Inferring on the intentions of others by hierarchical bayesian learning
| Autor: | Diaconescu, Andreea O, Mathys, Christoph, Weber, Lilian A E, Daunizeau, Jean, Kasper, Lars, Lomakina, Ekaterina I, Fehr, Ernst, Stephan, Klaas E |
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| Přispěvatelé: | University of Zurich, Diaconescu, Andreea O |
| Rok vydání: | 2014 |
| Předmět: |
Adult
Male Decision Making 2804 Cellular and Molecular Neuroscience 610 Medicine & health Intention 170 Ethics Experimental Young Adult 1311 Genetics Models 10007 Department of Economics 1312 Molecular Biology Humans Learning 10237 Institute of Biomedical Engineering Social Behavior lcsh:QH301-705.5 Motivation Bayes Theorem Games Experimental Models Psychological Settore M-PSI/02 - Psicobiologia e Psicologia Fisiologica 1105 Ecology Evolution Behavior and Systematics lcsh:Biology (General) Psychological Games 2303 Ecology 2611 Modeling and Simulation 1703 Computational Theory and Mathematics |
| Zdroj: | PLoS Computational Biology, Vol 10, Iss 9, p e1003810 (2014) PLoS Computational Biology PLoS Computational Biology, 10 (9) |
| ISSN: | 1553-734X 1553-7358 |
| DOI: | 10.5167/uzh-103974 |
| Popis: | Inferring on others' (potentially time-varying) intentions is a fundamental problem during many social transactions. To investigate the underlying mechanisms, we applied computational modeling to behavioral data from an economic game in which 16 pairs of volunteers (randomly assigned to “player” or “adviser” roles) interacted. The player performed a probabilistic reinforcement learning task, receiving information about a binary lottery from a visual pie chart. The adviser, who received more predictive information, issued an additional recommendation. Critically, the game was structured such that the adviser's incentives to provide helpful or misleading information varied in time. Using a meta-Bayesian modeling framework, we found that the players' behavior was best explained by the deployment of hierarchical learning: they inferred upon the volatility of the advisers' intentions in order to optimize their predictions about the validity of their advice. Beyond learning, volatility estimates also affected the trial-by-trial variability of decisions: participants were more likely to rely on their estimates of advice accuracy for making choices when they believed that the adviser's intentions were presently stable. Finally, our model of the players' inference predicted the players' interpersonal reactivity index (IRI) scores, explicit ratings of the advisers' helpfulness and the advisers' self-reports on their chosen strategy. Overall, our results suggest that humans (i) employ hierarchical generative models to infer on the changing intentions of others, (ii) use volatility estimates to inform decision-making in social interactions, and (iii) integrate estimates of advice accuracy with non-social sources of information. The Bayesian framework presented here can quantify individual differences in these mechanisms from simple behavioral readouts and may prove useful in future clinical studies of maladaptive social cognition. PLoS Computational Biology, 10 (9) ISSN:1553-734X ISSN:1553-7358 |
| Databáze: | OpenAIRE |
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