Individual Differences in Decision-Making: A Neural Trait Approach to Study Sources of Behavioral Heterogeneity

Nash, Kyle; Knoch, Daria

doi:10.1007/978-3-642-35923-1_11

Cited by 10 publications

(9 citation statements)

References 103 publications

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“…Decision-making models usually describe aggregate behavior; yet people evidence quite divergent behavior, even in identical situations (Scheres and Sanfey, 2006 ; Wischniewski et al, 2009 ; Levallois et al, 2012 ). Neural traits are well suited to capture this variance in decision-making (Nash and Knoch, 2015 ). The second goal is to add a level of analysis that supplements and is informed by task-dependent analyses of neural and psychological processes (Berkman and Falk, 2013 ).…”

Section: The Neural Trait Approachmentioning

confidence: 99%

A neural trait approach to exploring individual differences in social preferences

Nash

Gianotti

Knoch

2015

Front. Behav. Neurosci.

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Research demonstrates that social preferences are characterized by significant individual differences. An important question, often overlooked, is from where do these individual differences originate? And what are the processes that underlie such differences? In this paper, we outline the neural trait approach to uncovering sources of individual differences in social preferences, particularly as evidenced in economic games. We focus on two primary methods—resting-state electroencephalography and structural magnetic resonance imaging (MRI)—used by researchers to quantify task-independent, brain-based characteristics that are stable over time. We review research that has employed these methods to investigate social preferences with an emphasis on a key psychological process in social decision-making; namely, self-control. We then highlight future opportunities for the neural trait approach in cutting-edge decision-making research. Finally, we explore the debate about self-control in social decision-making and the potential role neural trait research could play in this issue.

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Section: The Neural Trait Approachmentioning

confidence: 99%

A neural trait approach to exploring individual differences in social preferences

Nash

Gianotti

Knoch

2015

Front. Behav. Neurosci.

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“…Behavioral heterogeneity is a pervasive feature of risk taking and decision-making. A neural trait approach suggests that heterogeneity in behavior can be at least partially explained by stable brain-based characteristics of individuals ( Nash and Knoch, 2016 ). It was reported that, on average, females take fewer risks than males (e.g., Jianakoplos and Bernasek, 1998 ; Charness and Gneezy, 2012 ).…”

Section: Introductionmentioning

confidence: 99%

Resting-State Theta Oscillations and Reward Sensitivity in Risk Taking

et al. 2021

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Females demonstrate greater risk aversion than males on a variety of tasks, but the underlying neurobiological basis is still unclear. We studied how theta (4–7 Hz) oscillations at rest related to three different measures of risk taking. Thirty-five participants (15 females) completed the Bomb Risk Elicitation Task (BRET), which allowed us to measure risk taking during an economic game. The Domain-Specific Risk-Taking Scale (DOSPERT) was used to measure self-assessed risk attitudes as well as reward and punishment sensitivities. In addition, the Barratt Impulsiveness Scale (BIS11) was included to quantify impulsiveness. To obtain measures of frontal theta asymmetry and frontal theta power, we used magnetoencephalography (MEG) acquired prior to task completion, while participants were at rest. Frontal theta asymmetry correlated with average risk taking during the game but only in the female sample. By contrast, frontal theta power correlated with risk taking as well as with measures of reward and punishment sensitivity in the joint sample. Importantly, we showed that reward sensitivity mediated a correlation between risk taking and the power of theta oscillations localized to the anterior cingulate cortex. In addition, we observed significant sex differences in source- and sensor-space theta power, risk taking during the game, and reward sensitivity. Our findings suggest that sensitivity to rewards, associated with resting-state theta oscillations in the anterior cingulate cortex, is a trait that potentially contributes to sex differences in risk taking.

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“…The neuropsychological underpinnings of trust have been studied dominantely by utilizing task‐based fMRI, but it remains obscure whether trust propensity can be predicted by task‐free fMRI based on resting‐state functional connectivity (RSFC). As a task‐independent measure, RSFC is free from confounds associated with ongoing task demand and different experimental designs across studies (Kable & Levy, ; Nash, Gianotti, & Knoch, ; Nash & Knoch, ). Moreover, fMRI‐RSFC has emerged as a widely‐used network‐level approach to significantly advance our understanding of individual variations in cognitive functions, personality traits, and behaviors (Feng, Yuan, et al, ; Feng, Zhu, et al, ; Finn et al, ; Gianotti, Lobmaier, Calluso, Dahinden, & Knoch, ; Gianotti, Nash, Baumgartner, Dahinden, & Knoch, ; Hsu, Rosenberg, Scheinost, Constable, & Chun, ; Jung, Lee, Lerman, & Kable, ; Rosenberg et al, ; Wang et al, ).…”

Section: Introductionmentioning

confidence: 99%

Connectome‐based model predicts individual differences in propensity to trust

Xia

et al. 2019

Human Brain Mapping

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Trust constitutes a fundamental basis of human society and plays a pivotal role in almost every aspect of human relationships. Although enormous interest exists in determining the neuropsychological underpinnings of a person's propensity to trust utilizing task‐based fMRI; however, little progress has been made in predicting its variations by task‐free fMRI based on whole‐brain resting‐state functional connectivity (RSFC). Here, we combined a one‐shot trust game with a connectome‐based predictive modeling approach to predict propensity to trust from whole‐brain RSFC. We demonstrated that individual variations in the propensity to trust were primarily predicted by RSFC rooted in the functional integration of distributed key nodes—caudate, amygdala, lateral prefrontal cortex, temporal–parietal junction, and the temporal pole—which are part of domain‐general large‐scale networks essential for the motivational, affective, and cognitive aspects of trust. We showed, further, that the identified brain‐behavior associations were only evident for trust but not altruistic preferences and that propensity to trust (and its underlying neural underpinnings) were modulated according to the extent to which a person emphasizes general social preferences (i.e., horizontal collectivism) rather than general risk preferences (i.e., trait impulsiveness). In conclusion, the employed data‐driven approach enables to predict propensity to trust from RSFC and highlights its potential use as an objective neuromarker of trust impairment in mental disorders.

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Individual Differences in Decision-Making: A Neural Trait Approach to Study Sources of Behavioral Heterogeneity

Cited by 10 publications

References 103 publications

A neural trait approach to exploring individual differences in social preferences

A neural trait approach to exploring individual differences in social preferences

Resting-State Theta Oscillations and Reward Sensitivity in Risk Taking

Connectome‐based model predicts individual differences in propensity to trust

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