Multiple scales of organization for object selectivity in ventral visual cortex
Bibliographic record
Abstract
Object knowledge is hierarchical. For example, a Labrador belongs to the category of dogs, all dogs are mammals, and all mammals are animals. Several hypotheses have been proposed about how this hierarchical property of object representations might be reflected in the spatial organization of ventral visual cortex. For example, all exemplars of a basic-level category might activate the same feature columns or cortical patches (e.g., Tanaka, 2003, Cerebral Cortex), so that a differentiation between specific exemplars is only possible by comparing the responses of neurons within these columns or patches. According to this view, category selectivity would be organized at a larger spatial scale compared to exemplar selectivity. Little empirical evidence is available for such proposals from monkey studies, and no direct evidence from experiments with human subjects. Here we describe a new method in which we use fMRI data to infer differences between stimulus properties in the scale at which they are organized. The method is based on the reasoning that spatial smoothing of fMRI data will have a larger beneficial effect for a larger-scale functional organization. We applied this method to several datasets, including an experiment in which basic-level category selectivity (e.g., face versus building) was compared with subordinate-level selectivity (e.g., rural building versus skyscraper). The results reveal a significantly larger beneficial effect of smoothing for basic-level selectivity compared to subordinate-level selectivity. This is in line with the proposal that selectivity for stimulus properties that underlie finer distinctions between objects is organized at a finer scale than selectivity for stimulus properties that differentiate basic-level categories. This finding confirms the existence of multiple scales of organization in ventral visual cortex.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".