Computational Models in Social Neuroscience

Cheong, Jin Hyun; Jolly, Eshin; Sul, Sunhae; Chang, Luke J.

doi:10.1002/9781119159193.ch17

Cited by 25 publications

(20 citation statements)

References 85 publications

(89 reference statements)

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“…Implicit and explicit signals thus both play a role in decisions to trust. Increases in the use of computational modeling approaches (Kishida and Montague, 2012; Cheong et al, 2017) may shed light on the interaction between these different types of signals. One hypothesis suggests that a choice to place trust in another is not static, but rather dynamically evolves over time as we update initial implicit appraisals of others with explicitly experienced patterns of reciprocity (Chang et al, 2010) using associative mechanisms that enable learning the value of a partner on a trial-by-trial basis (Rescorla and Wagner, 1972).…”

Section: Decisions To Trust Othersmentioning

confidence: 99%

Neurobehavioral Mechanisms Supporting Trust and Reciprocity

Fareri

2019

Front. Hum. Neurosci.

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Trust and reciprocity are cornerstones of human nature, both at the levels of close interpersonal relationships and economic/societal structures. Being able to both place trust in others and decide whether to reciprocate trust placed in us is rooted in implicit and explicit processes that guide expectations of others, help reduce social uncertainty, and build relationships. This review will highlight neurobehavioral mechanisms supporting trust and reciprocity, through the lens of implicit and explicit social appraisal and learning processes. Significant consideration will be given to the neural underpinnings of these implicit and explicit processes, and special focus will center on the underlying neurocomputational mechanisms facilitating the integration of implicit and explicit signals supporting trust and reciprocity. Finally, this review will conclude with a discussion of how we can leverage findings regarding the neurobehavioral mechanisms supporting trust and reciprocity to better inform our understanding of mental health disorders characterized by social dysfunction.

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Section: Decisions To Trust Othersmentioning

confidence: 99%

Neurobehavioral Mechanisms Supporting Trust and Reciprocity

Fareri

2019

Front. Hum. Neurosci.

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“…Third, adjusting behaviour requires expectations to be updated in response to new information. Updating of expectations in social environments can be captured by reinforcement learning (RL) models (e.g., [28][29][30], in which learning is driven by differences between expected and received rewards (i.e., prediction errors). Adolescence is characterized by substantial improvements in flexible learning and quick adaptation to novel non-social contexts ( [31][32][33]; whether this extends to the social domain, however, is still unclear (but see 34 ).…”

Section: Introductionmentioning

confidence: 99%

Conducta adaptativa en niños con trastorno del espectro autista y su efecto sobre la calidad de vida familiar

et al. 2020

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Learning to successfully navigate social environments is a critical developmental goal, predictive of long-term wellbeing. However, little is known about how people learn to adjust to different social environments, and how this behaviour emerges across development. Here, we use a series of economic games to assess how children, adolescents, and young adults learn to adjust to social environments that differ in their level of cooperation (i.e., trust and coordination). Our results show an asymmetric developmental pattern: adjustment requiring uncooperative behaviour remains constant across adolescence, but adjustment requiring cooperative behaviour improves markedly across adolescence. Behavioural and computational analyses reveal that age-related differences in this social learning are shaped by age-related differences in the degree of inequality aversion and in the updating of beliefs about others. Our findings point to early adolescence as a phase of rapid change in cooperative behaviours, and highlight this as a key developmental window for interventions promoting well-adjusted social behaviour. .

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“…Studies in the field of social neuroscience have begun to apply these models to understand how and whether quantities predicted by RL are represented in the brain during social situations (Behrens et al, 2008;Hampton et al, 2008;Burke et al, 2010;Suzuki et al, 2012;Seo et al, 2014;Apps et al, 2015;Hackel et al, 2015;Sul et al, 2015;Kumaran et al, 2016;Lockwood et al, 2016;Spiers et al, 2016;Wittmann et al, 2016;Zaki et al, 2016;Cheong et al, 2017;Hill et al, 2017;Charpentier and O'Doherty, 2018;Konovalov et al, 2018;Lindström et al, 2018;Wittmann et al, 2018;Yoon et al, 2018;Farmer et al, 2019;Lockwood et al, 2019). The implementation of these models has already provided important new insights into multiple aspects of social behavior.…”

Section: Introductionmentioning

confidence: 99%

Computational modelling of social cognition and behaviour – a reinforcement learning primer

Lockwood¹,

Klein-Flügge²

2019

Preprint

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Social neuroscience aims to describe the neural systems that underpin social cognition and behaviour. Over the past decade, researchers have begun to combine computational models with neuroimaging to link social computations to the brain. Inspired by approaches from reinforcement learning theory, which describes how decisions are driven by the unexpectedness of outcomes, accounts of the neural basis of prosocial learning, observational learning, mentalising and impression formation have been developed. Here we provide an introduction for researchers who wish to use these models in their studies. We consider both theoretical and practical issues related to their implementation, with a focus on specific examples from the field.

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Computational Models in Social Neuroscience

Cited by 25 publications

References 85 publications

Neurobehavioral Mechanisms Supporting Trust and Reciprocity

Neurobehavioral Mechanisms Supporting Trust and Reciprocity

Conducta adaptativa en niños con trastorno del espectro autista y su efecto sobre la calidad de vida familiar

Computational modelling of social cognition and behaviour – a reinforcement learning primer

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