Back

Multiple forms of sensory reinstatement in category-selective cortex

Prasad, D.; Steel, A.; Roberston, C. E.

2026-08-19 neuroscience
10.64898/2026.08.10.743957 bioRxiv
Show abstract

Visual recall is classically thought to depend on reinstatement: areas engaged when encoding a visual input are similarly reactivated when remembering it. Here we investigated if reinstatement might be differently implemented across the diverse category-selective systems of visual cortex. Using fMRI in 25 participants, we assessed possible reinstatement organizations across scene-, face-, and body-selective cortex. We asked whether memory reactivates the same category-selective areas engaged during perception, whether it engages same or distinct vertices, and whether perceptual-mnemonic distinctions were topographically organized. All regions were selectively engaged during both perception and memory, though memory activity was weaker overall. At the vertex-level, most regions--including body-selective LOS, ITG, MTG; face-selective FFA1, FFA2; and scene-selective PPA--showed classic reinstatement, with memory enriched in the most perceptually selective vertices. In contrast, OFA and OPA showed separable perception-and memory-biased vertices. Critically, only scene-selective areas showed topographic distinction: in both PPA and OPA, mnemonic activity was located consistently anterior to perceptual activity, whereas no face-or body-selective areas showed such a distinction. Thus, while all category-selective areas are reactivated during memory, scene-selective cortex topographically separates memory from perception, suggesting different sensory reinstatement implementations across high-level visual cortex, possibly reflecting the distinct computational demands.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.