Inferring on the intentions of others by hierarchical Bayesian learning
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...
| Main Authors: | , , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Public Library of Science
2014
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| _version_ | 1826308913026301952 |
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| author | Diaconescu, AO Mathys, C Weber, LAE Daunizeau, J Kasper, L Lomakina, EI Fehr, E Stephan, KE |
| author_facet | Diaconescu, AO Mathys, C Weber, LAE Daunizeau, J Kasper, L Lomakina, EI Fehr, E Stephan, KE |
| author_sort | Diaconescu, AO |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-07T07:26:16Z |
| format | Journal article |
| id | oxford-uuid:d7577e77-bbbf-4f7a-a0c0-c4f48d071ee8 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:26:16Z |
| publishDate | 2014 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | oxford-uuid:d7577e77-bbbf-4f7a-a0c0-c4f48d071ee82022-11-15T13:42:42ZInferring on the intentions of others by hierarchical Bayesian learning Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:d7577e77-bbbf-4f7a-a0c0-c4f48d071ee8EnglishSymplectic ElementsPublic Library of Science2014Diaconescu, AOMathys, CWeber, LAEDaunizeau, JKasper, LLomakina, EIFehr, EStephan, KEInferring 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. |
| spellingShingle | Diaconescu, AO Mathys, C Weber, LAE Daunizeau, J Kasper, L Lomakina, EI Fehr, E Stephan, KE Inferring on the intentions of others by hierarchical Bayesian learning |
| title | Inferring on the intentions of others by hierarchical Bayesian learning
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| title_full | Inferring on the intentions of others by hierarchical Bayesian learning
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| title_fullStr | Inferring on the intentions of others by hierarchical Bayesian learning
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| title_full_unstemmed | Inferring on the intentions of others by hierarchical Bayesian learning
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| title_short | Inferring on the intentions of others by hierarchical Bayesian learning
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| title_sort | inferring on the intentions of others by hierarchical bayesian learning |
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