Schema-based active inference supports rapid generalization of experience and frontal cortical coding of abstract structure

TL;DR

This paper introduces a new computational framework called schema-based hierarchical active inference (S-HAI) that models how schemas support rapid generalization and neural coding of abstract structures in spatial navigation tasks.

Contribution

The paper presents S-HAI, a novel hierarchical active inference model that integrates schemas with predictive processing, explaining neural and behavioral phenomena in spatial navigation and generalization.

Findings

S-HAI reproduces behavioral signatures of schema-based generalization.

The model captures neural codes observed in rodent medial prefrontal cortex.

S-HAI demonstrates how hierarchical predictive processing supports schema formation.

Abstract

Schemas -- abstract relational structures that capture the commonalities across experiences -- are thought to underlie humans' and animals' ability to rapidly generalize knowledge, rebind new experiences to existing structures, and flexibly adapt behavior across contexts. Despite their central role in cognition, the computational principles and neural mechanisms supporting schema formation and use remain elusive. Here, we introduce schema-based hierarchical active inference (S-HAI), a novel computational framework that combines predictive processing and active inference with schema-based mechanisms. In S-HAI, a higher-level generative model encodes abstract task structure, while a lower-level model encodes spatial navigation, with the two levels linked by a grounding likelihood that maps abstract goals to physical locations. Through a series of simulations, we show that S-HAI reproduces key behavioral signatures of rapid schema-based generalization in spatial navigation tasks, including the ability to flexibly remap abstract schemas onto novel contexts, resolve goal ambiguity, and balance reuse versus accommodation of novel mappings. Crucially, S-HAI also reproduces prominent neural codes reported in rodent medial prefrontal cortex during a schema-dependent navigation and decision task, including task-invariant goal-progress cells, goal-and-spatially conjunctive cells, and place-like codes at the lower level. Taken together, these results provide a mechanistic account of schema-based learning and inference that bridges behavior, neural data, and theory. More broadly, our findings suggest that schema formation and generalization may arise from predictive processing principles implemented hierarchically across cortical and hippocampal circuits, enabling the generalization of experience.