3.3 Predictive Coding and Inside-Out Construction

Contemporary models of brain function, including predictive coding, provide additional support for the Monotropic Expansion framework. Predictive coding posits that the brain constantly generates models to anticipate incoming sensory input, updating predictions based on error signals. In autism, these error signals are often assigned unusually high precision—leading to a reduced reliance on prior expectations and an increased emphasis on bottom-up sensory input (Lawson et al., 2020).

This maps directly onto the model’s inside-out structure. In monotropic cognition, generalized categories are not assumed at the outset. Instead, relevance builds from specific, verifiable inputs, forming coherence through iterative integration. What may appear as detail fixation is better understood as an effort to reduce uncertainty through high-resolution internal modeling—favoring precision over assumption, depth over expedience.