Bringing the real world into the fMRI scanner: Robust release from adaptation for 2D pictures but not actual 3D objects
Bibliographic record
Abstract
Our understanding of the neural underpinnings of perception is largely built upon studies that have employed 2-dimensional (2D) planar images. When viewing a sequence of two 2D pictures of objects, a change in objects produces a characteristic release from adaptation within ventral visual object-selective areas. Here we use functional brain imaging in humans to examine whether neural populations show a similar effect for real-world 3-dimensional (3D) objects. We found robust release from adaptation for 2D images of objects within classic object-selective cortical regions along the ventral and dorsal visual processing streams. Surprisingly however, BOLD responses remained in the adapted state on trials involving different 3D stimuli suggesting broader neural tuning for real-world objects. Our findings indicate that the neural mechanisms involved in processing real-world 3D objects are distinct from those that arise when we encounter a 2D representation of the same items. Incorporating real-world stimuli into fMRI designs may provide a more thorough understanding of the neural mechanisms of human vision.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".