Perceptual learning of motion directions transfers to smooth pursuit eye movements
Bibliographic record
Abstract
GOAL Perceptual learning (PL) studies find that practice improves motion direction discrimination. We previously showed that PL affects perceived motion direction (Szpiro-Grinberg, Spering, Carrasco; ECVP 2011). However, it is unknown whether this perceptual effect would transfer to motor actions. Using a direction estimation task, we examined PL and its effect on the direction of smooth pursuit eye movements. METHOD We presented a random-dot kinematogram (75% motion coherence, drifting at 10º/s) and after stimulus offset asked observers to estimate its motion direction by manually adjusting the angle of an arrow shown on the screen. All observers underwent a pre-test (day 1), training sessions (days 2-4) and a post-test (day5). During the testing sessions, we presented stimuli moving rightward or leftward along the horizontal axis or in a direction deviating ±3º from horizontal. During the training sessions, we only presented stimuli moving to one side. We conducted two experiments that differed only in the procedure of the testing sessions: in Experiment 1, observers fixated throughout the stimulus presentation; in Experiment 2, observers tracked the stimulus motion direction with their eyes. Both groups performed the estimation task. In the training sessions, all observers fixated during the stimulus presentation and then performed the estimation task. Comparing these experiments enabled us to isolate whether potential effects of PL on direction estimation would also transfer to pursuit directions. RESULTS In both experiments and for all directions PL produced an overestimation of motion direction away from the perceived horizontal. This result reveals that PL shifts perceived directions even when viewed under different retinal stimulation (fixation/pursuit). Moreover, in Experiment 2, pursuit directions also showed overestimation, as if the eyes followed the perceived directions. The results show that training perception is sufficient to alter pursuit direction, thus indicating that PL can transfer to a smooth pursuit motor response. Meeting abstract presented at VSS 2012
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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 | 0.003 |
| 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 | 0.001 |
| 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".