Poster No:
816
Submission Type:
Abstract Submission
Authors:
Xiaoyan WU1, Niklas Bürgi2, Gökhan Aydogan2, Chao Liu3, Christian Ruff2
Institutions:
1Beijing Normal University, Beijing, GU, 2University of Zurich, Zurich, Zurich, 3Beijing Normal University, Beijing, Beijing
First Author:
Co-Author(s):
Chao Liu
Beijing Normal University
Beijing, Beijing
Introduction:
Objective: People often assess others' trustworthiness based on past interactions, but existing research has typically focused only on single aspects of such experiences. What is lacking is a comprehensive model of how people decide to trust based on different aspects of past social interactions. Here we introduce such an experimental and modelling framework, which systematically evaluates how individuals decide to trust based on three key social experiences: Generosity, favoritism, and reward. This framework may be useful for studying individual differences and pathologies of trust.
Methods:
Methods: Investors and trustees played a modified trust game preceded by various rounds of laboratory interactions. The investors first had to complete various political and personal questionnaires that allowed us to build real social profiles of them. After 3-5 days, the trustees were shown pairs of the investors' social profiles and had to allocate varying amounts of money between them. After another 3-5 days, the investors were asked to play a trust game with the trustees during the scanning. In each round, investors were asked make the investment decision after they were showed the trustee's generosity (how much they shared with both investors), favoritism (how much they allocated to the investor), and reward (the final amount the investor received) in past interaction. In various classes of computational models, we investigated how the resulting trust decisions depended on these three types of social experiences, over and above general social preferences and betrayal aversion.
Results:
Results: Participants increased their investment with larger rewards (linear mixed-effects model, p<0.001) and generosity (p<0.001). An interaction of generosity × favoritism (p<0.001) indicated specific trust if generous trustees previously favored the investor. Computational modeling showed that participants' decisions are best described by a model with social preferences and betrayal aversion. Subjective value from the betrayal aversion model is primarily represented in the dlPFC, Parietal Cortex, NAcc, and Precuneus (left panel). Generosity and Favoritism are differentially represented when making trust decisions. While Generosity is primarily represented in the left TPJ and right dlPFC, Favoritism, in addition to these regions, is also represented in reward areas such as the ventral striatum and anterior insula.
Conclusions:
Conclusion: People systematically evaluate and infer the social characteristics of others from previous interactions and use this information to guide their future behavior. Our new model formalizes how these past experiences overcome betrayal aversion to facilitate trust. The model predictions are validated by brain imaging to show that different past experiences are represented in distinct brain networks. Our paradigm and model extend the understanding of the cognitive components underlying trust and may inform interventions aimed at fostering trust in interpersonal interactions. The experimental and modelling approach may be useful for studying pathological alterations of trust in patient populations.
Emotion, Motivation and Social Neuroscience:
Social Cognition
Social Interaction 1
Social Neuroscience Other 2
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Computational Neuroscience
Modeling
Social Interactions
1|2Indicates the priority used for review
Provide references using author date format
Bradshaw, A. R., & McGettigan, C. (2021). Instrumental learning in social interactions: Trait learning from faces and voices. Quarterly Journal of Experimental Psychology, 74(8), 1344-1359.
Hackel, L. M., Doll, B. B., & Amodio, D. M. (2015). Instrumental learning of traits versus rewards: dissociable neural correlates and effects on choice. Nature Neuroscience, 18(9), 1233-1235.