Complex object representations in the medial temporal lobe: Feature conjunctions and view invariance
Bibliographic record
Abstract
The medial temporal lobe (MTL) is known to be vital for memory function. However, recent studies have shown that a specific set of brain structures within the MTL are also important for perception. For example, studies of amnesic patients with damage to MTL structures indicated that these patients performed poorly on perceptual tasks, specifically, when discriminating between items that shared overlapping features. It was suggested that one MTL structure in particular, the perirhinal cortex (PRC), should be considered part of the representational hierarchy in the ventral visual stream (VVS) and is responsible for representing the complex conjunction of features that comprise objects, perhaps at a view-invariant level. In this study we investigated how the different features comprising complex objects are represented throughout the VVS up to and including the MTL, and at what stage the representations become view-invariant. To address these questions, we used multi-voxel pattern analysis (MVPA) of fMRI data, a technique that has gained prominence for its ability to probe the underlying neural representations of visual information. Participants completed a one-back task involving novel objects that were comprised of either one (e.g., A, B, or C), two (e.g., AB, AC, BC), or three features (e.g., ABC) and were presented from one of two possible viewpoints. This allowed us to examine the degree to which neural representation of a pair of objects depended only on the sum of their parts (i.e., A+BC=AB+C), or whether the specific feature conjunctions within objects were encoded. A searchlight analysis using this method indicated that anterior regions of the VVS, including the PRC, coded the complex conjunctions of features comprising the objects, over and above the individual features themselves. Moreover, we found evidence to suggest that the conjunctive representations in PRC were view-invariant. Meeting abstract presented at VSS 2013
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".