Automatic prospective and retrospective activation of object representations during statistical learning
Bibliographic record
Abstract
Although the visual system detects statistical relationships between objects with remarkable efficiency, it is unclear how statistical learning occurs. Here we examine how object representations are activated during statistical learning. In the experiment, participants viewed a continuous sequence of objects at the center of the screen while performing a cover 1-back task during exposure. Unbeknownst to the participants, the sequence contained pairs of objects where one object always appeared before another (e.g., A always appeared before B). At the periphery of the screen, all unique objects in the sequence were presented in fixed locations at all times during exposure. This means that at any given trial, the object in the central sequence was also presented in the periphery, as well as its partner in the pair and all the other objects in other pairs. Participants' eye gaze was tracked throughout exposure. At test, participants chose pairs over foils as more familiar, indicating that they successfully learned the object pairs. Importantly during exposure, we found that when the first object in the pair was presented at the center of the screen, participants looked at its partner (the second object in the pair) in the periphery more than the other objects in other pairs. When the second object in the pair was presented at the center of the screen, participants looked at its partner (the first object in the pair) in the periphery more than the other objects in other pairs. This finding suggests that seeing the first member automatically activates the representation of the upcoming second member in a pair, and seeing the second member automatically activates the representation of the preceding first member. This study not only provides a novel paradigm to measure representation activation during statistical learning, but also elucidates the mechanism of how statistical learning occurs. Meeting abstract presented at VSS 2018
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".