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Dynamic Reversal of IT-PFC Information Flow Orchestrates Visual Categorization Under Perceptual Uncertainty

Abouhadi, Z.; Karimi-Rouzbahani, H.

2026-06-10 neuroscience
10.64898/2025.12.17.695044 bioRxiv
Show abstract

Categorization relies on a dynamic interplay between sensory representation and cognitive control, yet the classical view posits a fixed, feed-forward information flow from the inferotemporal (IT) cortex to the prefrontal cortex (PFC). Whether this hierarchical directionality adapts to cognitive context, such as perceptual certainty, remains a fundamental question in systems neuroscience. We investigated this by recording intracranial neural activity in monkeys performing a delayed match-to-category task. We developed a novel connectivity framework, Model-Based Representational Connectivity Analysis (RCA), to simultaneously track the content, timing, and directionality of information flow between IT and PFC while controlling for common task-general representations. Our results revealed that while both areas rapidly encoded task-relevant information, the directionality of information flow was highly modulated by stimulus certainty. For high-certainty stimuli (far from the category boundary), we observed the classical feed-forward flow from IT to PFC. However, for low-certainty stimuli (near the category boundary), this hierarchy dynamically reversed, with a dominant, early feedback flow from PFC to IT preceding the feed-forward sweep. This feedback signal carried content-specific information related to the ambiguous stimuli, suggesting a top-down mechanism recruited to refine sensory representations. These findings challenge fixed-hierarchy models of visual processing, providing mechanistic evidence that the brain dynamically reconfigures the interactions between sensory and executive areas as a function of perceptual difficulty. We propose that the PFC initiates a top-down biasing signal to the IT cortex when sensory evidence is ambiguous, serving as an adaptive, context-driven control mechanism.

Published in Progress in Neurobiology (predicted rank #19) · training set

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