Distributed category‐specific recognition‐memory signals in human perirhinal cortex
Bibliographic record
Abstract
Evidence from a large body of research suggests that perirhinal cortex (PrC), which interfaces the medial temporal lobe with the ventral visual pathway for object identification, plays a critical role in item-based recognition memory. The precise manner in which PrC codes for the prior occurrence of objects, however, remains poorly understood. In the present functional magnetic resonance imaging (fMRI) study, we used multivoxel pattern analyses to examine whether the prior occurrence of faces is coded by distributed patterns of PrC activity that consist of voxels with decreases as well as increases in signal. We also investigated whether pertinent voxels are preferentially tuned to the specific object category to which judged stimuli belong. We found that, when no a priori constraints were imposed on the direction of signal change, activity patterns that allowed for successful classification of recognition-memory decisions included some voxels with decreases and others with increases in signal in association with perceived prior occurrence. Moreover, successful classification was obtained in the absence of a mean difference in activity across the set of voxels in these patterns. Critically, we observed a positive relationship between classifier accuracy and behavioral performance across participants. Additional analyses revealed that voxels carrying diagnostic information for classification of memory decisions showed category specificity in their tuning for faces when probed with an independent functional localizer in a nonmnemonic task context. These voxels were spatially distributed in PrC, and extended beyond the contiguous voxel clusters previously described as the anterior temporal face patch. Our findings provide support for proposals, recently raised in the neurophysiological literature, that the prior occurrence of objects is coded by distributed PrC representations. They also suggest that the stimulus category to which an item belongs shapes the organization of these distributed representations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".