The Effects of Different Aperture-Viewing Conditions on the Recognition of Novel Objects
Bibliographic record
Abstract
The process of learning the structure of novel objects involves the selective use of information available in the distal stimulus. By allowing participants to explore the object within a limited field of view, we were able to examine more rigorously what regions of the object are actually selected in the learning process. Participants explored objects either by moving a circular aperture over a stationary novel object (the aperture-movement condition), or by moving the object behind a stationary aperture (the object-movement condition). Given the differences in how the spatial layout of object parts is revealed in the two study conditions, we expected that exploration would be more systematic in the aperture-movement condition than it would be in the object-movement condition, and would lead to better object recognition. We show evidence that in the aperture-movement condition exploration patterns were more related to the structure of the object and, as a consequence, the aperture-movement condition resulted in more accurate recognition in a later old--new discrimination test.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".