The Influence of Salient Distractors over the Course of a Category Learning Task
Bibliographic record
Abstract
Understanding the relative contributions of goal-directed and stimulus-responsive attention is a critical problem in visual cognition. To pit the two processes against one another, we develop an experiment where both are elicited by the task structure and the stimulus set, respectively. Specifically, we aim uncover the relative influence of salient, distracting information while participants learn how to sort visual stimuli into four different categories using less salient, but informative features of the stimulus. In this regard, we can examine both learned goal-directed attention and reflexive, stimulus-responsive attention to salient distractors simultaneously, thereby examining their interactions over learning. The 3 informative features of the stimulus are learned through trial-and-error. The salience condition presents irrelevant features that act as salient distractors; and the irrelevant features in the baseline condition are all equally non-salient. In contrast with predictions from theories of salience in visual attention, we find that those in the salient condition display more efficient eye movements by minimizing fixations to unimportant features relative to the baseline condition as measured by an optimization score (Blair, Watson & Meier, 2009) that reflects the proportion of fixations allocated to informative versus irrelevant features. Findings suggest that theories predicting eye movements from bottom-up information do not wholly account for oculo-motor activity during category learning problems of this nature. However, salience must enact some influence on the programming of saccades, otherwise both conditions would exhibit a similar optimization score. Our interpretations of these data are: 1) in the short duration preceding a saccade, goal-directed and stimulus-responsive attention both act to influence the selected saccade target; and 2) hybrid theories of goal-directed/stimulus-responsive processes provide the most plausible account of attention as it is observed in eye movements during a learning task. Meeting abstract presented at VSS 2013
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".