Changes in Fixation Strategy May account for a portion of Perceptual Learning observed in visual tasks
Bibliographic record
Abstract
Perceptual learning in visual discrimination can be observed by monitoring an increase in an observer's ability to perform a certain task with practice. Perceptual learning has been previously linked to several different mechanisms that can account for the increase in an observer's ability: learning to perform the task it self (Anderson, Psychological Review, 94, 192, 1987), learning an optimal response strategy/adjusting criteria (Doane, Alderton, Sohn & Pellegrino, Journal of Experimental Psychology, 22, 1218, 1996), as well as changes in how the physical stimuli are perceived and processed by the observer (Gibson, 1969; Goldstone, Annual Review of Psychology, 49, 585, 1998). Observers can learn to visually fixate on areas of an image/stimuli that provide information necessary to complete their task, while avoiding areas that are not informative. This form of perceptual learning suggests a learned change in the observer's visual fixation strategy, an area of perceptual learning that has not been studied with visual hyperacuity paradigms. During training for visual hyperacuity discriminations based on small differences in the spatial frequency or orientation of suprathreshold sinusoidal gratings, observers had their eye fixations recorded. Results showed a change in fixation strategy for all observers as their experience increased and the difficulty of the discriminations increased. Observers varied in their fixation changes, as well as their final fixation points. There was a negative correlation between fixation variance and number of trials completed, but this value did not reach significance for most observers. These results suggest that observers modify their fixation strategy over time to optimize their performance on the discrimination task. This is somewhat contradicted by the observation that incorrect responses belong to the same distribution of eye fixations as correct responses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".