Prediction Gone Awry: A Computational Framework for Emotion Dysregulation Across Autism and Attention-Deficit/Hyperactivity Disorder

Emotion dysregulation is a common issue across childhood and adolescence, including in autism and attention-deficit/hyperactivity disorder (ADHD). Yet most accounts remain descriptive-focusing on irritability, rapid escalation, slow return to baseline-without specifying the computational processes that stabilize emotions in context. We propose a predictive-coding account in which emotion regulation depends on how the brain predicts bodily and environmental signals, detects mismatches, between expectation and sensation, and determines how much confidence to assign those mismatches. Within this framework, dysregulation can arise when prediction errors are assigned excessive precision (i.e., incoming signals are overweighted relative to prior expectations, making them feel "too loud"), amplifying emotional reactivity or when prior expectations update too slowly (feelings get "stuck"), prolonging emotional states beyond contextual demands. We hypothesize that autism may involve heightened confidence assigned to surprising inputs together with sluggish updating of expectations, a combination that can produce both reactivity and emotional inertia. ADHD, by contrast, may be characterized by weaker expectations and unstable gain control, meaning the system's amplification of incoming signals fluctuates from moment to moment, contributing to rapid, reactive swings in affect. We outline candidate physiological correlates-including heart-rate variability dynamics, pupillary variability, mismatch negativity, and neural indices of interoceptive precision-that could evaluate these hypotheses. Framing intervention around restoring flexible inference rather than suppressing emotion offers a mechanistic direction for developmental research. These predictions await empirical validation in developmental samples.