Closed-contour shapes encoded through deviations from circularity in lateral-occipital complex (LOC): An fMRI study
Bibliographic record
Abstract
PURPOSE: Exceptional human sensitivity to small deviations from circularity in closed contours has been well demonstrated psychophysically. Here we used fMRI to test whether circularity holds a special status in the neural coding of closed-contour shapes. METHODS: BOLD signals were recorded from 5 participants in 13 6-mm coronal slices with the most posterior slice anchored on the occipital pole. A region-of-interest analysis isolated the lateral-occipital complex (LOC) by contrasting BOLD signals from images of intact vs. scrambled tools. In key experiments, observers viewed closed contours that varied in basic shape (i.e. radial frequency) and deviation from circularity (i.e. radial amplitude). Experiments followed a block design where deviation from circularity was varied across blocks, and basic shape was either varied within block (multi-shape blocks) or held fixed (single-shape blocks). Observers performed size judgments to maintain attention. RESULTS: BOLD response in LOC for multi-shape blocks was lowest for pure circles and increased monotonically with deviation from circularity. Single-shape blocks showed similar results. Response in striate and extrastriate areas remained approximately constant across all conditions. CONCLUSIONS: Results are consistent with neural representations of closed-contour shapes that are centered on circular prototypes, and data suggest that prototype deviations constitute the basis of increased neural activity. Lack of circle-selective responses in striate and extrastriate areas suggests that LOC activity reflects active shape integration rather than passive inheritance from lower-level areas. Encoding prototype deviations is an efficient strategy and is likely a recurring theme throughout the visual hierarchy.
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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 | 0.001 |
| 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) | 0.001 | 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".