The relationship between fMRI adaptation and repetition priming of visually presented objects
Bibliographic record
Abstract
The neural correlates of fMRI adaptation and repetition priming provide useful insight as to how visual information is perceived and stored in the brain. Yet, although both phenomena are typically associated with reduced activation in visually responsive brain regions, it is presently unknown whether they rely on common or dissociable neural mechanisms. In an event-related fMRI experiment, we manipulated fMRI adaptation and repetition priming orthogonally. Subjects made comparative size judgments for pairs of visual stimuli that depicted either the same or different objects; some of the pairs presented during scanning had been shown previously and others were new. This design allowed us to examine whether object-selective regions in occipital and temporal cortex were sensitive to adaptation, priming, or both. Critically, it also allowed us to test whether any region showing sensitivity to both manipulations displayed interactive or additive effects. Only a partial overlap was found between areas that were sensitive to fMRI adaptation and those sensitive to repetition priming. Moreover, in most of the object-selective regions that showed both effects, the reduced activation associated with the two phenomena were additive rather than interactive. Together, these findings suggest that fMRI adaptation and repetition priming can be dissociated from one another in terms of their neural mechanisms.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".