Adaptation to radial frequency patterns in the lateral occipital cortex
Bibliographic record
Abstract
Background: We used functional magnetic resonance imaging adaptation (fMRIa) and radial frequency (RF) stimuli to investigate shape coding in the lateral occipital complex (LOC) an important cortical area that mediates integration of local contour information. An RF pattern consists of a closed contour whose radius sinusoidally deviates from circularity as determined by frequency (i.e., the periodicity of bumps around the circle) and amplitude (i.e., bump size). As a Fourier basis set, the family of RF patterns defines a large space of unique closed-contour shapes. Methods: BOLD responses in LOC were collected for 7 participants on a 3T GE scanner according to standard fMRI techniques. LOC was localized by contrasting BOLD responses to scrambled and non-scrambled objects. Results: BOLD responses were lowest for blocks of purely circular stimuli than for all other blocks consisting of single RF contours with fixed shape and amplitude. Blocks in which contours varied in amplitude and/or shape exhibited varying degrees of adaptation release with respect to fixed-shape-and-amplitude blocks. Conclusions: Results are consistent with a population code in which closed-contour shapes are represented as deviations from a circularity prototype. In particular, we interpret weaker BOLD responses to pure circles as an instance of efficient predictive coding that reduces redundancy between neural outputs. We are further characterizing the topology of BOLD response in RF space and the corresponding shape selectivity of population subunits in LOC.
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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".