1
|
Philippe R, Janet R, Khalvati K, Rao RPN, Lee D, Dreher JC. Neurocomputational mechanisms involved in adaptation to fluctuating intentions of others. Nat Commun 2024; 15:3189. [PMID: 38609372 PMCID: PMC11014977 DOI: 10.1038/s41467-024-47491-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 03/12/2024] [Indexed: 04/14/2024] Open
Abstract
Humans frequently interact with agents whose intentions can fluctuate between competition and cooperation over time. It is unclear how the brain adapts to fluctuating intentions of others when the nature of the interactions (to cooperate or compete) is not explicitly and truthfully signaled. Here, we use model-based fMRI and a task in which participants thought they were playing with another player. In fact, they played with an algorithm that alternated without signaling between cooperative and competitive strategies. We show that a neurocomputational mechanism with arbitration between competitive and cooperative experts outperforms other learning models in predicting choice behavior. At the brain level, the fMRI results show that the ventral striatum and ventromedial prefrontal cortex track the difference of reliability between these experts. When attributing competitive intentions, we find increased coupling between these regions and a network that distinguishes prediction errors related to competition and cooperation. These findings provide a neurocomputational account of how the brain arbitrates dynamically between cooperative and competitive intentions when making adaptive social decisions.
Collapse
Affiliation(s)
- Rémi Philippe
- CNRS-Institut des Sciences Cognitives Marc Jeannerod, UMR5229, Neuroeconomics, reward, and decision making laboratory, Lyon, France
- Université Claude Bernard Lyon 1, Lyon, France
| | - Rémi Janet
- CNRS-Institut des Sciences Cognitives Marc Jeannerod, UMR5229, Neuroeconomics, reward, and decision making laboratory, Lyon, France
- Université Claude Bernard Lyon 1, Lyon, France
| | - Koosha Khalvati
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - Rajesh P N Rao
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
- Center for Neurotechnology, University of Washington, Seattle, WA, USA
| | - Daeyeol Lee
- Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, MD, USA
- Kavli Discovery Neuroscience Institute, Johns Hopkins University, Baltimore, MD, USA
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University, Baltimore, MD, USA
| | - Jean-Claude Dreher
- CNRS-Institut des Sciences Cognitives Marc Jeannerod, UMR5229, Neuroeconomics, reward, and decision making laboratory, Lyon, France.
- Université Claude Bernard Lyon 1, Lyon, France.
| |
Collapse
|
2
|
Khalvati K, Kiani R, Rao RPN. Bayesian inference with incomplete knowledge explains perceptual confidence and its deviations from accuracy. Nat Commun 2021; 12:5704. [PMID: 34588440 PMCID: PMC8481237 DOI: 10.1038/s41467-021-25419-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 08/04/2021] [Indexed: 11/08/2022] Open
Abstract
In perceptual decisions, subjects infer hidden states of the environment based on noisy sensory information. Here we show that both choice and its associated confidence are explained by a Bayesian framework based on partially observable Markov decision processes (POMDPs). We test our model on monkeys performing a direction-discrimination task with post-decision wagering, demonstrating that the model explains objective accuracy and predicts subjective confidence. Further, we show that the model replicates well-known discrepancies of confidence and accuracy, including the hard-easy effect, opposing effects of stimulus variability on confidence and accuracy, dependence of confidence ratings on simultaneous or sequential reports of choice and confidence, apparent difference between choice and confidence sensitivity, and seemingly disproportionate influence of choice-congruent evidence on confidence. These effects may not be signatures of sub-optimal inference or discrepant computational processes for choice and confidence. Rather, they arise in Bayesian inference with incomplete knowledge of the environment.
Collapse
Affiliation(s)
- Koosha Khalvati
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - Roozbeh Kiani
- Center for Neural Science, New York University, New York, NY, USA
- Department of Psychology, New York University, New York, NY, USA
- Neuroscience Institute, NYU Langone Medical Center, New York, NY, USA
| | - Rajesh P N Rao
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
- Center for Neurotechnology, University of Washington, Seattle, WA, USA.
| |
Collapse
|
3
|
Barragan RC, Oliveira N, Khalvati K, Brooks R, Reinecke K, Rao RPN, Meltzoff AN. Identifying with all humanity predicts cooperative health behaviors and helpful responding during COVID-19. PLoS One 2021; 16:e0248234. [PMID: 33690679 PMCID: PMC7946174 DOI: 10.1371/journal.pone.0248234] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Accepted: 02/23/2021] [Indexed: 01/10/2023] Open
Abstract
In the ongoing COVID-19 pandemic, public health experts have produced guidelines to limit the spread of the coronavirus, but individuals do not always comply with experts' recommendations. Here, we tested whether a specific psychological belief-identification with all humanity-predicts cooperation with public health guidelines as well as helpful behavior during the COVID-19 pandemic. We hypothesized that peoples' endorsement of this belief-their relative perception of a connection and moral commitment to other humans-would predict their tendencies to adopt World Health Organization (WHO) guidelines and to help others. To assess this, we conducted a global online study (N = 2537 participants) of four WHO-recommended health behaviors and four pandemic-related moral dilemmas that we constructed to be relevant to helping others at a potential cost to oneself. We used generalized linear mixed models (GLMM) that included 10 predictor variables (demographic, contextual, and psychological) for each of five outcome measures (a WHO cooperative health behavior score, plus responses to each of our four moral, helping dilemmas). Identification with all humanity was the most consistent and consequential predictor of individuals' cooperative health behavior and helpful responding. Analyses showed that the identification with all humanity significantly predicted each of the five outcomes while controlling for the other variables (Prange < 10-22 to < 0.009). The mean effect size of the identification with all humanity predictor on these outcomes was more than twice as large as the effect sizes of other predictors. Identification with all humanity is a psychological construct that, through targeted interventions, may help scientists and policymakers to better understand and promote cooperative health behavior and help-oriented concern for others during the current pandemic as well as in future humanitarian crises.
Collapse
Affiliation(s)
- Rodolfo C. Barragan
- Department of Psychology, University of Washington, Seattle, WA, United States of America
- Institute for Learning & Brain Sciences, University of Washington, Seattle, WA, United States of America
| | - Nigini Oliveira
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, United States of America
| | - Koosha Khalvati
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, United States of America
| | - Rechele Brooks
- Institute for Learning & Brain Sciences, University of Washington, Seattle, WA, United States of America
| | - Katharina Reinecke
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, United States of America
| | - Rajesh P. N. Rao
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, United States of America
- Center for Neurotechnology, University of Washington, Seattle, WA, United States of America
| | - Andrew N. Meltzoff
- Department of Psychology, University of Washington, Seattle, WA, United States of America
- Institute for Learning & Brain Sciences, University of Washington, Seattle, WA, United States of America
| |
Collapse
|
4
|
Khalvati K, Park SA, Mirbagheri S, Philippe R, Sestito M, Dreher JC, Rao RPN. Modeling other minds: Bayesian inference explains human choices in group decision-making. Sci Adv 2019; 5:eaax8783. [PMID: 31807706 PMCID: PMC6881156 DOI: 10.1126/sciadv.aax8783] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 09/19/2019] [Indexed: 05/06/2023]
Abstract
To make decisions in a social context, humans have to predict the behavior of others, an ability that is thought to rely on having a model of other minds known as "theory of mind." Such a model becomes especially complex when the number of people one simultaneously interacts with is large and actions are anonymous. Here, we present results from a group decision-making task known as the volunteer's dilemma and demonstrate that a Bayesian model based on partially observable Markov decision processes outperforms existing models in quantitatively predicting human behavior and outcomes of group interactions. Our results suggest that in decision-making tasks involving large groups with anonymous members, humans use Bayesian inference to model the "mind of the group," making predictions of others' decisions while also simulating the effects of their own actions on the group's dynamics in the future.
Collapse
Affiliation(s)
- Koosha Khalvati
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - Seongmin A. Park
- Center for Mind and Brain, University of California, Davis, Davis, CA, USA
- Neuroeconomics Laboratory, Institut des Sciences Cognitives Marc Jeannerod, Lyon, France
| | | | - Remi Philippe
- Neuroeconomics Laboratory, Institut des Sciences Cognitives Marc Jeannerod, Lyon, France
| | - Mariateresa Sestito
- Neuroeconomics Laboratory, Institut des Sciences Cognitives Marc Jeannerod, Lyon, France
| | - Jean-Claude Dreher
- Neuroeconomics Laboratory, Institut des Sciences Cognitives Marc Jeannerod, Lyon, France
| | - Rajesh P. N. Rao
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
- Center for Neurotechnology, University of Washington, Seattle, WA, USA
- Corresponding author.
| |
Collapse
|