (Forgays, Forgays, &
Spielberger, 1997);
- Spielberger Anxiety Inventory (Spielberger, 1983);
- Childhood Trauma Questionnaire (Bernstein et al., 1994);
- Brown-Goodwin Assessment of Lifetime History of Aggression (BGLHA)
(Brown & Goodwin, 1986);
- Quick Inventory of Depressive Symptomatology – Self Report (QIDS-
SR)(Rush et al., 2003);
29 After the sociodemographic and clinical interview, the experiment comprised a neuropsychological interview and an EEG session.
After completion of the experiment, participants received $50 as compensation for their time. Compensation was not based on their performance on the task.
30 2.3 Experimental Task:
Iowa Gambling Task (IGT)
Participants perform the task on a laptop. Before the start of the game, the rules are read out loud to each participant and the goal of the game is stated.
Each participant received the same instructions as follows:
‘On the screen in front of you, you will see 4 decks of cards labelled A, B, C and
D. After a few seconds, a message will appear that asks you to pick a card. You
will t have 3 seconds to click on one of the decks of cards and it will be up to you
to decide which deck you pick a card from.
Every time you pick a card from deck A, B, C or D, the computer will tell you that
you have won some money. I don’t know how much money you will win, but you’ll
find out as you go along. Every time you win, the green bar at the bottom of the
screen gets bigger.
Every so often, however, when you pick a card, the computer will tell you that you
have lost some money. I don’t know when or how much you will lose, you will find
out as you go along. Every time you lose, the green bar at the bottom of the
screen gets smaller.
You are absolutely free to switch from one deck to the other at any time, and as
often as you wish.
The goal of the game is to win as much money as possible and, if you find
yourself unable to win, make sure you avoid losing money as much as possible.
You won’t know when the game will end. You must keep on playing until the
computer stops.
You will have a $2000 credit, as you can see on the green bar at the bottom of
the screen, to start the game. The red bar at the bottom of the screen is a
reminder of how much money you borrowed to play the game.
31 It is important to know that the computer does not make you lose money at
random. However, there is no way for you to figure out when or why you lose
money. All I can say is that you may find yourself losing money on all of the
decks, but some decks will make you lose more than others. Even if you lost a lot
of money, you can still win if you stay away from the worst decks.’
During the game, participants have to choose among 4 decks of cards to make as much money as possible. When they click on a deck, they may win or lose money. What they do not know is that 2 decks are advantageous in the long-term i.e. they yield small gains but lower losses (Decks C and D), i.e. leading to long-term gains, and two decks are disadvantageous yielding high attractive gains but even higher loses (Deck A and B) (Bianchin & Angrilli,
2011). Decks of the same kind present losses and gains with the same frequency and lead to the same net loss or gain. Wins and losses within each deck are randomized. The task is also designed to create a feeling of uncertainty and to prevent, to a large extend, conscious calculation of outcomes. Additionally, participants do not know how many trials the task comprises and are instructed to play until the game ends, (Bechara et al.,
2005) which in our case is after 200 trials. The trial number was increased to
200 trials in our study (as compared to 100 trials in the original game) so that it was possible to obtain recordings of evoked potentials with sufficient power.
Participants were instructed to make choices using the keys 1, 2, 3 and 4 to choose decks A, B, C and D respectively. They had 3 seconds to choose one deck (click keys 1, 2, 3 or 4).
32 Immediately after choosing, participants are shown whether they have won or lost money. Following feedback a crosshair is shown which lasts a minimum of 3 seconds to a maximum of 4.5 seconds (randomized), plus the time that was not used from the time allotted to choosing a deck.
The IGT set-up for one trial is presented in Figure 1. Specific time intervals are annotated.
Figure 1
Fig. 1: Figure 1 illustrates the IGT set-up for one trial. The blue arrow indicates when participants are making a choice (clicking on a particular deck). A time interval of 500ms prior to the choice was chosen for the investigation of the Decision-Preceding Negativity (DPN).
33 After every 50th choice, the game pauses and participants were asked the following question: ‘Tell me what you understand of the game’. Answers were noted. At the end of the 200 trials participants were asked three additional questions:
(1) Did you find differences between the decks?
(2) If you had to select ten new cards from deck A/B/C/D, would you earn
on average or would you lose money?
(3) In order to win money, which deck would you choose?
Based on the answers, explicit understanding of the task was coded in: ‘no understanding’, ‘partial understanding’, or ‘full understanding’. This variable was not investigated in this thesis and will be the topic of future analyses.
At the end of the EEG session, participants received feedback about their performance and answers to any questions.
34 2.4 EEG recording
EEG measurements were carried out while the participant performed the IGT.
The EEG was recorded with 62 Ag/AgCl ActiCap electrodes, placed according to the 10-20 system (American Electrophysiological Society, 1994), attached to an elastic cap. One additional electrode was placed below the right eye to monitor ocular movements. Impedance was reduced to below 25kΩ, however was kept below 5kΩ for each electrode during recording (Brain Products Inc.).
The sampling rate was set to 1000Hz and the EEG was amplified 20,000 times using a BrainAmp DC amplifier (Brain Products Inc.). The recording continuously was monitored using Brain Vision Recorder (Brain Products Inc.) software.
2.5 Data Analysis - Behavioral Data
1. IGT net scores were calculated as the difference between the number
of choices from advantageous minus disadvantageous choices for 10
blocks of 20 trials.
2. To be able to assess choice behavior, the percentage of good and bad
choices was calculated and assessed using a repeated measures
ANOVA. Firstly, the ANOVA has been set up for the analysis of 10
blocks of 20 choices, considering within subject factor ‘Block’ (10
levels) and ‘Choice’ (2 levels). Secondly, the factor ‘Block’ (10 levels)
was kept and ‘Choice’ (4 levels – each individual deck) was added,
allowing the investigation of choice behavior (percentage of choices)
for each individual deck. For post hoc effects, t-tests were performed.
35 EEG Data
Electrodes have been chosen according to previous literature and activity maps. Based on previous findings by Bianchin and Angrilli (2011), the predominant regions of interest are in the frontal and central areas of the brain, allowing us to gain information about activity in brain regions devoted to higher cognitive processes (frontal left and right) as well as motor areas
(central left and right). Chosen electrodes are highlighted in Figure 2.
Figure 2
Figure 2: Figure 2 illustrates the electrodes considered for analysis. The location highlights the regions of interest in the frontal left and right and central left and right areas of the brain that will allow us to investigate DPN.
36 2.6 EEG Analysis
EEG data were analyzed and pre-processed using BrainVision Analyzer 2
(Version 2.0.2) (Brain Products; 2012). Offline high and low pass filters were set to cut-offs of 0.01Hz and 100Hz (12dB/oct), respectively, and independent component analyses were used to remove eye blinks and ocular movements.
Based on previous studies, we investigated the EEG component corresponding to the immediate anticipation of choice (DPN) as well as the
DPN when the previous feedback is taken into account. Several time intervals have been considered in previous studies. The time intervals considered in this study was chosen according to Bianchin and Angrilli (Bianchin & Angrilli,
2011), but also based on activation maps obtained from the original data. The anticipation phase has been described as a Decision-Preceding Negativity
(DPN). Time interval for DPN was set at -500 to 0ms before clicking on decks.
For all measurements 200 trials are taken into account in the first set of analyses and subsequently the first 100 trials and second 100 trials are considered separately to investigate the effect of time.
Analysis methods have been chosen according to previous literature and based on the question considered. For each of the main questions raised, analyses methods are described.
37 1. Can we identify a DPN in the brain? Is there a difference between
DPN preceding a risky vs safe option?
A repeated measures ANOVA was used with the factors: Hemisphere (2
Levels: Left and Right), Electrode (8 Levels) and Choice (2 Levels: Risky and
Safe) for all 200 trials. In the case of interactions, the results were decomposed further utilizing the same analysis with reduced levels and post- hoc t-tests (corrected for multiple comparisons).
2. Does DPN change over the course of the task?
For this analysis, we considered two blocks of 100 trials.
A repeated measures ANOVA was conducted with a set-up very similar to what has been described above, with an added within-subjects factor of Block
(2 Levels: Trials 1-100 and Trials 101-200). In the case of interactions, the results were decomposed further utilizing the same analysis with reduced levels and post-hoc t-tests (corrected for multiple comparisons).
3. Does DPN influence final decision-making performance?
DNP preceding good choices and preceding bad choices were correlated with the IGT Net Score for all electrodes over 200 trials using Pearson correlations. Secondly, DNP before advantageous choices minus disadvantageous choices was correlated with the IGT net score over 200 trials, with the hypothesis that it is not the disadvantageous choices or
38 advantageous choices that matters, but the contrast between both. The same analysis was run for two blocks of 100 trials, to indicate the change over time.
This analysis will show which individual electrodes recording a DNP that is most strongly associated with the IGT Net Score (i.e. the learning effect).
4. Is DPN influenced by previous feedback (win/loss)?
In the pre-processing steps of this analysis, new markers have been coded that took into account the feedback participants have received before the choice that is being assessed. This processing step led to four different markers. Two out of these four markers are being considered in the analysis, these are: Choices (risky/safe) after win and choices (risky/safe) after loss.
The analysis set-up to investigate if DNP changes according to feedback received was similar to what has been described above. A repeated measures ANOVA with the factors Hemisphere (2 Levels: Left, Right),
Electrode (8 Levels) and Choice (2 Levels: Good, Bad) were conducted for
200 trials. In the case of interactions, the results were decomposed further utilizing the same analysis with reduced levels and post-hoc t-tests (corrected for multiple comparisons).
39 3. Results
3.1 Behavioral Data
Firstly, behavioral results are considered to gain an overview of participants’ behavior. Figure 3 illustrates choice behavior of participants during the task.
The percentage of risky choices is considered over time (200 trials) in 10 consecutive blocks of 20 choices. Participants significantly changed their choice behavior and learned to select less often from the risky decks
(Block*Risky Decks: F(9,288)= 8.031, p<0.0001).
Figure 3
Figure 3: Figure 3 illustrates choice behavior of participants over time. The graph shows the percentage of risky choices over 200 trials in 10 block of 20 trials. Participants changed their behavior over the course of the game and learned to choose less from the risky decks than the safe decks.
40 To consider participant behavior for each deck individually, a similar analysis was performed (Figure 4) and allows us to state that after the first block, participants chose significantly more often from deck C and D than A and B
(Deck*Block: F(27,918)=12.055, p<0.000). There was no significant difference between deck A and B or between C and D. However overall, it seems that deck C is chosen slightly more often than deck D.
Figure 4
Figure 4: Figure 4 illustrates participant choice behavior in more detail, showing the percentage of choices from each individual deck over time in 10 blocks of 20 choices. It can be seen that there is a significant difference of choice behavior for choices from deck A and B and C and D and between block 1 and the remaining blocks. There is however no significant difference between the percentage of choices from deck A and B or between C and D, though C seems to be slightly more preferred when compared to D.
41 3.2 EEG Data
Can we identify a DPN? Is there a difference between DPN preceding a good/bad decision?
Considering the first question, it can be answered positively as a DPN can be identified in the brain. Figure 5 illustrates a DPN that is located in the frontal right hemisphere (electrode Af4). Moreover, it can be seen that the potential is more negative preceding a disadvantageous or risky choice (red line) when compared with an advantageous (safe) choice (black line). Thus it is possible to state that on the right frontal hemisphere, risky choices lead to a higher activation in the brain, indicating an increased processing, than when compared to an advantageous choice.
Figure 5
Figure 5: Figure 5 illustrates a DPN located on the right frontal hemisphere of the brain. It can be seen that the negative potential is more negative preceding a bad (disadvantageous) choice (red) than when preceding an advantageous (safe) choice (black).
42 To explore the second part of the question (is there a difference in DPN for good/bad choices) an analysis of DNP over the 200 trials has been performed. Considering EEG data and activation map, it is possible to show the difference between bad and good choices for 200 trials of the IGT (Figure
6). The activation map indicates the difference in mean voltage between the two choices (risky and safe). It can be seen that there is a difference present in the hemispheres, showing an activation of the frontal and central right part
(shown in blue, and indicated through a negative mean voltage), and a deactivation of the frontal and central left part of the hemisphere (shown in red and yellow which indicate a positive mean voltage). Further, the activation shown through the negative mean voltage indicates a greater processing in these areas.
43
Figure 6
Figure 6: Figure 6 shows the activation map for the time frame of 500ms to 0 (when participants actively click on a deck). The difference between safe and risky choices is illustrated for the total of 200 trials. A hemispheric difference can be seen, specifically showing a greater activity in the right frontal and parietal hemisphere (blue) and less activity on the left hemisphere in the frontal and central part of the brain (red/yellow).
A significant Interaction for Hemisphere*Electrode*Choice (F(8, 272)=6.940, p<0.001), as well as a significant interaction for Hemisphere*Choice
(F(1,34)=8.169, p<0.008 was found. Hence, depending on the choice made, there is a significant difference between the hemispheres. Taking the DPN into account, it is indicated that the difference in DPN before risky and safe choices is in the opposite direction between the left and right hemisphere. On the right hemisphere, disadvantageous choices are reflected in a potential that is more negative than when compared to advantageous choices.
44 The opposite is true on the left hemisphere, however the location within the hemisphere has to be taken into account as well.
Further decomposition revealed a significant interaction between
Electrode*Choice (F(8,272)=4.341, p<0.001) for the left hemisphere and a significant interaction between Electrode*Choice, F(8,272)=2.968, p<0.004, as well as a significant main effect for Choice (F(1,34)=5.541, p<0.025) for the right hemisphere. Hence, it can be stated that there is a difference in DPN between good and bad choices, which is presented as a significant voltage difference. A post hoc t-test revealed the following results: Electrodes C5, Af4,
C4, Cp2 and Tp8 showed strong trend indicating differences between good and bad choices (corrected for multiple comparisons).
Does DPN change during the task?
When considering whether DPN changes over time, from the beginning of the task towards the end of the task, a separate analysis for two blocks of 100 choices each, was conducted. Significant interactions for
Hemisphere*Electrode*Choice (F(8,216)=3.692, p<0.001) as well as
Choice*Block (F(1,27)=5.605, p<0.03) and Electrode*Block (F(8,216)=3.856, p<0.001) were found, suggesting that choice behavior differs according to block as well as voltage at individual electrodes.
Further it can be assumed that choice behavior varies with block on individual hemispheres. To confirm these assumptions, further decompositions are performed.
45 For the left hemisphere, a significant effect for block (F(1,27)=0.05 as well as interaction effects for Electrode*Choice (F(8,216)=2.540, p<0.02 and
Electrode*Block (F(8,216)=2.468, p<0.02) was found. On the right hemisphere, significant interactions for Electrode*Choice (F(8,216)=2.005, p<0.05) and Choice*Block (F(1,27)=5.556, p<0.03) were shown. Choice behavior varies with block on the right hemisphere when compared to the left hemisphere. On the right hemisphere, choice behavior within the first block shows a significant interaction effect with electrode (F(F8,256)=4.298, p<0.006), and in the second block a significant main effect for Block
(F(1,27)=4.832, p<0.04).
Consequently, the difference in choice for good and bad decks is stronger in the second block. This can also be seen in higher voltage differences previous to the choice of bad decks when compared to good decks. This is not the case for the first block. On the left hemisphere this significant difference cannot be observed, however what can be noted is the fact that there is a strong difference for good decks between blocks in the frontal part of the hemisphere. Figure 7 and 8 illustrate the change in voltage difference for bad decks (Figure 7) and good decks (Figure 8) in electrode Fc5, located in the frontal part of left hemisphere.
DPN during the first block (choices 1-100) are illustrated with the black line.
DPN during the second block (choices 101-200) are shown in red.
46 A post-hoc t-test has indicated a significant difference for good decks between blocks; this difference however did not survive multiple comparison corrections. Despite the latter, the change in voltage over the course of the task can be seen, indicating an increase in negativity in the second block of the task.
Figure 7 Figure 8
Figure 7 (left): Figure 7 illustrates a DPN preceding a disadvantageous choice in the first block (black line) and in the second block (red line). Figure 8 (right): Figure 8 shows a DPN preceding an advantageous choice in the first block (black line) and second block (red line). What can be seen is a DPN that shows a greater negativity for both choices. The difference for advantageous choices between block approaches significance when correcting for multiple comparisons.
47 Does DPN influence final decision-making performance?
There was no significant correlation between DPN before advantageous or before disadvantageous choices and IGT net score over 200 trials (Figure 9).
However, there was a significant correlation for the difference in DPN and IGT net score for electrode F1 (p=0.044) and Tp7 (p=0.014). For the first block of choices, significant correlations were found for Cp6, F1 (p<0.05) and F7
(p<0.02). For the second block significant correlations were found forAf4, Fp1,
Cp5, Ft8, Fc5, and Tp7 (all p<0.05). However, none of these correlations survived correction for multiple comparisons.
Figure 9
Figure 9 shows the electrodes correlated with the IGT net score. The association seems stronger during the second half of the task. Results are not corrected for multiple comparisons.
48 Is DPN Influenced by previous feedback (win/loss)?
For the analysis of the last question, previous feedback was taken into account when considering the current choice. Two different scenarios are possible and analyzed for. The two scenarios are illustrated in Figure 10.
Figure 10
Figure 10: Figure 10 illustrates the analysis set-up for the last question raised. The current choice is always preceded by a feedback that can either be positive (win) or negative (loss). For the analysis the current choice (advantageous/disadvantageous) after loss is analysed separately from the current choice (advantageous/disadvantageous) after win.
49 Choices after Win:
A significant three-way interaction between Hemisphere*Electrode*Choice
(F(8,208)= 6.236, P<0.001) was found for Advantageous/Disadvantageous after win. Hence it can be assumed that DPN is different according to hemisphere. There was also a trend for a significant main effect of choice
(F(1,26)=3.481, p<0,074). Further decomposition was conducted by hemispheres: On the right Hemisphere a significant main effect of choice
(F(1,26)=6.474, p=0.017) was shown and a significant interaction for
Electrode*Choice (F(8,208)=2.882, p=0.005). On the left hemisphere, only a significant interaction was found for Electrode*Choice (F(8,208)=4.361, p=0.006).
Figure 11 illustrates the results for choices after win for the right hemisphere and Figure 12 shows results for the left hemisphere. While a higher voltage is apparent in the left frontal hemisphere, there is a significant difference present in the right frontal hemisphere between disadvantageous choices and advantageous choices. In the central part of the left hemisphere is a significant difference present before choosing from good and bad decks
(denoted with star). Good choices are shown in green and bad choices are shown in red.
50 Figure 11
Figure 11: Figure 11 shows the mean voltage of the DPN preceding a bad (red) and good (green) choice immediately following a win on the right hemisphere. It can be seen that there is a significant difference between the DPN for good and bad choices in the right frontal hemisphere (electrode F6) and central hemisphere (electrode Cp2) (blue stars). Error bars correspond to the first and third quartiles.
Figure 12
Figure 12: Figure 12 shows the mean voltage of the DPN preceding a bad (red) and good (green) choice immediately preceding a win on the left hemisphere. It can be seen that there is a significant difference between the DPN for good and bad choices in the central part of the left hemisphere denoted with a blue star. Error bars correspond to the first and third quartiles.
51 Choices after Loss:
There was a significant interaction for Hemisphere*Electrode*Choice
(F(8,208)= 3.259, p=0.002), as well as interactions for Hemisphere*Electrode
(F(8,208)=2.692, p=0.008) and a trend for Hemisphere*Choice
(F(1,26)=3.727, p=0.065). Decomposition led to a significant interaction for
Electrode*Choice (F(1,26)=2.673, p=0.008) for the left hemisphere and an interaction for Electrode*Choice (F(8,208)= 2.111, p=0.036) for the right hemisphere. Both hemispheres comprise electrodes associated with choice.
Further decomposition however did not lead to any significant results, suggesting no significant effect of loss on DPN. Figure 13 and 14 illustrate the mean voltages obtained of the DPNs preceding good (shown in green) and bad (shown in red) choices. Voltages seem to be slightly higher in the left frontal hemisphere, however both hemispheres are associated with differences in choice.
52 Figure 13
Figure 13: Figure 13 shows the mean voltage of the DPN preceding a bad (red) and good (green) choice immediately following a loss on the right hemisphere. It can be seen that there is no significant difference between the DPN for good and bad choices. Error bars correspond to the first and third quartiles.
Figure 14
Figure 14: Figure 14 shows the mean voltage of the DPN preceding a bad (red) and good (green) choice immediately following a loss on the left hemisphere. It can be seen that there is no significant difference between the DPN for good and bad choices. Error bars correspond to the first and third quartiles.
53 Figure 15 and 16 illustrate DPN before good and bad choices obtained from the right frontal hemisphere (electrode F6), choices following win (Figure 15) and choices following losses (Figure 16). While it is clearly visible that there is a difference in voltage for DPN preceding a good choice (red) and a bad choice (black), this difference in voltage for the choices is significant for choices after win (Figure 15) not so for choices after loss (Figure 16). Wins therefore seem to reinforce the difference in DPN between risky and safe decks. This is not the case for choices after loss.
Figure 15 Figure 16
Figure 15: Figure 15 illustrates choices after win at electrode F6 (right frontal hemisphere). Figure 16: Figure 16 illustrates choices after loss at electrode F6 (right frontal hemisphere). It can be seen that there is a difference in the negativity of the potential observed. In Fig. 15, advantageous decks (red) show a significantly less negative potential when compared to disadvantageous choices after win (black). Considering figure 16, the contrast observed in figure 15 cannot be seen anymore. Disadvantageous choices after loss seem to not elicit a significantly different potential when compared to advantageous choices after loss.
54 4. Discussion
The principal aim of this study was to further investigate implicit markers of decision-making in conditions of uncertainty with a particular emphasis on the anticipatory processes, i.e. the processes immediately preceding a choice. To meet our aims, we intended to replicate and extend previous results examining DPN, a negative potential preceding a willed risky choice (Bianchin
& Angrilli, 2011).
In this study, as in previous studies, the Iowa Gambling Task (IGT) as a measure of decision-making (Bechara et al., 1994) was utilized while EEG recordings were made. The IGT is a unique task for investigating decision- making processes, as it is not possible to precisely foresee the outcome following participants’ choice. This is because individual trials are considered unpredictable for participants, especially in the beginning of the game
(Bechara et al., 2005). A possible way that allows subjects to minimize the unpredictability of the game is to develop ‘approximate estimates of probability’ (Bechara et al., 2005; Bianchin & Angrilli, 2011), that have also been considered as ‘hunches’ by Bechara and colleagues (Bechara, 1997;
Bianchin & Angrilli, 2011). A hunch or ‘approximate estimate of probability’ would rely on the generation of an unconscious and simultaneously complex, intuitive signal (Bechara, 1997). The IGT allows the examination of these processes (Bechara, Tranel, et al., 2000; Bianchin & Angrilli, 2011).
55 Analyses have been performed to adequately capture the crucial time interval right before a decision is made. During this time interval a specific negative potential was found, the decision-preceding negativity (DPN) (Bianchin &
Angrilli, 2011), which has been investigated. Specifically, our main interest was 1) the difference in DPN before a good or bad choice on the basis of previous findings using SCRs (Bechara et al., 2005; Guillaume et al., 2009),
2) if there is a change of DPN over time to investigate the learning effect and
3) if previous feedback has an influence on DPN as one mechanism underlying the generation of DPN.
To summarize our findings: DPN was 1) confirmed in several frontal and central brain regions, 2) more pronounced before risky compared to safe choices, 3) changing during the task suggesting a learning effect, and 4) modified by previous feedback, specifically wins.
The origin of these potentials are depolarized apical dendrites, that are located in the upper cortical areas (Birbaumer, 1999). Depolarizations arise through synchronous firing mechanisms and are known to establish a mechanism utilized for threshold regulation of the cortical networks involved in action execution (higher cortical networks as well as motor areas).
Specifically, excitatory mechanisms are expressed through negative slow potentials, inhibitory mechanisms on the other hand are elicited by positive slow potentials. It is possible for humans to control these slow potentials
(Birbaumer, 1999).
56 Training is necessary that relies on feedback as well as mechanisms that reinforce positive experience. Post-training, it is likely to improve actions and cognitive performance. During and after the training process, cortical negativity decreases. Areas involved in this training mechanism have been pinpointed as prefrontal mechanisms that were shown to be involved in attention (Birbaumer, 1999). The results we have obtained can be related back to these training mechanisms and can be linked to the SMH, discussed in the introduction and the change observed over time.
Can we identify a DPN in the brain? Is there a difference between DPN preceding a risky vs safe option?
The first question has been researched previously by Bianchin and Angrilli
(Bianchin & Angrilli, 2011) as well as Carlson et al (Carlson et al., 2009). Both groups considered the anticipation effects of decision-making in a very similar set-up utilizing the IGT. Taking into account the results obtained in the current study, it is possible to confirm the existence of a DPN in certain brain areas.
Specifically, the DPN shows a difference depending on location in the brain and laterality. More importantly however, a greater negativity, hence activation for disadvantageous decks was found in the right frontal and central hemisphere. With that, a reduced negativity/increased positivity, indicating an inhibition in left central areas can be shown for disadvantageous decks
(Bianchin & Angrilli, 2011).
57 Considering the difference in amplitude and consulting previous literature, it is suggested that the amplitude of slow wave pattern associated with a particular task could be dependent on resource allocation (Rösler, Heil, & Röder, 1997).
Hence, larger amplitudes of potentials observed can be linked with the recruitment of more resources of a specific kind (Rösler et al., 1997).
Additionally, it is known that these slow wave patters arise with a ‘task-specific topography’. The location is correlated with the areas that are specifically involved in computing a certain task (Rösler et al., 1997). As mentioned above, amplitude is a variable of the slow wave negative patterns that is dependent on resource allocation. With that, the correlation is also extended to the strain the active cortical system is under, hence the difficulty of the task performed. Difficult tasks require the involvement of more processing units
(resources) and are presented with a more negative amplitude (Rösler et al.,
1997). Lastly, slow wave potentials will change their pattern throughout a task.
This happens when the pattern of brain areas activated changes (Rösler et al., 1997).
Relating this information back to the results obtained in the current study, it appears that processing of choices in the IGT is preceded by anticipatory mechanisms or hunches. These hunches work as a subconscious bias of choice behavior over time and in the right direction (Bianchin & Angrilli, 2011).
Considering the individual potentials observed before negative and positive decks are chosen: a more negative amplitude indicated a greater processing and the allocation of more resources before a bad deck is picked, when compared to a good deck (Bianchin & Angrilli, 2011).
58 The allocation of a greater number of resources indicates a difficult task as well as the pattern of brain regions activated. Specifically the activity of frontal areas led to assume that a greater processing takes place before negative choices (Rösler et al., 1997) which then bias the choices made towards beneficial decisions (Bechara et al., 2005).
Specific areas in the prefrontal cortex have been researched previously by
Bechara et al (2000) as well as Fellows and Farah (2007) and Bianchin and
Angrilli (2011), to name a few. Fellows and Farah (2007) have investigated the ventromedial prefrontal cortex and its significance in decision-making. It was found that this region was particularly involved in ambiguous as well as certain decision-making (Fellows & Farah, 2007). While the ventromedial prefrontal cortex area considered in the initial experiments performed by
Bechara et al 1997), Fellows and Farah showed decision-making inconsistencies in individuals with ventromedial prefrontal cortex damage in conditions of certain as well as uncertain decision-making (Fellows & Farah,
2007). Bianchin and Angrilli (2011) discuss the activity of the dorsolateral medial-prefrontal cortex and orbitofrontal cortex during the IGT (Bianchin &
Angrilli, 2011). Activity in the medial prefrontal cortex together with the amygdala can be correlated with the somatic marker hypothesis as it represents an area involved in the control of the Autonomic Nervous System.
Activation of this system is in turn involved in the generation of skin conductance responses (Bianchin & Angrilli, 2011).
59 Does DPN change over the course of the task?
DPN changes with time, suggesting a learning effect that manifests itself over the course of the task. At the beginning of the task, there is a difference in voltage present for good and bad choices. The contrast however, appears to be less than significant in most brain regions considered. In the second half of the task, this contrast is more pronounced and for certain areas and specific electrodes a significant difference in voltage could be detected. It can also be seen that the choice of bad decks evokes more negative potentials later on in the task than at the beginning, indicating a learning effect through a manifested difference between DNP for good and bad choices (Bianchin &
Angrilli, 2011).
The somatic marker hypothesis, as discussed, suggests the presence of so- called skin conductance responses (SCRs) that are apparent before choices are made(Bechara, Damasio, et al., 2000). These SCRs are more prominent before a bad choice when compared to a good choice. Similar to this, the negative amplitude of the DPN is more pronounced before a bad choice when compared to a good choice. Over time, SCRs change, decrease further for good choices and increase slightly to a plateau for bad choices (Bechara,
Damasio, et al., 2000).
60 The results obtained in the current study show similar patterns. It is therefore possible to state that the current results provide support of the somatic marker hypothesis, and with that the presence of non-conscious mechanisms or hunches that precede a willed (risky) economic decision and are apparent to guide choices in a right direction (Bechara, 1997; Bechara et al., 2005;
Bianchin & Angrilli, 2011) that are confirmed through the bigger and significant difference in voltage between good and bad choices at the end of the task compared to the beginning.
What is discussed above is the ‘training effect’ that can be seen when regarding event-preceding potentials such as the DPN. This training effect that is possible through the integration of feedback allows an improvement of behavior and cognitive performance (Birbaumer, 1999). Both can be shown through the increased difference in DPN for good and bad choices over time and an improvement in performance over time that has been confirmed through behavioral results. However what is to note is the fact that the training effect is described as leading to a decrease in cortical negativity (Birbaumer,
1999). This is only observed for good choices not for bad choices.
61 Does DPN influence final decision-making performance?
To answer this question, the differential DPN and the IGT net score have been utilized. The Net Score reflects the global performance of each participant, expressed through subtracting the number of bad choices from the number of good choices. A positive result indicates good performance
(choosing more from safe than risky decks); a negative result indicates bad performance. The differential DPN is calculated through subtracting the voltage recorded for disadvantageous choices from the voltage obtained for advantageous choices. The results provide an overview of the individual electrodes associated with final performance.
For 200 trials, the effect can be considered not strong enough. The location of the electrode most strongly associated with final performance, in the frontal left hemisphere, does not match with the previous results obtained in this study and has to be considered carefully. Regarding the first 100 and the second 100 trial separately leads to different results. While in the first block the frontal left and central right hemisphere comprise electrodes more strongly associated with final performance, the second block shows results that match more closely with our other results. Areas with electrodes showing significant correlations with final performance are found in the frontal right and central left hemisphere. Hence there is a difference over time and there are areas that indicate processing of motor responses.
62 However, what needs to be considered with caution is the fact that none of these correlations are significant after corrections for multiple comparisons
(although this may not be necessary in this very exploratory analysis).
Another important fact to take into account with these measurements has to do with the general set-up of EEG. EEG is a technique comprising two reference electrodes that also function as recording electrodes. Thus, correlations that are done when a common reference is present are more prone to significant results, as individual measurements are correlated by nature.
It can be stated that these result confirm the difference in activation of brain areas between the two blocks of the IGT, however more specific statements need to be made with caution.
Is DPN influenced by previous feedback (win/loss)?
When considering the difference in DPN between good and bad choices, two additional scenarios have to be discussed and with that the results obtained.
Before each choice a feedback is presented to each participant, this feedback can be positive indicating a win, or negative indicating a loss. The effect of feedback can be regarded as learning through reinforcement. Our specific interest is if the DPN changes according to the feedback preceding the DPN.
63 The two scenarios are: choices after a win and choices after a loss. The results obtained in the current study indicate a difference between DPNs for both scenarios. DPN for choices after win reflect a greater difference between good and bad choices in the frontal right hemisphere. Comparing these results with the results obtained for choices after loss, it can be noted that the contrast in DPN in the right frontal hemisphere does not indicate significant differences between safe and risky choices.
The difference in DPN observed for choices after win reflects a reinforcement effect of good choices after a positive feedback and a greater processing for bad choices after positive feedback. The more negative DPN before bad choices after win also indicates an activation of a greater number of resources and a more intense processing (Birbaumer, 1999). Those processes are the
‘hunches’ (Bechara et al., 2005) as previously discussed, that bias choice behavior towards a more beneficial decision-making and also advance explicit learning (Bechara et al., 2005) through reinforcing good choices after positive feedback (a decreased activation of resources and less intense processing
(Bianchin & Angrilli, 2011; Birbaumer, 1999)).
64 Hence, the DPN guides choices (Bianchin & Angrilli, 2011) and reinforces choices through differences in DPN. This is however not the only point to notice: negative choices after a negative feedback still elicit a more negative potential than a positive choice after a negative feedback, however the difference is not significant. It can be assumed that in the case of a bad choice, the processing system has detected an event that appears to be against what is expected and elicits a bias against the choice (Cella et al.,
2007). For a good choice after a negative feedback, the system still needs to be activated as the previous behavior that led to the negative feedback should not be repeated. Hence a negative feedback seems to elicit stronger anticipatory effects representing caution for good and bad choices that follow that feedback (Bianchin & Angrilli, 2011; Cella et al., 2007).
With this set of results, the SMH can also be confirmed as it indicates the effect of anticipatory signals (also referred to as ‘somatic markers’ or
‘hunches’) in form of the DPN. It might even be possible to state, that this set of results extends the SMH further as the effect of feedback is shown and offers insight into the learning effect that is present during the IGT. The learning effect has been discussed extensively (Bechara et al., 2005; Bianchin
& Angrilli, 2011; Cui et al., 2013), however has only been shown through measurements of the DPN over time (Carlson et al., 2009). While the latter measurement also confirms the difference of DPN, considering the feedback together with the DPN allows the possibility to gain more insight into the actual impact the two kinds of feedback have on the choice that follows.
65 5. Limitations
This study was set up to investigate the DPN, immediately preceding willed- risky decisions. The task utilized to research the DPN is the IGT. The IGT has been considered the most widely used test to examine real-life economic decision-making in a laboratory setting, with the possibility to also assess emotional processing as part of the decision-making process (O. H. Turnbull,
Bowman, Shanker, & Davies, 2014). However, with these benefits of the IGT that allow us to set up studies like this, a number of limitations have to be taken into account as well.
The IGT is a highly complex task that does involve a number of processes beyond decision-making. The assumptions associated with the IGT have been criticized by a number of groups (Bowman & Turnbull, 2003; Dunn et al.,
2006; C. E. Evans, Kemish, & Turnbull, 2004; Maia & McClelland, 2004).
Specifically, the IGT is closely associated with the somatic marker hypothesis to assess abnormalities in complex choice behavior and emotional processing
(O. H. Turnbull et al., 2014). Emotional processing has been assigned a significant part in decision-making (Bechara, Damasio, et al., 2000) and with that emotion-based learning (O. Turnbull, Berry, & Bowman, 2003). Hence, two aspects need to be considered when utilizing the IGT. The complexity of the IGT itself (Dunn et al., 2006) and the somatic marker hypothesis closely associated with the IGT (Dunn et al., 2006).
66 Criticism on the IGT
The IGT has been shown to be quite robust, thus it allows the acquisition of
‘normal’ results even when certain aspects, such as wins and losses are changed from make-shift money to real money (Bowman & Turnbull, 2003;
Dunn et al., 2006), or the timing of inter-trial intervals are changed (Dunn et al., 2006). Additionally, through a number of studies, the IGT could be identified as a task offering the possibility to quantify impaired decision- making in different patient groups presenting with psychiatric and neurological abnormalities (Adida et al., 2011; Bazanis et al., 2002; F. Jollant et al., 2005).
Results from above mentioned studies make it possible to state that the IGT does in fact have some level of predictive validity (Dunn et al., 2006).
The IGT, has also been the target of intense criticism, predominantly targeting the ‘cognitive impenetrability’ as it is described by Dunn et al (2006) (Dunn et al., 2006) among others. ‘Cognitive impenetrability’ is defined by the assumption that the IGT could only be finished satisfactorily when somatic markers are present throughout the game, hence indicating emotion-based learning. Particularly at the beginning of the game, choices are made based on emotional hunches, rather than explicit understanding (Bechara et al.,
2005). Maia and McClelland (2004) argue against this assumption. They state that the schedule on which wins and losses are set-up in the IGT can be understood by participants more easily than assumed, hence explicit decision- making can be made soon after the task has started (Maia & McClelland,
2004).
67 Further, the questions that are asked during the IGT, and that represent a crucial part of the task, in the original study by Bechara et al (1997) are criticized. The questions lack sensitivity and do not allow exact assessment of participants’ knowledge of the task, as claimed by Dunn et al (Dunn et al.,
2006). When more precise questions are utilized, it was possible to detect that subjects were able to depict the specifics of the IGT before showing their understanding in their choice behavior. Hence it can be assumed that participants are able to pinpoint good and bad decks as early as during the first twenty choices (Dunn et al., 2006).
Moreover, it is stated that although the task set-up of the IGT is supposed to encourage implicit choice behavior, it might actually promote explicit choice behavior. This assumption is based upon several facts: subjects are given time to decide in the inter-trial intervals, rewards and punishments follow a certain schedule and do not vary in sum. Mainly the latter does allow participants to consciously prepare for feedback as the IGT makes it possible to quickly learn what to assume when a deck is picked (Maia & McClelland,
2004).
68 Criticism on the SMH
In a number of previous studies, Skin conductance responses are detected when participants are completing the IGT. These physiological responses are the measurements the somatic marker hypothesis is based on and have been shown to be different for good and bad choices. Additionally it is suggested that skin conductance responses can be linked to an improved performance, thus a higher total winnings (Dunn et al., 2006).
While this physiological marker can be regarded as reliable, it might be more complex than originally thought in its interpretation. The complexity arises through a number of different observations. Dunn et al (2006) describes a study in which the skin conductance differences between good and bad choices have been shown only in a certain subgroup of healthy subjects that perform well. Furthermore, it is assumed that SCRs indicate differences in magnitude of wins and losses rather than the actual nature of the choice
(good or bad) (Dunn et al., 2006). There are big differences in magnitude in wins and losses for the bad decks and small differences in magnitude for wins and losses for good decks in the original IGT. This set-up was shown to elicit
SCRs indicating a difference between good and bad choices. When the differences in magnitude were changed, i.e. big differences in magnitude of wins and losses for good decks and small differences for bad decks, SCRs were shown to be present more prominently in anticipation of choosing good decks (Dunn et al., 2006).
69 While these findings are not necessarily deviating from the assumptions of the
SMH, as the SCRs still indicate anticipatory effects, they also show that the nature of the choice, bad or good, does not serve as the main reason for these physiological effects (Dunn et al., 2006; Maia & McClelland, 2005).
What can be taken away from the numerous discussions around the IGT and the SMH, is positive and negative. While the task itself is highly complex, involving a number of different processes and assumptions (Bechara et al.,
2005) with which individual research questions and studies are designed, it has to also be considered with caution as to what exactly the task will be able to show. However, the more the IGT is used the more discussions arise. This can be taken as a positive development, as findings are considered critically and alternative interpretations are found, which in turn benefits the field of decision-making research.
Regarding the findings of our current study in light of the above presented criticism, it can be stated that while our results are in agreement with the somatic marker hypothesis, criticism should be taken into account and other reasons for the difference between the choices (safe/risky) need to be considered, as not necessarily the difference between good and bad choices drives the contrast in DPN, but also the frequency and magnitude of wins and losses (Dunn et al., 2006; Maia & McClelland, 2004). However, DPN can still be taken as a measure in anticipation of a willed-risky choice that does indicate a difference between the choices made.
70 Variability in participants’ motivation
Another important point that has to be taken into account when considering the current study, and its participants is the variability in participants’ motivation to do well in the IGT.
Participants that enter a scientific study do so for various reasons and interests. When participants are compensated for their time, the financial incentive might be the greatest motivation to devote time to aid scientific research, however does this matter when investigating decision-making and the performance of subjects on a specific task? While this might be worth an investigation on its own, it is known that in different studies the matter of compensation is handled differently. Certain studies base the final sum participants receive at the end of the study dependent on their performance in the game. Other studies inform participants of the sum they receive at the beginning of the study, and do not change the sum based on their performance.
There are arguments for and against each way to handle this issue, but not one way has been proven to be most beneficial in this test (Bowman &
Turnbull, 2003). It is however important to note the questions that arise with each method, which could be: Would participants be more motivated when they receive a higher compensation based on their performance on the test?
Would participants perform better when they were playing with their own money?
71 Would participants show the same motivation to do well in the game without any compensation? While these questions are concerned with the money- aspect of testing, there are other aspects that should be addressed as well when regarding participant motivation. A study by Horstmann and colleagues
(2012) found that participants were more concerned with the frequency of wins and losses, and less with the long-term outcome of any individual deck
(Horstmann, Villringer, & Neumann, 2012).
What this discussion of the game and participants highlights is the fact that with each study utilizing the IGT, not only characteristics of the game itself are being assessed, but also the set-up is considered more closely.
Participant Population
The participant population in this study comprises a majority of undergraduate and graduate students. While this population set-up is common in studies of
‘healthy individuals’, there are limitations that need to be taken into account.
Undergraduate students do not satisfactorily represent a real-world population as they are highly educated, and represent an upbringing in a specific society, thus it is not possible to adequately project the findings in this study, or in any study using students as subjects, onto the normal population (Henrich, Heine,
& Norenzayan, 2010). Moreover, many young individuals will later develop mental disorders. For future studies, a population of higher age and more variability in socio-economic and education status is suggested.
72 6. Future Directions
The current study covers three important measurements and provides results for three crucial questions in the area of decision-making research. The central component considered in the current and previous studies is the DPN that precedes a willed-risky decision. Investigations of the DPN have been conducted using the IGT (Bianchin & Angrilli, 2011; Carlson et al., 2009).
While this test and assessments allow the possibility to gain a good picture of the DPN in the decision-making process, future studies would benefit from the use of different or additional tests. As the IGT comprises not only a decision- making component but also a learning component (Bechara & Damasio,
2005; Bechara et al., 2005), future studies could consider simple decision- making tasks without any learning component (e.g. the Cambridge Gambling
Task (Rogers et al., 1999), learning tasks or gambling tasks e.g. (Sanfey,
Rilling, Aronson, Nystrom, & Cohen, 2003) and assess whether it is possible to identify a DPN, and show similar results to what has been found before.
Another aspect of the decision-making process would be to consider a different ERP component, such as post- feedback signals (signal-preceding negativity preceding feedback). This has been done in two studies: Bianchin and Angrillie (2011) take into account post-feedback signals and Carlson and colleagues (2009) investigate both, post-feedback signals and signal- preceding negativity preceding feedback. It is therefore important that results are confirmed and differences are discussed.
73 Additionally, measurements that should be assessed include the impact of emotion and mood. Would the presence of highly positive emotions or negative emotions/mood during the IGT improve or decrease participants’ behavior? It is known that emotional implicit or explicit predispositions and biases allow quick and beneficial decision-making (Bechara, Damasio, et al.,
2000), however the opposite can also be true (Raghunathan & Tuan Pham,
1999), leading to detrimental choices (Bechara et al., 2005).
When investigating different measurements, it is necessary to utilize different methodologies to confirm previous results. As the current measurements rely on the superior temporal resolution of EEG, it would be interesting to use a technique that offers the latter but also additional features. Hence
Magnetoencephalography (MEG) or functional MRI coupled with EEG would be techniques that offer the possibility for similar measurements but add a superior source localization and structural imaging.
Regarding the participant population, future studies should consider different subject groups as well as patient groups. While previous studies using the
IGT and EEG and considering DPN, have primarily used children (Carlson et al., 2009) and university students (Bianchin & Angrilli, 2011) it would be important to replicate results with older participants, adolescents and elderly, but also recruit patient groups that present with decision-making impairments.
74 These could be patients with histories of suicidal acts (violent, non-violent)
(Bridge et al., 2012; Gorlyn et al., 2013; F. Jollant et al., 2005), mood disorders (Adida et al., 2011; Cella et al., 2010), borderline personality disorders (Bazanis et al., 2002) substance dependence (Barry & Petry, 2008;
Bechara et al., 2001), gambling addictions (Brand et al., 2005) and eating disorders (Brogan, Hevey, O'Callaghan, Yoder, & O'Shea, 2011; Garrido &
Subira, 2013). It would be interesting to see if DPN is altered in these groups, and if the alterations are related to abnormal processing of outcomes (wins and/or losses).
In addition to the abnormalities observed in different groups of patients, the difference in decision-making between males and females should receive attention. There have been studies examining this question using the IGT and other neuroimaging methods (Bolla, Eldreth, Matochik, & Cadet, 2004), however only few results have been obtained using a similar set-up to the current study, involving EEG and the IGT (Carlson et al., 2009). With studying gender differences, it is possible to investigate the (neural) mechanisms that lead to differential decision-making behavior (Bolla et al., 2004; Stanton,
Liening, & Schultheiss, 2011) that has been shown previously (Bechara et al.,
2005; Bolla et al., 2004; Carlson et al., 2009; van den Bos, Homberg, & de
Visser, 2013).
75 As mentioned before, while the current and previous studies cover a lot of ground investigating DPN as a crucial part of the decision-making process, additional measurements, such as different participant groups, different techniques and tasks and additional ERP components of the choice process will add to our understanding of this important process that is crucial in human behavior. Finally, an important aspect to investigate will be the interaction between implicit markers as reflected by DPN, and explicit understanding to guide decision-making.
76 7. Conclusion
The main objective of the current work was the study of the implicit anticipatory processes of decision-making, and more specifically of a negative potential called DPN, that immediately precedes a willed-risky decision. Our study confirms the existence of this negative potential and replicates previous findings. Additionally we showed that DPN is more pronounced in anticipation of risky compared to safe choices (Bianchin & Angrilli, 2011), a result that parallels previous findings using SCRs (Bechara et al., 2005). Overall, our findings support the SMH and the hypothesis that implicit markers play a role in guiding decision-making(Bechara, 1997; Bechara et al., 2005). Finally, we can report that wins have a stronger effect on subsequent DPN than losses, adding new findings to the understanding of the mechanisms of anticipatory processes of decision-making. The markers discussed may also be useful in future studies and add to the understanding of the mechanisms of impaired decision-making.
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