Position Specificity of Learning Using Complex Visual Stimuli
Bibliographic record
Abstract
Visual perceptual learning (PL) is characterized by long-lasting, stimulus-specific improvements in simple visual tasks. One explanation for stimulus specificity is that PL causes changes in early cortical areas; an alternative explanation is that PL is due to a higher-level process, reflecting what is learned rather than where it occurs in the visual pathway. One method to evaluate this hypothesis is to test if PL occurs in a task using complex stimuli encoded later in the visual pathway. We measured response accuracy with a 1-of-5 identification task using complex stimulus types (textures and faces) encoded by mechanisms in the inferotemporal cortex. In the training phase, participants saw one of the two stimulus types above or below a central fixation point. In the test phase, Group 1 identified the same stimuli in the same position; Group 2 identified the same stimuli in a new position; Group 3 identified new stimuli of the same type at the same position; and Group 4 identified new stimuli of the same type in a new position. For both stimulus types, we found evidence of both generalized and stimulus-specific learning: changing the stimuli and/or stimulus position significantly reduced accuracy but not to the level shown at the start of training. In addition, for textures (but not faces), accuracy was significantly lower in Group 4 than in Groups 2 and 3. Therefore, stimulus- and position-specific PL occurs in an identification task using complex stimuli.
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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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".