Нейрофизиологические корреляты принятия решений в условиях риска тема диссертации и автореферата по ВАК РФ 19.00.02, кандидат наук Япл Захарий Адам

  • Япл Захарий Адам
  • кандидат науккандидат наук
  • 2019, ФГАОУ ВО «Национальный исследовательский университет «Высшая школа экономики»
  • Специальность ВАК РФ19.00.02
  • Количество страниц 96
Япл Захарий Адам. Нейрофизиологические корреляты принятия решений в условиях риска: дис. кандидат наук: 19.00.02 - Психофизиология. ФГАОУ ВО «Национальный исследовательский университет «Высшая школа экономики». 2019. 96 с.

Оглавление диссертации кандидат наук Япл Захарий Адам

Table of Contents


Chapter 1: Neurobiological mechanisms of risk-taking (Literature review)

1.1 Main task paradigms used in EEG studies of risky decision-making

1.2 Event-related potentials associated with risk-taking

1.3 Oscillatory activity associated with risk-taking

1.4 Resting state oscillatory activity

1.5 Summary

Chapter 2: Behavioural study - a novel paradigm to study voluntary risk-taking

2.1 Background and Hypothesis

2.2 Methods

2.3 Results

2.4 Discussion

2.5 Summary

Chapter 3: tACS study of voluntary risk-taking

3.1 Background and Hypothesis

3.2 Methods

3.3 Results

3.4 Discussion

3.5 Summary

Chapter 4: EEG study of feedback processing during voluntary risk-taking

4.1 Background and Hypothesis

4.2 Methods

4.3 Results

4.4 Discussion

4.5 Summary

General Conclusions






Рекомендованный список диссертаций по специальности «Психофизиология», 19.00.02 шифр ВАК

Введение диссертации (часть автореферата) на тему «Нейрофизиологические корреляты принятия решений в условиях риска»


Various aspects of psychological mechanisms of decisions under risk and uncertainty have been thoroughly investigated by psychologists (for a review, see Mishra, 2014) including Russian researchers (e.g. Корнилова, 1997; Карпов А.В., 2000, Поддьяков 2006; Леонтьев 2009). Some studies focused on economic risky decision-making which involves choosing uncertain options when the probabilities are known. Inspired by dual system models, that irrational decision-making increases when cognitive resources become depleted (e.g. Kahneman, 2011; Kahneman & Frederick, 2007; Kahneman, 2003), some have tested the influence of executive control on risky decision-making by administering the n-back task, a popular working memory task, in parallel with various risky decision-making tasks (e.g. Gathmann et al., 2014a; Gathmann et al., 2014b; Pabst et al., 2013; Farrell et al., 2012; Starcke et al., 2011; Whitney, Rinehart & Hinson, 2008). Likewise, many have examined inhibitory processes and risky decision-making by employing the Go/No-Go task in parallel with various risky decision-making tasks (Welsh et al., 2017; Ba et al., 2016; Yeomans & Brace, 2015; Verdejo-Garda et al., 2007). Importantly, an ability to adaptively shift attention and action is an important characteristic of human cognitive control. Task-set switching is an important aspect of cognitive control when frequently switching between tasks. However, to date few have examined the link between risky decision-making and task switching (Frober & Dreisbach, 2016; Verdejo-Garcia et al., 2007).

Risky decision-making has been extensively investigated using electrophysiological measures alongside traditional task paradigms such as the monetary gambling task, probabilistic two-choice gambling task (Endrass et al., 2016; Zheng et al., 2015; Schuermann et al., 2012) and the balloon analog risk task (Kiat et al., 2016; Lejuez et al., 2002). Specifically, researchers have relied on evoked potentials, task-dependent neural oscillatory activity, and resting state oscillatory activity electroencephalography (EEG) to investigate and predict the neural correlates of economic risky decision-making

(Zhang et al., 2014; Leicht et al., 2013; Oberg et al., 2011; Wu and Zhou, 2009; Goyer et al., 2008; Marco-Palleres, et al., 2008; Masaki et al., 2006; Gehring and Willoughby, 2004; Yeung and Sanfrey, 2004; Gehring and Willoughby, 2002 Balconi and Finocchiaro, 2015; Balconi et al., 2015; Balconi et al., 2014; Telpaz and Yechiam, 2014; Schutter and Van Honk, 2005; Schutter et al., 2004). Neuromodulation techniques such as transcranial magnetic stimulation, transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS), have also been applied to healthy participants for the purpose of modulating economic risky decision-making (Sela et al., 2012).

The goal my PhD project was twofold. The first goal is to further explore Kahnmann's dual process theory by modifying an executive (task switching) task with risky/safe feedback prospects. The purpose of this task paradigm is to test whether executive control measures can alter risky decision-making. Our second goal is to test whether it is possible to modulate risky decision-making by using tACS, a relatively novel stimulating technique which entrains ongoing oscillatory activity. In correspondence with the tACS project, we aim to measure the oscillatory activity using the same task design to confirm and explore the significance of tACS modulation.

We attempt these goals by first reporting the current literature on electrophysiological measures of risky decision-making (Chapter 2) using various task paradigms. To follow, in a series of experiments we report our findings from: 1) a behavioral pilot study demonstrating an influence of executive control on risky decision-making using a novel behavioral task (Chapter 3); 2) a study using tACS showing a robust modulation of risky decision-making from 20 Hz stimulation yet not set-switching (Chapter 4), and 3) an EEG study revealing higher synchronization of frontal-central low beta (12-20 Hz) frequency component in association with risky decision-making in the gain domain (Chapter 5). These findings are then summarized in the General conclusions (Chapter 6).

Main goals of the study

1. Investigate the behavior associated with executive control on risky decision making

2. Investigate the behavior associated with feedback on future risky decision making

3. Explore neural activity of executive control on risky decision making with tACS

4. Investigate feedback processing with the FRN component using (ERP) analysis

5. Explore neural oscillations of feedback processing using event-related spectral perturbations (ERSP) and source analysis

6. Explore neural oscillations of feedback processing on future risky decision making

Chapter 1: Neurobiological mechanisms of risk-taking (Literature review)

Economic risky decision-making has become a rather prominent topic in recent years, for the relatively new field of neuroeconomics which combines cognitive neuroscience with an economic approach in order to quantitatively measure neural signatures of decision-making. In neuroeconomics, risky decision-making is characterized by selecting between two uncertain options in which the probabilities of outcomes are known (Tversky & Foz, 1995; Bernoulli, 1738) and as a research line is being one of the mainstreams in decision making such as affect heuristic (Slovic and Peters, 2006).

Neuroimaging studies investigating the neural structures on economic risky decision-making have revealed a prominent role of the brain's reward system, i.e. the prefrontal cortex, ventral striatum, insula, subthalamic nucleus, and amygdala (Tom et al., 2013; Cohen et al., 2009a; Rao et al., 2008; Eshel et al., 2007; Galvan et al., 2006; Ernst et al., 2005), culminating to an functional magnetic resonance imaging (fMRI) meta-analysis of economic risky decision-making (Mohr et al., 2010). However, a disadvantage of using fMRI is the low temporal resolution, as this procedure requires several seconds to obtain a functional image. As an alternative method, electroencephalography (EEG) has been used to understand dynamic temporal changes of the cognitive processes underlying economic risky decision-making, due to its high temporal resolution, measuring changes electrophysiology on the millisecond scale.

To our knowledge there are no current reviews on the electroencephalographic measures of economic risky decision-making. We summarize the literature with respect to electrophysiological measures of economic risky decision-making. The first section discusses commonly used task paradigms for determining electrophysiological measures of economic risky decision-making. The second section discusses the current status on evoked potentials in association with the three key parameters of economic risky decision-making: valence, magnitude of outcome, and probability. In the third section, we discuss induced oscillatory activity found in various gambling tasks associated with economic

risky decision-making, emphasizing learning and reward processing. The final section addresses the literature review regards to resting state oscillations and how they may be used to predict individual preferences for risk. From this review we hope to shed light on the electrophysiological measures of economic risky decision-making.

Похожие диссертационные работы по специальности «Психофизиология», 19.00.02 шифр ВАК

Заключение диссертации по теме «Психофизиология», Япл Захарий Адам

General Conclusions

For this report we first sought to investigate the link between risky decision making and executive control. This first goal was inspired by Kahnmann's dual process theory by which decisions are governed by an automatic and a cognitive system. To investigate this question, we designed a novel paradigm that allows one to measure executive control and risky decision making within a single response. We found that executive control influenced risky decisions in the gain domain, specifically affected Lose-stay strategies, i.e. instances in which participants select risky consecutively despite receiving negative feedback. We next explored whether this mechanism can be modulated with transcranial alternating current stimulation with specified frequencies (5, 10, 20, 40 Hz) against a sham condition (i.e. random noise stimulation). This experiment revealed a frequency specific effect of 20 Hz applied to the left frontal area, which increased participants motivation to select risky decisions. No differences were observed for the choice to switch or repeat task sets (i.e. our measure of executive control). We proceeded to understand how 20 Hz tACS modulated behavior by performing an independent EEG experiment. For this experiment we performed time-frequency analysis which revealed a corresponding change in beta neural oscillations during the feedback stage, specifically when during gain omission. Further exploratory analysis had shown that risky decision making was affected by beta power density during the same time window. This additional analysis revealed a possible learning mechanism for beta oscillations which was identified as a violation of expectations within the gain domain. Hence, modulation of 20 Hz may have perturbed endogenous beta oscillations associated with feedback learning; and in turn hindered this mechanism that would otherwise recognize expectation violation during gain omission. To conclude, we find that executive control can indeed influence decisions, yet the neural mechanism for this process is still unclear. In our attempt to investigate this question, we serendipitously revealed a possible learning mechanism which focuses mainly on the reward aspects of

decision making. Further work is necessary in order to explore both the role of executive control on risky decision making, and the role of beta oscillations in reward learning.


1. Using novel RVST-paradigm, we found the effect of executive control on risk-taking: only under high executive control, participants demonstrated a "reflection effect": higher risk taking for losses than for gains. No reflection effect was observed under low executive control.

2. The gain-domain specific influence of executive control on risk-taking during RVST-paradigm occurred due to modulation of decision making strategies on a trial-by-trial basis.

3. Frequency-specific 20-Hz tDCS stimulation over the left prefrontal area significantly increases voluntary risky-taking during RVST-paradigm indicating a link between risk-taking and beta oscillations.

4. Feedback-related negativity is sensitive to omissions of gains compared to the receptions of gains, which suggests that the feedback-related negativity is associated with the processing of outcomes in the context of gains as opposed to losses.

5. Beta oscillations (12-20 Hz) are sensitive to the omission of rewards relative to the reception of rewards and not to the omission of losses. Beta oscillations elicited by positive feedback in the gain correlate with risk-taking in the following trial.

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