Greater decision uncertainty characterizes a transdiagnostic patient sample during approach-avoidance conflict: a computational modelling approach

Abstract

Background: Imbalances in approach-avoidance conflict (AAC) decision-making (e.g., sacrificing rewards to avoid negative outcomes) are considered central to multiple psychiatric disorders. We used computational modelling to examine 2 factors that are often not distinguished in descriptive analyses of AAC: decision uncertainty and sensitivity to negative outcomes versus rewards (emotional conflict). Methods: A previously validated AAC task was completed by 478 participants, including healthy controls (n = 59), people with substance use disorders (n = 159) and people with depression and/or anxiety disorders who did not have substance use disorders (n = 260). Using an active inference model, we estimated individual-level values for a model parameter that reflected decision uncertainty and another that reflected emotional conflict. We also repeated analyses in a subsample (59 healthy controls, 161 people with depression and/or anxiety disorders, 56 people with substance use disorders) that was propensity-matched for age and general intelligence. Results: The model showed high accuracy (72%). As further validation, parameters correlated with reaction times and self-reported task motivations in expected directions. The emotional conflict parameter further correlated with self-reported anxiety during the task (r = 0.32, p < 0.001), and the decision uncertainty parameter correlated with self-reported difficulty making decisions (r = 0.45, p < 0.001). Compared to healthy controls, people with depression and/or anxiety disorders and people with substance use disorders showed higher decision uncertainty in the propensity-matched sample (t = 2.16, p = 0.03, and t = 2.88, p = 0.005, respectively), with analogous results in the full sample; people with substance use disorders also showed lower emotional conflict in the full sample (t = 3.17, p = 0.002). Limitations: This study was limited by heterogeneity of the clinical sample and an inability to examine learning. Conclusion: These results suggest that reduced confidence in how to act, rather than increased emotional conflict, may explain maladaptive approach-avoidance behaviours in people with psychiatric disorders.

Active inference models do not contradict folk psychology

“…As such, if the semantics and functional role of desired outcomes is never inconsistent with the role of ( ), and the role of ( ) is always consistent with the semantics and functional role of desires, then active inference does effectively contain desired outcomes. This is consistent with recent empirical work that has used dAI to model behavior in reinforcement learning and reward-seeking tasks (Sajid, Ball, Parr, & Friston, 2021;Smith, Kirlic, et al, 2021;, and with other work demonstrating that dAI meets criteria for Bellman optimality (i.e., optimal reward-seeking within reinforcement learning) in certain limiting cases (Da Costa, Sajid, Parr, Friston, & Smith, 2020). In these cases, ( ) encodes the strength of the relative preferences for winning and losing money or points, being exposed to positive or negative emotional stimuli, and so forth.…”

Active inference models do not contradict folk psychology

“…As such, if the semantics and functional role of desired outcomes are never inconsistent with the role of p(o τ ), and the role of p(o τ ) is always consistent with the semantics and functional role of desires, then active inference does effectively contain desired outcomes. This is consistent with recent empirical work that has used dAI to model behavior in reinforcement learning and reward-seeking tasks (Markovic et al, 2021;Sajid et al, 2021;Smith et al, 2020Smith et al, , 2021aSmith et al, , 2021b, and with other work demonstrating that dAI meets criteria for Bellman optimality (i.e., optimal reward-seeking within reinforcement learning) in certain limiting cases (Da Cost et al, 2020b). In these cases, p(o τ ) is used to encode the strength of the relative preferences for winning and losing money or points (e.g., subjective reward value), being exposed to positive or negative emotional stimuli, and so forth.…”

“…In practice, several empirical studies have used model-fitting to identify the value of this precision in individual participants. For example, two studies in psychiatric samples fit this precision within the context of an approach-avoidance conflict task to identify differences in motivations to avoid exposure to unpleasant stimuli; and to identify continuous relationships between this precision and self-reported anxiety and decision uncertainty (Smith et al, 2021a(Smith et al, , 2021b. Two other studies in substance users identified individual differences in this precision value while participants performed a three-armed bandit task designed to examine the balance of information-versus reward-seeking behavior (Smith et al, 2020(Smith et al, , 2021c.…”

The Bayesian brain and cooperative communication in schizophrenia

“…The precision-driven active inference framework has been invoked in multiple transdiagnostic studies, including some published in JPN, 15,16 to explain abnormalities in perception, 17 interoception 18 and emotional expression, 19 to name a few. Here, we theoretically expand its application to disrupted communication in psychosis as a clinical phenomenon of interest.…”