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Neurocomputational mechanisms of confidence in self and others.

ABSTRACT: Computing confidence in one's own and others' decisions is critical for social success. While there has been substantial progress in our understanding of confidence estimates about oneself, little is known about how people form confidence estimates about others. Here, we address this question by asking participants undergoing fMRI to place bets on perceptual decisions made by themselves or one of three other players of varying ability. We show that participants compute confidence in another player's decisions by combining distinct estimates of player ability and decision difficulty - allowing them to predict that a good player may get a difficult decision wrong and that a bad player may get an easy decision right. We find that this computation is associated with an interaction between brain systems implicated in decision-making (LIP) and theory of mind (TPJ and dmPFC). These results reveal an interplay between self- and other-related processes during a social confidence computation.

SUBMITTER: Bang D

PROVIDER: S-EPMC9307648 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Neurocomputational mechanisms of confidence in self and others.

Bang Dan D Moran Rani R Daw Nathaniel D ND Fleming Stephen M SM

Nature communications 20220722 1

Computing confidence in one's own and others' decisions is critical for social success. While there has been substantial progress in our understanding of confidence estimates about oneself, little is known about how people form confidence estimates about others. Here, we address this question by asking participants undergoing fMRI to place bets on perceptual decisions made by themselves or one of three other players of varying ability. We show that participants compute confidence in another play ...[more]

PMID: 35869044

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Project description:Over the last two decades, neuroscientists have used antidepressant placebo probes to examine the biological mechanisms implicated in antidepressant placebo effects. However, findings from these studies have not yet elucidated a model-based theory that would explain the mechanism through which antidepressant expectancies evolve to induce persistent mood changes. Emerging evidence suggests that antidepressant placebo effects may be informed by models of reinforcement learning (RL). Such that an individual's expectation of improvement is updated with the arrival of new sensory evidence, by incorporating a reward prediction error (RPE), which signals the mismatch between the expected (expected value) and perceived improvement. Consistent with this framework, neuroimaging studies of antidepressant placebo effects have demonstrated placebo-induced μ-opioid activation and increased blood-oxygen-level dependent (BOLD) responses in regions tracking expected values (e.g., ventromedial prefrontal cortex (vmPFC)) and RPEs (e.g., ventral striatum (VS)). In this study, we will demonstrate the causal contribution of reward learning signals (expected values and RPEs) to antidepressant placebo effects by experimentally manipulating expected values using transcranial magnetic stimulation (TMS) targeting the vmPFC and μ-opioid striatal RPE signal using pharmacological approaches. We hypothesized that antidepressant placebo expectancies are represented in the vmPFC (expected value) and updated by means of μ-opioid-modulated striatal learning signal. In a 3 × 3 factorial double-blind design, we will randomize 120 antidepressant-free individuals with depressive symptoms to one of three between-subject opioid conditions: the μ-opioid agonist buprenorphine, the μ-opioid antagonist naltrexone, or an inert pill. Within each arm, individuals will be assigned to receive three within-subject counterbalanced forms of TMS targeting the vmPFC-intermittent Theta Burst Stimulation (TBS) expected to potentiate the vmPFC, continuous TBS expected to de-potentiate the vmPFC, or sham TBS. These experimental manipulations will be used to modulate trial-by-trial reward learning signals and related brain activity during the Antidepressant Placebo functional MRI (fMRI) Task to address the following aims: (1) investigate the relationship between reward learning signals within the vmPFC-VS circuit and antidepressant placebo effects; (2) examine the causal contribution of vmPFC expected value computations to antidepressant placebo effects; and (3) investigate the causal contribution of μ-opioid-modulated striatal RPEs to antidepressant placebo effects. The proposed study will be the first to investigate the causal contribution of μ-opioid-modulated vmPFC-VS learning signals to antidepressant placebo responses, paving the way for developing novel treatments modulating learning processes and objective means of quantifying and potentially reducing placebo effects during drug development. ClinicalTrials.gov Identifier: NCT04276259.

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Neurocomputational mechanisms of confidence in self and others.

Publications

Neurocomputational mechanisms of confidence in self and others.

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