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Sensorimotor features of a reversal learning task bias decision behavior without disrupting individual difference structure

Aug 2026 · 0 citations · 27 references
Biology

TL;DR

Prior sensorimotor history does not compromise the task's ability to extract stable cognitive variables, but does bias the values it yields - a non-random source of variance that could shift patients across diagnostic thresholds in proposed clinical applications.

Abstract

In computational psychiatry, task-irrelevant factors such as a task's perceptual and motor features are typically assumed not to bias decision behavior, but at most to add noise. Yet growing evidence links sensorimotor processing to decision-making through multiple pathways, challenging this assumption. We tested this directly using a two-choice probabilistic reversal learning task completed by 90 participants under two sensorimotor conditions: a stationary condition requiring only arm movements to respond, and an active condition requiring participants to walk between physically separated computers. Task performance, measured as the propensity to choose the more probable rewarding option, did not differ between conditions. However, stay rate (the tendency to repeat the previous choice) was significantly elevated in the stationary condition, but only among participants who completed the active condition first; those who completed the stationary condition first showed no such difference. Logistic regression weights capturing the influence of recent win history on choice showed the same pattern, with stationary-condition elevation restricted to the active-first group. Cross-condition correlations for both measures were strong, indicating that this order-dependent effect shifted absolute measurements without disrupting individual difference structure. Prior sensorimotor history therefore does not compromise the task's ability to extract stable cognitive variables, but does bias the values it yields - a non-random source of variance that, if it generalizes beyond this experiment, could shift patients across diagnostic thresholds in proposed clinical applications. The finding adds to a growing list of potential methodological contaminants in computational psychiatry's cognitive-task paradigms that must be characterized and mitigated before clinical translation.

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