Implicit and Explicit Learning of a Covariation Across Visual Search Displays
Bibliographic record
Abstract
The goal of this study was to extend prior reports of implicit learning in visual search (e.g., Chun & Jiang, 1999) by employing eye movement monitoring and reaction time measures to contrast implicit and explicit learning.Towards this end, participants' eye movements were monitored as they performed a visual search task in the 'change blindness' flicker paradigm.In each trial, participants were asked to detect a letter that differed in shape or color across otherwise identical alternating letter arrays.In a subset of trials, for some participants the background luminance covaried with target color (Color rule condition) and for other participants letter thickness covaried with target shape (Shape rule condition).In addition, half of the participants were told of the existence of a covariation (Informed group) and the other half were not notified of this regularity and in a post-experimental interview reported no awareness of this covariation (Uninformed group).In both groups, reaction time data indicated that visual search was facilitated for trials that contained the covariation, and eye movement data showed that participants guided eye movements to potential targets based on the covariation information.Further, Informed participants in the Color rule condition were able to use covariation information to a greater extent than those in the Shape rule condition.In contrast, no differential sensitivity across rule conditions was found for Uninformed participants.Implications to the study of implicit learning in visual search are discussed.
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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 | 0.015 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".