Reduced prediction updating shapes serial dependence in autistic traits.

backgroundSerial dependence, the influence of prior experience on current perception or decision, has typically been studied in static, perceptual contexts. Here, we investigate whether serial dependence reflects not just passive carryover but feedback-based updating of internal models, and how this process varies with autistic traits. In an immersive virtual reality penalty-kick task, participants kicked a ball that disappeared mid-flight and estimated its landing position. By laterally displacing the ball upon reappearance, we introduced trial-by-trial prediction errors.

resultsWe found that individuals with higher autistic traits showed larger prediction deviations, indicating mis-calibrated forward predictions. At the same time, their responses were more strongly shaped by those priors, and unlike lower autistic traits individuals, they did not down-weight reliance when distortions were maximal. This pattern suggests reduced flexibility in updating prediction use: priors were both less accurate and more rigidly applied. Classical stimulus and response history biases were unaffected by autistic traits, highlighting a specific impairment in prediction updating. Football experts, by contrast, combined low directional updating with near-zero prediction consistency, suggesting robust mappings that resist transient perturbations.

conclusionsThese findings suggest that serial dependence in dynamic tasks reflects not only prediction formation but the flexible (or rigid) deployment of those predictions in the face of changing feedback. Our results highlight a distinctive rigidity in prediction weighting, rather than a general perceptual bias, in individuals with elevated autistic traits, and reveal contrasting stabilization strategies in domain experts.