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 наук
• 2019

Оглавление диссертации кандидат наук Япл Захарий Адам

Table of Contents

Introduction

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

Conclusions

References

Tables

Abbreviations

Acknowledgments

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

Introduction

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.

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.

Conclusions

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.

Список литературы диссертационного исследования кандидат наук Япл Захарий Адам, 2019 год

References

1. Abouzari, M., Oberg, S., & Tata, M. (2016). Theta-band oscillatory activity differs between gamblers and nongamblers comorbid with attention-deficit hyperactivity disorder in a probabilistic reward-learning task. Behavioural Brain Research, 312, 195-200

2. Alexander, W. H., & Brown, J. W. (2011). Medial prefrontal cortex as an action-outcome predictor. Nature neuroscience, 14(10), 1338-44.

3. Antal A, Boros K, Poreisz C, Chaieb L, Terney D, Paulus W (2008) Comparatively weak after-effects of transcranial alternating current stimulation (tACS) on cortical excitability in humans. Brain Stimul 1(2): 97-105.

4. Apitz, T., & Bunzeck, N. (2014). Early effects of reward anticipation are modulated by dopaminergic stimulation. PloS one, 9(10), e108886.

5. Aranovich, G. J., McClure, S. M., Fryer, S., & Mathalon, D. H. (2016). The effect of cognitive challenge on delay discounting. Neuroimage, 124, 733-39.

6. Arrington CM, Logan GD (2004) The cost of a voluntary task switch. Psych Sci 15(9):610-5.

7. Arrington CM, Logan GD (2005) Voluntary task switching: chasing the elusive homunculus. J Exp Psychol Learn Mem Cogn 31(4):683.

8. Arrington CM, Reiman KM, Weaver SM (2014) Voluntary task switching. In Task switching (ed. J. Grange & G. Houghton 117-136 (Oxford, UK: Oxford University Press).

9. Avila, C., Garbin, G., Sanjuan, A., Forn, C., Barros-Loscertales, A, Bustamante, J. C., Rodriguez-Pujadas, A, Belloch, V., et al., (2012). Frontostriatal response to set switching is moderated by gain sensitivity. Social, Cognitive and Affective Neuroscience, 7, 423-30.

10. Ba Y, Zhang W, Salvendy G, Cheng AS, Ventsislavova P (2016) Assessments of risky driving: A Go/No-Go simulator driving task to evaluate risky decisionmaking and associated behavioral patterns. Appl Ergon 52:265-274.

11. Bai S, Dokos S, Ho KA, Loo C (2014) A computational modelling study of transcranial direct current stimulation montages used in depression. Neuroimage 87:332-44.

12. Bates D, Mâchler M, Bolker B, Walker S (2014) Fitting linear mixed-effects models using lme4. arXiv preprint arXiv: 1406.5823.

13. Balconi, M., Finocchiaro, R., & Canavesio, Y. (2014). Reward-system effect (BAS rating), left hemispheric "unbalance"(alpha band oscillations) and decisional impairments in drug addiction. Addictive behaviors, 39(6), 1026-32.

14. Balconi, M., & Finocchiaro, R. (2015). Decisional impairments in cocaine addiction, reward bias, and cortical oscillation "unbalance". Neuropsychiatric disease and treatment, 11, 777.

15. Balconi, M., Finocchiaro, R., & Canavesio, Y. (2015). Left hemispheric imbalance and reward mechanisms affect gambling behavior: the contribution of the metacognition and cortical brain oscillations. Clinical EEG and neuroscience, 46(3), 197-207.

16. Bechara, A., Damasio, A. R., Damasio, H., Anderson, S. W. (1994). Insensitivity to future consequences following damage to human prefrontal cortex. Cognition, 50, 1-3, 7-15.

17. Bellebaum, C., & Daum, I. (2008). Learning-related changes in reward expectancy are reflected in the feedback-related negativity. European Journal of Neuroscience, 27(7), 1823-35.

18. Bernat, E. M., Nelson, L. D., Holroyd, C. B., Gehring, W. J., & Patrick, C. J. (2008, August). Separating cognitive processes with principal components analysis of EEG time-frequency distributions. In Optical Engineering+ Applications (pp. 70740S-70740S). International Society for Optics and Photonics.

19. Bernoulli, D. (1738). Specimen theoriae novae de mensura sortis [Exposition of a new theory on the measurement of risk]. Commentarii Academiae Scientarum Imperialis Petropolitanae, 5, 175-92.

20. Bunzeck N, Guitart-Masip M, Dolan RJ, Duzel E (2011) Contextual novelty modulates the neural dynamics of reward anticipation. J Neurosci 31(36): 1281612822.

21. Bush, G., Luu, P., & Posner, M. I. (2000). Cognitive and emotional influences in anterior cingulate cortex. Trends in cognitive sciences, 4(6), 215-22.

22. Carlson, J. M., Foti, D., Mujica-Parodi, L. R., Harmon-Jones, E., & Hajcak, G. (2011). Ventral striatal and medial prefrontal BOLD activation is correlated with reward-related electrocortical activity: a combined ERP and fMRI study. Neuroimage, 57(4), 1608-16.

23. Cavanagh, J. F., Bismark, A. J., Frank, M. J., & Allen, J. J. B. (2011). Larger error signals in major depression are associated with better avoidance learning. Frontiers in Psychology, 2, 331.

24. Cavanagh JF, Figueroa CM, Cohen MX, Frank MJ (2011) Frontal theta reflects uncertainty and unexpectedness during exploration and exploitation. Cereb Cortex 22(11):2575-2586.

25. Cavanagh JF, Frank MJ (2014) Frontal theta as a mechanism for cognitive control. Trends Cogn Sci 18(8):414-21.

26. Cavanagh JF, Frank MJ, Klein TJ, Allen JJ (2010) Frontal theta links prediction errors to behavioral adaptation in reinforcement learning. Neuroimage 49(4):3198-3209.

27. Cavanagh, J. F., Zambrano-Vazquez, L., & Allen, J. J. (2012). Theta lingua franca: A common mid-frontal substrate for action monitoring processes. Psychophysiology, 49(2), 220-38.

28. Cazzell, M., Li, L., Lin, Z.-J., Patel, S.J., & Liu, H. (2012). Comparison of neural correlates of risk decision-making between genders: an exploratory fNIRS study of the Balloon Analogue Risk Task (BART). Neurolmage, 62(3), 1896-1911.

29. Charness, G., Gneezy, U., & Imas, A. (2013). Experimental methods: Eliciting risk preferences. Journal of Economic Behavior & Organization, 87, 43-51.

30. Cheng GL, Lee TM (2016) Altering risky decision-making: Influence of impulsivity on the neuromodulation of prefrontal cortex. Soc Neurosci 11(4):353-364.

31. Christie, G. J., & Tata, M. S. (2009). Right frontal cortex generates reward-related theta-band oscillatory activity. Neuroimage, 48(2), 415-22.

32. Clemens, B., Bessenyei, M., Fekete, I., Puskas, S., Kondakor, I., Toth, M., & Hollody, K. (2010). Theta EEG source localization using LORETA in partial epilepsy patients with and without medication. Clinical Neurophysiology, 121(6), 848-58.

33. Cohen, M. X., Elger, C. E., & Ranganath, C. (2007). Reward expectation modulates feedback-related negativity and EEG spectra. Neuroimage, 35(2), 968-78.

34. Cohen, M. X., & Ranganath, C. (2007). Reinforcement learning signals predict future decisions. Journal of Neuroscience, 27(2), 371-78.

35. Cohen, M. X., Axmacher, N., Lenartz, D., Elger, C. E., Sturm, V., & Schlaepfer, T. E. (2009a). Neuroelectric signatures of reward learning and decision-making in the human nucleus accumbens. Neuropsychopharmacology, 34(7), 1649-58.

36. Cohen, M. X., Elger, C. E., & Fell, J. (2009b). Oscillatory activity and phaseamplitude coupling in the human medial frontal cortex during decision-making. Journal of cognitive neuroscience, 21(2), 390-402.

37. Cui, J. F., Chen, Y. H., Wang, Y., Shum, D. H., & Chan, R. C. (2013). Neural correlates of uncertain decision-making: ERP evidence from the Iowa Gambling Task. Frontiers in human neuroscience, 7, 776.

38. Cunillera T, Fuentemilla L, Perianez J, Marco-Pallares J, Krämer UM, Camara E, et al., (2012) Brain oscillatory activity associated with task switching and feedback processing. Cogn Affect Behav Neurosci 12(1):16-33.

39. Davidson, R. J. (2000). Affective style, psychopathology, and resilience: brain mechanisms and plasticity. American Psychologist, 55(11), 1196.

40. de Jong R, Gladwin TE, & 't Hart BM (2006) Movement-related EEG indices of preparation in task switching and motor control. Brain Res, 1105(1), 73-82.

41. Deck, C., & Jahedi, S. (2015). The effect of cognitive load on economic decision-making: A survey and new experiments. European Economic Review, 78, 97-119.

42. Derrfuss J, Brass M, Neumann J, Yves von Cramon D (2005) Involvement of the inferior frontal junction in cognitive control: Meta-analyses of switching and Stroop studies. Hum Brain Mapp 25:22-34.

43. Diamond A (2013) Executive functions. Annu. Rev Psychol 64:135-68. doi: 10.1146/annurev-psych-113011-143750.

44. Duff EP, Johnston LA, Xiong J, Fox PT, Mareels I, & Egan GF (2008) The power of spectral density analysis for mapping endogenous BOLD signal fluctuations. Human brain mapping, 29(7), 778-790.

45. Endrass, T., Schuermann, B., Roepke, S., Kessler-Scheil, S., & Kathmann, N. (2016). Reduced risk avoidance and altered neural correlates of feedback processing in patients with borderline personality disorder. Psychiatry Research, 243, 14-22.

46. Engelmann JB, Tamir D (2009) Individual differences in risk preference predict neural responses during financial decision-making. Brain Res 1290:28-51.

47. Ernst, M., Nelson, E.E., Jazbec, S., McClure, E.B., Monk, C.S., Leibenluft, E., ... & Pine, D. S. (2005). Amygdala and nucleus accumbens in responses to receipt and omission of gains in adults and adolescents..Neuroimage, 25(4), 1279-91.

48. Ernst, M., Nelson, E.E., McClure, E.B., Monk, C.S., Eshel, N., Zarahn, E., Leibenluft, E., Zametkin, A.J., Towbin, K., Charney, D.S., & Pine, D.S. (2004). Choice selection and reward anticipation: an fMRI study. Neuropsychologia 42, 1585-97.

49. Eshel, N., Nelson, E. E., Blair, R. J., Pine, D. S., & Ernst, M. (2007). Neural substrates of choice selection in adults and adolescents: development of the ventrolateral prefrontal and anterior cingulate cortices. Neuropsychologia, 45(6), 1270-79.

50. Etzel, J. A., Cole, M. W., Zacks, J. M., Kay, K. N., & Braver, T. S. (2016). Gain Motivation Enhances Task Coding in Frontoparietal Cortex. Cerebral Cortex, 26, 1647-59.

51. Evans, J. (2009). Introspection, confabulation, and dual-process theory. Behavioral and Brain Sciences, 32, 142-43.

52. Farrell SM, Tunbridge EM, Braeutigam S Harrison PJ (2012) COMT Val 158 Met genotype determines the direction of cognitive effects produced by catechol-O-methyltransferase inhibition. Biol Psychiatry 71(6):538-544.

53. Fecteau S, Knoch D, Fregni F, Sultani N, Boggio P, Pascual-Leone A (2007a) Diminishing risk-taking behavior by modulating activity in the prefrontal cortex: a direct current stimulation study. J Neuroscience 27(46): 12500-12505.

54. Fecteau S, Pascual-Leone A, Zald DH, Liguori P, Theoret H, Boggio PS, Fregni F (2007b) Activation of prefrontal cortex by transcranial direct current stimulation reduces appetite for risk during ambiguous decision-making. J Neuroscience 27(23):6212-6218.

55. Feurra M, Bianco G, Santarnecchi E, Del Testa M, Rossi A, Rossi S (2011) Frequency-dependent tuning of the human motor system induced by transcranial oscillatory potentials. J Neuroscience 31(34): 12165-12170.

56. Feurra M, Galli G, Pavone EF, Rossi A, Rossi S (2016) Frequency-specific insight into short-term memory capacity. J Neurophysiol, 116(1): 153-158.

57. Feurra M, Galli G, Rossi S (2012) Transcranial alternating current stimulation affects decision-making. Front Syst Neurosci 6:39.

58. Foti, D., Weinberg, A., Dien, J., & Hajcak, G. (2011). Event-related potential activity in the basal ganglia differentiates rewards from nonrewards: Temporospatial principal components analysis and source localization of the feedback negativity. Human brain mapping, 32(12), 2207-16.

59. Fröber, K., & Dreisbach, G. (2016). How sequential changes in gain magnitude modulate cognitive flexibility: evidence from voluntary task switching. Journal of Experimental Psychology: Learning, Memory and Cognition, 42, 285-95.

60. Fuentes-Claramonte, P., Avila, C., Rodriguez-Pujadas, A., Ventura-Campos, N., Bustamante, J. C., Costumero, V., Rosell-Negre, P., Barros-Loscertales, A., (2015). Gain Sensitivity Modulates Brain Activity in the Prefrontal Cortex, ACC and Striatum during Task Switching. PlosOne, 10, UNSP e0123073.

61. Fujiwara J, Tobler PN, Taira M, Iijima T, Tsutsui KI (2009) Segregated and integrated coding of reward and punishment in the cingulate cortex. J Neurophysiology 101(6):3284-93.

62. Fukui, H., Murai, T., Fukuyama, H., Hayashi, T., & Hanakawa, T. (2005). Functional activity related to risk anticipation during performance of the Iowa Gambling Task. Neuroimage, 24(1), 253-59.

63. Galvan, A., Hare, T. A., Parra, C. E., Penn, J., Voss, H., Glover, G., & Casey, B. J. (2006). Earlier development of the accumbens relative to orbitofrontal cortex might underlie risk-taking behavior in adolescents. Journal of Neuroscience, 26(25), 6885-92.

64. Gathmann B, Pawlikowski M, Schöler T, Brand M (2014a) Performing a secondary executive task with affective stimuli interferes with decision-making under risk conditions. Cogn Process 15:113-26.

65. Gathmann B, Schulte FP, Maderwald S, Pawlikowski M, Starcke K, Schäfer LC, et al., (2014) Stress and decision-making: neural correlates of the interaction

between stress, executive functions, and decision-making under risk. Exp Brain Res 232(3):957-973.

66. Gehring, W.J., Willoughby, & A.R. (2002). The medial frontal cortex and the rapid processing of monetary gains and losses. Science, 295, 2279-82.

67. Gehring, W. J., & Willoughby, A. R. (2004). Are all medial frontal negativities created equal? Toward a richer empirical basis for theories of action monitoring. Errors, conflicts, and the brain. Current opinions on performance monitoring, 14, 20.

68. Gianotti, L. R., Knoch, D., Faber, P. L., Lehmann, D., Pascual-Marqui, R. D., Diezi, C., ... & Fehr, E. (2009). Tonic activity level in the right prefrontal cortex predicts individuals' risk taking. Psychological science, 20(1), 33-38.

69. Goyer, J. P., Woldorff, M. G., & Huettel, S. A. (2008). Rapid electrophysiological brain responses are influenced by both valence and magnitude of monetary rewards. Journal of Cognitive Neuroscience, 20(11), 2058-69.

70. Gruendler, T. O., Ullsperger, M., & Huster, R. J. (2011). Event-related potential correlates of performance-monitoring in a lateralized time-estimation task. PloS one, 6(10), e25591.

71. Hajcak, G., Moser, J., Holroyd, C. B., & Simons, R. F. (2006). The feedback-related negativity reflects the binary evaluation of good versus bad outcomes. Biological Psychology, 71, 148-54.

72. HajiHosseini A, Rodriguez-Fornells A, Marco-Pallares J (2012) The role of beta-gamma oscillations in unexpected rewards processing. Neuroimage 60(3):1678-1685.

73. HajiHosseini A, Holroyd CB (2015a) Reward feedback stimuli elicit high-beta EEG oscillations in human dorsolateral prefrontal cortex. Sci Rep 5:13021.

74. HajiHosseini A, & Holroyd CB (2015b) Sensitivity of frontal beta oscillations to reward valence but not probability. Neurosci Lett, 602, 99-103.

75. Harrison GW, Martínez-Correa J, Swarthout JT (2013) Inducing risk neutral preferences with binary lotteries: A reconsideration. J Econ Behav Organ

94:145-159.

76. Hauser, T. U., Iannaccone, R., Stämpfli, P., Drechsler, R., Brandeis, D., Walitza, S., & Brem, S. (2014). The feedback-related negativity (FRN) revisited: new insights into the localization, meaning and network organization. Neuroimage, 84, 159-68.

77. Helfrich RF, Schneider TR, Rach S, Trautmann-Lengsfeld SA, Engel AK, Herrmann CS (2014) Entrainment of brain oscillations by transcranial alternating current stimulation. Curr Biol 24(3):333-339.

78. Helfrich RF, Knepper H, Nolte G, Strüber D, Rach S, Herrmann CS, Schneider TR, Engel AK (2014b) Selective modulation of interhemispheric functional connectivity by HD-tACS shapes perception. PLoS Biol 12(12):e1002031.

79. Hewig, J., Trippe, R., Hecht, H., Coles, M. G., Holroyd, C. B., & Miltner, W. H. (2007). Decision-making in Blackjack: an electrophysiological analysis. Cerebral Cortex, 17(4), 865-77.

80. Hinson, J. M., Jameson, T. L., & Whitney, P. (2003). Impulsive decision-making and working memory. Journal of Experimental Psychology: Learning, Memory and Cognition, 29, 298-306.

81. Holroyd CB, Coles MG (2002) The neural basis of human error processing: reinforcement learning, dopamine, and the error-related negativity, Psychol. Rev. 109

82. 679-709.

83. Holroyd CB, Larsen JT, Cohen JD (2004) Context dependence of the event-related brain potential to reward and punishment, Psychophysiology 41 245-253.

84. IBM Corp (2011) Released. IBM SPSS Statistics for Windows, Version 20.0. Armonk, NY: IBM Corp.

85. Im CH, Park JH, Shim M, Chang WH, Kim YH (2012) Evaluation of local electric fields generated by transcranial direct current stimulation with an extracephalic reference electrode based on realistic 3D body modeling. Phys Med Biol 57(8):2137.

86. Jonasson, L. S., Axelsson, J., Riklund, K., Braver, T. S., Ögren, M., Bäckman, L., & Nyberg, L. (2014). Dopamine release in nucleus accumbens during gained task switching measured by [uC]raclopride. Neuroimage, 1 (99): 357-64.

87. Joundi RA, Jenkinson N, Brittain JS, Aziz TZ, Brown P (2012) Driving oscillatory activity in the human cortex enhances motor performance. Curr Biol 22(5):403-407.

88. Jurkiewicz MT, Gaetz WC, Bostan AC, & Cheyne D (2006) Post-movement beta rebound is generated in motor cortex: evidence from neuromagnetic recordings. Neuroimage, 32(3), 1281-1289.

89. Kahneman D (2003) A perspective on judgment and choice. Am Psychol 58:697-720.

90. Kahneman D (2011) Thinking, fast and slow. New York: Farrar, Straus and Giroux.

91. Kahneman D, Frederick S (2007) Frames and brains: elicitation and control of response tendencies. Trends Cogn Sci 11:45-46.

92. Kanai R, Chaieb L, Antal A, Walsh V, Paulus W (2008) Frequency-dependent electrical stimulation of the visual cortex. Curr Biol 18(23):1839-1843.

93. Kanai R, Paulus W, Walsh V (2010) Transcranial alternating current stimulation (tACS) modulates cortical excitability as assessed by TMS-induced phosphene thresholds. Clin Neurophysiol 121(9):1551-1554.

94. Карпов А.В. Общая психология субъективного выбора: структура, процесс, генезис. Ярославль: ИП РАН, 2000. - 328 с.

95. Kawasaki, M., & Yamaguchi, Y. (2013). Frontal theta and beta synchronizations for monetary reward increase visual working memory capacity. Social cognitive and affective neuroscience, 8(5), 523-30.

96. Kiat, J., Straley, E., & Cheadle, J. E. (2016). Escalating risk and the moderating effect of resistance to peer influence on the P200 and feedback-related negativity. Social Cognitive and Affective Neuroscience, 377-86.

97. Kiesel, A., Steinhauser, M., Wendt, M., Falkenstein, M., Jost, K., Philipp, A. M., Koch, I. (2010). Control and interference in task switching--a review. Psychological Bulletin, 136, 879-74.

98. Kim C, Cilles SE, Johnson NF, Gold BT (2012) Domain general and domain preferential brain regions associated with different types of task switching: A meta-analysis. Hum Brain Mapp 33:130-42.

99. Klimesch W (2012) Alpha-band oscillations, attention, and controlled access to stored information. Trends Cogn Sci 16(12):606-617.

100. Knoch D, Gianotti LR, Pascual-Leone A, Treyer V, Regard M, Hohmann M, Brugger P (2006) Disruption of right prefrontal cortex by low-frequency repetitive transcranial magnetic stimulation induces risk-taking behavior. J Neuroscience 26(24):6469-6472.

101. Knutson B, Adams CM, Fong GW, Hommer D (2001a) Anticipation of increasing monetary reward selectively recruits nucleus accumbens. J Neurosci 21(16):RC159.

102. Knutson B, Fong GW, Adams CM, Varner JL, Hommer D (2001b) Dissociation of reward anticipation and outcome with event-related fMRI. Neuroreport 12(17):3683-3687.

103. Knutson B, Greer SM (2008) Anticipatory affect: neural correlates and consequences for choice. Philosophical Transactions of the Royal Society of London B: Biological Sciences. 363 (1511):3771-3786.

104. Kohls G, Perino MT, Taylor JM, Madva EN, Cayless SJ, Troiani V, Price E, Faja S, Herrington JD, Schultz RT (2013) The nucleus accumbens is involved in both the pursuit of social reward and the avoidance of social punishment. Neuropsychologia 51(11):2062-2069.

105. Корнилова Т.В. Диагностика мотивации и готовности к риску. М.: ИГЕРАН, 1997.

106. Kreussel, L., Hewig, J., Kretschmer, N., Hecht, H., Coles, M. G., & Miltner, W. H. (2012). The influence of the magnitude, probability, and valence of potential wins and losses on the amplitude of the feedback negativity. Psychophysiology, 49(2), 207-19.

107. Kuhnen CM, Knutson B (2005) The neural basis of financial risk taking. Neuron 47(5):763-770.

108. Kuznetsova A, Brockhoff PB, Christensen, RHB (2016) Package 'lmerTest': Tests in Linear Mixed Effects Models. R package version 2.0-33. https: //CRAN. R-proj ect. org/package=lmerTest

109. Leicht, G., Troschutz, S., Andreou, C., Karamatskos, E., Ertl, M., Naber, D., & Mulert, C. (2013). Relationship between oscillatory neuronal activity during reward processing and trait impulsivity and sensation seeking. PloS one, 8(12), e83414.

110. Lejuez, C. W., Read, J. P., Kahler, C. W., Richards, J. B., Ramsey, S. E., Stuart, G. L., Strong, D. R., et al., (2002). Evaluation of a behavioral measure of risk taking: the Balloon Analogue Risk Task (BART). Journal of Experimental Psychology: Applied, 8, 2, 75-84.

111. Леонтьев, Д.А. К операционализации понятия «толерантность» / Д.А. Леонтьев // Вопросы психологии. - 2009. - №5. - С. 3-16.

112. Lucchiari, C., & Pravettoni, G. (2010). Feedback related brain activity in a gambling task: a temporal analysis of EEG correlates. Scandinavian journal of psychology, 51(6), 449-54.

113. Luft CD (2014) Learning from feedback: The neural mechanisms of feedback processing facilitating better performance. Behav Brain Res 261:356-68.

114. Luu, P., Flaisch, T., & Tucker, D. M. (2000). Medial frontal cortex in action monitoring. Journal of Neuroscience, 20(1), 464-69.

115. Marco-Pallares J, Cucurell D, Cunillera T, García R, Andrés-Pueyo A, Münte TF, Rodríguez-Fornells A (2008) Human oscillatory activity associated to reward processing in a gambling task. Neuropsychologia 46(1):241-248.

116. Marco-Pallarés J, Münte TF, Rodríguez-Fornells A (2015) The role of high-frequency oscillatory activity in reward processing and learning. Neurosci Biobehav Rev 49:1-7.

117. Martin, L. E., Potts, G. F., Burton, P. C., & Montague, P. R. (2009). Electrophysiological and hemodynamic responses to reward prediction violation. Neuroreport, 20(13), 1140.

118. Mas-Herrero E, Ripollés P, HajiHosseini A, Rodríguez-Fornells A, Marco-Pallarés J (2015) Beta oscillations and reward processing: Coupling oscillatory activity and hemodynamic responses. Neuroimage 119:13-19.

119. Masaki H, Takeuchi S, Gehring WJ, Takasawa N, Yamazaki K. Affective-motivational influences on feedback-related ERPs in a gambling task. Brain Res. 2006; 1105(1): 110-21.

120. Massar, S. A.A., Rossi, V., Schutter, D.J.L.G., & Kenemans, J.L. (2012). Baseline EEG theta/beta ratio and punishment sensitivity as biomarkers for feedback-related negativity (FRN) and risk-taking. Clinical Neurophysiology, 123(10), 1958-65.

121. Massar SA, Kenemans JL, Schutter DJ (2014) Resting-state EEG theta activity and risk learning: sensitivity to reward or punishment? Int J Psychophysiol. 91(3): 172-177.

122. Mathewson, K. J., Dywan, J., Snyder, P. J., Tays, W. J., & Segalowitz, S. J. (2008). Aging and electrocortical response to error feedback during a spatial learning task. Psychophysiology, 45(6), 936-48.

123. McCoy, A. N., & Platt, M. L. (2005). Risk-sensitive neurons in macaque posterior cingulate cortex. Nature neuroscience, 8(9), 1220-27.

124. Miltner, W. H., Braun, C. H., & Coles, M. G. (1997). Event-related brain potentials following incorrect feedback in a time-estimation task: Evidence for a "generic" neural system for error detection. Journal of cognitive neuroscience, 9(6), 788-98.

125. Mishra, S. (2014). Decision-making under risk: Integrating perspectives from biology, economics, and psychology. Personality and Social Psychology Review, 18(3), 280-307.

126. Mohr PN, Biele G, Heekeren HR (2010) Neural processing of risk. J Neuroscience 30(19):6613-6619.

127. Montague, P. R., Hyman, S. E., & Cohen, J. D. (2004). Computational roles for dopamine in behavioural control. Nature, 431(7010), 760.

128. Miyake A, Friedman NP, Emerson MJ, Witzki AH, Howerter A, Wager TD (2000) The unity and diversity of executive functions and their contributions to complex "frontal lobe" tasks: A latent variable analysis. Cogn Psychol 41(1):49-100.

129. Nieuwenhuis, S., Slagter, H. A., Geusau, V., Alting, N. J., Heslenfeld, D. J., & Holroyd, C. B. (2005). Knowing good from bad: differential activation of human cortical areas by positive and negative outcomes. European Journal of Neuroscience, 21(11), 3161-68.

130. Niv Y, Edlund JA, Dayan P, O'Doherty JP (2012) Neural prediction errors reveal a risk-sensitive reinforcement-learning process in the human brain. J Neurosci 32(2):551-562.

131. O'Doherty J, Kringelbach ML, Rolls ET, Hornak J, Andrews C (2001) Abstract reward and punishment representations in the human orbitofrontal cortex. Nat Neurosci 4(1):95-102.

132. Oberg, S. A., Christie, G. J., & Tata, M. S. (2011). Problem gamblers exhibit reward hypersensitivity in medial frontal cortex during gambling. Neuropsychologia, 49(13), 3768-75.

133. Oliveira, F. T., McDonald, J. J., & Goodman, D. (2007). Performance monitoring in the anterior cingulate is not all error related: expectancy deviation and the representation of action-outcome associations. Journal of cognitive neuroscience, 19(12), 1994-2004.

134. Orr JM, Banich MT (2014) The neural mechanisms underlying internally and externally guided task selection. Neuroimage 84:191-205.

135. Owen AM, McMillan KM, Laird AR, Bullmore E (2005) N-back working memory paradigm: A meta-analysis of normative functional neuroimaging studies. Hum Brain Mapp 25:46-59. doi: 10.1002/hbm.20131

136. Pabst S, Schoofs D, Pawlikowski M, Brand M, Wolf OT (2013) Paradoxical effects of stress and an executive task on decisions under risk. Behav Neurosci 127:369-79.

137. Paulus W (2010) On the difficulties of separating retinal from cortical origins of phosphenes when using transcranial alternating current stimulation (tACS). Clin Neurophysiol 121(7): 987-91.

138. Paulus W (2011) Transcranial electrical stimulation (tES-tDCS; tRNS, tACS) methods. Neuropsychol Rehabil 21(5):602-617.

139. Perfetti, B., Moisello, C., Landsness, E. C., Kvint, S., Pruski, A., Onofrj, M., ... & Ghilardi, M. F. (2011). Temporal evolution of oscillatory activity predicts performance in a choice-reaction time reaching task. Journal of Neurophysiology, 105(1), 18-27.

140. Pfurtscheller G, Stancak Jr A, Neuper, C (1996) Post-movement beta synchronization. A correlate of an idling motor area? Electroencephalogr Clin Neurophysiol, 98, 281-293.

141. Поддьяков, А.Н. Непереходность (нетранзитивность) отношений превосходства и принятие решений / А.Н. Поддьяков // Психология. Журнал Высшей школы экономики. - 2006. - Т. 3, - № 3. - С. 88-111.

142. Pogosyan A, Gaynor LD, Eusebio A, Brown P (2009) Boosting cortical activity at beta-band frequencies slows movement in humans. Curr Biol 19(19): 16371641.

143. Polania R, Nitsche MA, Korman C, Batsikadze G, Paulus W (2012) The importance of timing in segregated theta phase-coupling for cognitive performance. Curr Biol 22(14):1314-8.

144. Polezzi, D., Sartori, G., Rumiati, R., Vidotto, G., & Daum, I. (2010). Brain correlates of risky decision-making. Neuroimage, 49(2), 1886-94.

145. Potts, G. F., Martin, L. E., Burton, P., & Montague, P. R. (2006). When things

are better or worse than expected: the medial frontal cortex and the allocation of

processing resources. Journal of cognitive neuroscience, 18(7), 1112-19.

146. Proudfit GH (2015) The reward positivity: from basic research on reward to a biomarker for depression, Psychophysiology 52 (4) 449-459.

147. R Core Team (2016) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

148. Rao H, Korczykowski M, Pluta J, Hoang A, Detre JA (2008) Neural correlates of voluntary and involuntary risk taking in the human brain: an fMRI Study of the Balloon Analog Risk Task (BART). Neuroimage 42(2):902-910.

149. Rottschy C, Langner R, Dogan I, Reetz K, Laird AR, Schulz JB, et al., (2012) Modelling neural correlates of working memory: a coordinate-based meta-analysis. Neuroimage 60:830-846. doi: 10.1016/j.neuroimage.2011.11.050

150. Salmelin R, Hamalainen M, Kajola M, Hari R (1995) Functional segregation of movement-related rhythmic activity in the human brain. Neuroimage, 2, 237243.

151. Santarnecchi E, Muller T, Rossi S, Sarkar A, Polizzotto NR, Rossi A, Kadosh RC (2016) Individual differences and specificity of prefrontal gamma frequency-tACS on fluid intelligence capabilities. Cortex 75:33-43.

152. Santarnecchi E, Polizzotto NR, Godone M, Giovannelli F, Feurra M, Matzen L, Rossi A, Rossi S (2013) Frequency-dependent enhancement of fluid intelligence induced by transcranial oscillatory potentials. Curr Biol 23(15): 1449-53.

153. Sauseng P, Klimesch W, Freunberger R, Pecherstorfer T, Hanslmayr S, Doppelmayr M (2006) Relevance of EEG alpha and theta oscillations during task switching. Exp Brain Res 170(3):295-301.

154. Scheeringa, R., Bastiaansen, M. C., Petersson, K. M., Oostenveld, R., Norris, D. G., & Hagoort, P. (2008). Frontal theta EEG activity correlates negatively with the default mode network in resting state. International Journal of Psychophysiology, 67(3), 242-51.

155. Schneider, W., Eschman, A., & Zuccolotto, A. (2002). E-Prime: User's guide. Psychology Software Incorporated.

156. Schuermann, B., Endrass, T., & Kathmann, N. (2012). Neural correlates of feedback processing in decision-making under risk. Frontiers in human neuroscience, 6.

157. Schultz, W. (1998). Predictive reward signal of dopamine neurons. Journal of Neurophysiology, 80(1), 1-27.

158. Schultz, W. (2002). Getting formal with dopamine and reward. Neuron, 36(2), 241-263.

159. Schutter, D. J., de Haan, E. H., & van Honk, J. (2004). Anterior asymmetrical alpha activity predicts Iowa gambling performance: distinctly but reversed. Neuropsychologia, 42(7), 939-43.

160. Schutter DJ, van Honk J (2005) Electrophysiological ratio markers for the balance between reward and punishment. Cogn Brain Res 24(3):685-690.

161. Sela T, Kilim A, Lavidor M. (2012) Transcranial alternating current stimulation increases risk-taking behavior in the balloon analog risk task. Front Neurosci 14; 6, 22.

162. Selten R, Sadrieh A, Abbink K (1999) Money does not induce risk neutral behavior, but binary lotteries do even worse. Theor Decis 46(3):213-252.

163. Slovic, P., & Peters, E. (2006). Risk perception and affect. Current directions in psychological science, 15(6), 322-325.

164. Smith, E. H., Banks, G. P., Mikell, C. B., Cash, S. S., Patel, S. R., Eskandar, E. N., & Sheth, S. A. (2015). Frequency-dependent representation of reinforcement-related information in the human medial and lateral prefrontal cortex. Journal of Neuroscience, 35(48), 15827-36.

165. Starcke, K., Pawlikowski, M., Wolf, O. T., Altstotter-Gleich, C., & Brand, M. (2011). Decision-making under risk conditions is susceptible to interference by a secondary executive task. Cogn Process 12:177-182.

166. Studer, B., Pedroni, A., & Rieskamp, J. (2013). Predicting risk-taking behavior from prefrontal resting-state activity and personality. PLoS one, 8(10), e76861.

167. Struber D, Rach S, Neuling T, Herrmann CS (2015) On the possible role of stimulation duration for after-effects of transcranial alternating current stimulation. Front Cell Neurosci 9:311.

168. Sutton, S. K., & Davidson, R. J. (1997). Prefrontal brain asymmetry: A biological substrate of the behavioral approach and inhibition systems. Psychological Science, 8(3), 204-10.

169. Swick D, Ashley V, Turken U (2011) Are the neural correlates of stopping and not going identical? Quantitative meta-analysis of two response inhibition tasks. Neuroimage 56:1655-65.

170. Tadel F, Baillet S, Mosher JC, Pantazis D, & Leahy RM (2011) Brainstorm: a user-friendly application for MEG/EEG analysis. Comput Intell Neurosci, 2011, 8.

171. Thut G, Schyns P, Gross J (2011) Entrainment of perceptually relevant brain oscillations by non-invasive rhythmic stimulation of the human brain. Front Psychol 2:170.

172. Telpaz, A., & Yechiam, E. (2014). Contrasting losses and gains increases the predictability of behavior by frontal EEG asymmetry. Frontiers in behavioral neuroscience, 8.

173. Tom, S. M., Fox, C. R., Trepel, C., & Poldrack, R. A. (2007). The neural basis of loss aversion in decision-making under risk. Science, 315(5811), 515-18.

174. Tversky, A., & Fox, C. R. (1995). Weighing risk and uncertainty. Psychological review, 102(2), 269.

175. Tversky A, & Kahneman D. (1981). The framing of decisions and the psychology of choice. Science, 211, 453-57.

176. Turi Z, Ambrus GG, Janacsek K, Emmert K, Hahn L, Paulus W, Antal A (2013) Both the cutaneous sensation and phosphene perception are modulated in a frequency-specific manner during transcranial alternating current stimulation. Restor Neurol Neurosci 31(3):275-285.

177. Tversky A, Kahneman D (1985) The framing of decisions and the psychology of choice. In Environmental Impact Assessment, Technology Assessment, and Risk Analysis (pp. 107-129). Springer Berlin Heidelberg.

178. Umemoto, A., & Holroyd, C. B. (2015). Task-specific effects of gain on task switching. Psychological Research, 79, 698-707.

179. Van Noordt, S. J., Campopiano, A., & Segalowitz, S. J. (2016). A functional classification of medial frontal negativity ERPs: Theta oscillations and single subject effects. Psychophysiology, 53(9), 1317-34.

180. von Neumann, J., & Morgenstern, O. (1944). Theory of games and economic behavior. Princeton, NJ: Princeton University Press.

181. Veniero D, Vossen A, Gross J, Thut G (2015) Lasting EEG/MEG aftereffects of rhythmic transcranial brain stimulation: level of control over oscillatory network activity. Front Cell Neurosci 9: 477.

182. Verdejo-García AJ, Perales JC Pérez-García M (2007) Cognitive impulsivity in cocaine and heroin polysubstance abusers. Addict Behav 32(5):950-966.

183. Vosskuhl J, Huster RJ, Herrmann CS (2015) Increase in short-term memory capacity induced by down-regulating individual theta frequency via transcranial alternating current stimulation. Front Human Neurosci 9, 257.

184. Walsh, M. M., & Anderson, J. R. (2012). Learning from experience: event-related potential correlates of reward processing, neural adaptation, and behavioral choice. Neuroscience & Biobehavioral Reviews, 36(8), 1870-84.

185. Weaver SM, Arrington CM (2013) The effect of hierarchical task representations on task selection in voluntary task switching. J Exp Psychol Learn Mem Cogn 39(4):1128.

186. Welsh MC, Peterson E, Jameson MM (2017) History of childhood maltreatment and college academic outcomes: indirect effects of hot execution function. Front Psychol 8.

187. Whitney P, Rinehart CA, Hinson JM (2008) Framing effects under cognitive load: the role of working memory in risky decisions. Psychon Bull Rev 15(6): 1179-84.

188. Wu, Y., & Zhou, X. (2009). The P300 and reward valence, magnitude, and expectancy in outcome evaluation. Brain research, 1286, 114-22.

189. Yeomans MR, Brace A (2015) Cued to act on impulse: more impulsive choice and risky decision-making by women susceptible to overeating after exposure to food stimuli. PloS one 10(9):p.e0137626.

190. Yeung, N., & Sanfey, A. G. (2004). Independent coding of reward magnitude and valence in the human brain. Journal of Neuroscience, 24(28), 6258-64.

191. Yu R, & Zhang P (2014) Neural evidence for description dependent reward processing in the framing effect. Front Neurosci 8, 56.

192. Zhang D, Gu R, Broster LS, Jiang Y, Luo W, Zhang J, Luo Y (2014) Linking brain electrical signals elicited by current outcomes with future risk decision-making. Front Behav Neurosci 8:84.

193. Zheng, Y., Li, Q., Wang, K., Wu, H., & Liu, X. (2015). Contextual valence modulates the neural dynamics of risk processing. Psychophysiology, 52(7), 895-904.

194. Zhou, Z., Yu, R., & Zhou, X. (2010). To do or not to do? Action enlarges the FRN and P300 effects in outcome evaluation. Neuropsychologia, 48(12), 360613.