Active manipulation promotes predictive gaze strategies during virtual object exploration
Bibliographic record
Abstract
Actively manipulating novel objects rather than passively observing them can facilitate object recognition, but the mechanisms behind this effect still remain largely unknown. One potential explanation is that active learning facilitates an iterative process of generating and testing hypotheses about the effects of actions on the objects. We investigated how the gaze strategies utilized during active learning differ from those utilized during passive learning. Of particular interest was the question of whether participants would display any preference for the “leading edge” (the edge of a rotating object where previously unseen features are becoming visible) or the “trailing edge” (the edge of a rotating object where previously visible features are disappearing from view) of an object and whether such a preference would differ between learning conditions. Participants (n = 26) learned novel virtual objects either actively or passively while eye tracking data was collected, then completed an old/new discrimination task. Results indicated that during active learning, participants spent significantly more time looking at the leading than trailing edge (53%:47%). In contrast, participants spent equal amounts of time looking at the leading and trailing edges during passive learning (50%:50%). These results suggest that people may indeed be more likely to generate and test hypotheses about objects that are being learned actively rather than passively. However, no effect of active versus passive learning on recognition accuracy or speed was observed, likely because the objects we used had less distinctive parts than those used in prior studies did (Harman et al., 1999; Curr Biol). Thus, further investigation is needed to determine what role such predictive behavior plays in the “active learning effect” observed in past studies.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".