Humans use forward thinking to exploit social controllability

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Abstract

The controllability of our social environment has a profound impact on our behavior and mental health. Nevertheless, neurocomputational mechanisms underlying social controllability remain elusive. Here, 48 participants performed a task where their current choices either did (Controllable), or did not (Uncontrollable), influence partners’ future proposals. Computational modeling revealed that people engaged a mental model of forward thinking (FT; i.e., calculating the downstream effects of current actions) to estimate social controllability in both Controllable and Uncontrollable conditions. A large-scale online replication study (n=1342) supported this finding. Using functional magnetic resonance imaging (n=48), we further demonstrated that the ventromedial prefrontal cortex (vmPFC) computed the projected total values of current actions during forward planning, supporting the neural realization of the forward-thinking model. These findings demonstrate that humans use vmPFC-dependent FT to estimate and exploit social controllability, expanding the role of this neurocomputational mechanism beyond spatial and cognitive contexts.

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Most cited references 70

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A Theory of Fairness, Competition, and Cooperation

E. Fehr, K. Schmidt (1999)

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The valuation system: a coordinate-based meta-analysis of BOLD fMRI experiments examining neural correlates of subjective value.

Oscar Bartra, Joseph T McGuire, Joseph W Kable (2013)

Numerous experiments have recently sought to identify neural signals associated with the subjective value (SV) of choice alternatives. Theoretically, SV assessment is an intermediate computational step during decision making, in which alternatives are placed on a common scale to facilitate value-maximizing choice. Here we present a quantitative, coordinate-based meta-analysis of 206 published fMRI studies investigating neural correlates of SV. Our results identify two general patterns of SV-correlated brain responses. In one set of regions, both positive and negative effects of SV on BOLD are reported at above-chance rates across the literature. Areas exhibiting this pattern include anterior insula, dorsomedial prefrontal cortex, dorsal and posterior striatum, and thalamus. The mixture of positive and negative effects potentially reflects an underlying U-shaped function, indicative of signal related to arousal or salience. In a second set of areas, including ventromedial prefrontal cortex and anterior ventral striatum, positive effects predominate. Positive effects in the latter regions are seen both when a decision is confronted and when an outcome is delivered, as well as for both monetary and primary rewards. These regions appear to constitute a "valuation system," carrying a domain-general SV signal and potentially contributing to value-based decision making. Copyright © 2013 Elsevier Inc. All rights reserved.

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Model-based influences on humans’ choices and striatal prediction errors

Nathaniel D. Daw, Samuel Gershman, Ben Seymour … (2011)

Summary The mesostriatal dopamine system is prominently implicated in model-free reinforcement learning, with fMRI BOLD signals in ventral striatum notably covarying with model-free prediction errors. However, latent learning and devaluation studies show that behavior also shows hallmarks of model-based planning, and the interaction between model-based and model-free values, prediction errors and preferences is underexplored. We designed a multistep decision task in which model-based and model-free influences on human choice behavior could be distinguished. By showing that choices reflected both influences we could then test the purity of the ventral striatal BOLD signal as a model-free report. Contrary to expectations, the signal reflected both model-free and model-based predictions in proportions matching those that best explained choice behavior. These results challenge the notion of a separate model-free learner and suggest a more integrated computational architecture for high-level human decision-making.

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Author and article information

Contributors

Xiaosi Gu:

ORCID: https://orcid.org/0000-0002-9373-987X

Catherine Hartley: Role: Reviewing Editor

Christian Büchel: Role: Senior Editor

Journal

Journal ID (nlm-ta): eLife

Journal ID (iso-abbrev): Elife

Journal ID (publisher-id): eLife

Title: eLife

Publisher: eLife Sciences Publications, Ltd

ISSN (Electronic): 2050-084X

Publication date (Electronic, pub): 29 October 2021

Publication date Collection: 2021

Volume: 10

Electronic Location Identifier: e64983

Affiliations

[1 ] The Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai New York United States

[2 ] Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai New York United States

[3 ] Department of Psychiatry, Icahn School of Medicine at Mount Sinai New York United States

[4 ] Department of Biomedical Engineering, Ulsan National Institute of Science and Technology Ulsan Republic of Korea

[5 ] Austrian Institute of Technology Seibersdorf Austria

[6 ] School of Behavioral and Brain Sciences, The University of Texas at Dallas Richardson United States

[7 ] Queen Square Institute of Neurology, University College London London United Kingdom

[8 ] Max Planck Institute for Biological Cybernetics Tübingen Germany

[9 ] University of Tübingen Tübingen Germany

New York University United States

University Medical Center Hamburg-Eppendorf Germany

New York University United States

United States

Author notes

[†]

These authors contributed equally to this work.

Author information

Soojung Na https://orcid.org/0000-0003-2565-5524

Dongil Chung https://orcid.org/0000-0003-1999-0326

Ofer Perl https://orcid.org/0000-0002-3560-4344

Matthew Heflin https://orcid.org/0000-0002-0379-3582

Vincenzo G Fiore https://orcid.org/0000-0002-4865-5482

Peter Dayan https://orcid.org/0000-0003-3476-1839

Xiaosi Gu https://orcid.org/0000-0002-9373-987X

Article

Publisher ID: 64983

DOI: 10.7554/eLife.64983

PMC ID: 8555988

PubMed ID: 34711304

SO-VID: 3ce95fa6-6bbe-4404-b6bf-3fc32b01d720

License:

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

History

Date received : 18 November 2020

Date accepted : 30 September 2021

Funding

Funded by: FundRef http://dx.doi.org/10.13039/100000026, National Institute on Drug Abuse;

Award ID: R01DA043695

Award Recipient : Xiaosi Gu

Funded by: FundRef http://dx.doi.org/10.13039/100000026, National Institute on Drug Abuse;

Award ID: R21DA049243

Award Recipient : Xiaosi Gu

Funded by: FundRef http://dx.doi.org/10.13039/100000025, National Institute of Mental Health;

Award ID: R01MH124115

Award Recipient : Xiaosi Gu

Funded by: FundRef http://dx.doi.org/10.13039/100000025, National Institute of Mental Health;

Award ID: R01MH123069

Award Recipient : Xiaosi Gu

Funded by: FundRef http://dx.doi.org/10.13039/501100004189, Max Planck Society;

Award Recipient : Peter Dayan

Funded by: FundRef http://dx.doi.org/10.13039/100005156, Alexander von Humboldt Foundation;

Award Recipient : Peter Dayan

Funded by: FundRef http://dx.doi.org/10.13039/100000025, National Institute of Mental Health;

Award ID: R21MH120789

Award Recipient : Xiaosi Gu

Funded by: FundRef http://dx.doi.org/10.13039/100000025, National Institute of Mental Health;

Award ID: R01MH122611

Award Recipient : Xiaosi Gu

Funded by: FundRef http://dx.doi.org/10.13039/501100002613, Ulsan National Institute of Science and Technology;

Award ID: 1.180073.01

Award Recipient : Dongil Chung

Funded by: FundRef http://dx.doi.org/10.13039/501100003725, National Research Foundation of Korea;

Award ID: NRF-2018R1D1A1B07043582

Award Recipient : Dongil Chung

Funded by: Mental Illness Research, Education, and Clinical Center (MIRECC VISN 2), James J. Peter Veterans Affairs Medical Center;

Award Recipient : Vincenzo G Fiore

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Custom metadata

Author impact statement People use vmPFC-dependent forward thinking to guide social choices and exploit the controllability of social environments, expanding the role of this neurocomputational mechanism beyond spatial and cognitive mapping.

ScienceOpen disciplines: Life sciences

Keywords: social decision-making,controllability,forward thinking,model-based planning,vmpfc,computational modeling,human

Data availability:

ScienceOpen disciplines: Life sciences

Keywords: social decision-making, controllability, forward thinking, model-based planning, vmpfc, computational modeling, human

Humans use forward thinking to exploit social controllability

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Thermal Imaging in Social Decision Making

Most cited references 70

A Theory of Fairness, Competition, and Cooperation

The valuation system: a coordinate-based meta-analysis of BOLD fMRI experiments examining neural correlates of subjective value.

Model-based influences on humans’ choices and striatal prediction errors

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