Transsacadic Integration of Multiple Objects and The Influence of Stable Allocentric Cue
Bibliographic record
Abstract
Transsaccadic integration is the ability to retain and synthesize visual information between different stable fixations. In order to do this, the brain must be able to retain and update object locations and features despite relative changes in retinal location produced by saccades several times per second. It is known that humans are able to retain several objects across saccades based on egocentric mechanisms, but it is not known what role allocentric landmarks play in this process. In order to test this, we compared performance in a transsaccadic integration task (e.g., Prime et al. Exp. Brain Res. 2007) with or without the presence of an allocentric landmark. 1-7 Gabor patches with pseudorandom orientations were presented on a frontal screen while participants fixated a randomized fixation cross. Following a visual mask, participants were required to saccade to a new location and were asked to identify whether a new Gabor patch presented at one of the same locations was rotated clockwise or counterclockwise from the original orientation. In 50% of the trials, an allocentric landmark (a stable cross positioned pseudorandomly within the stimulus array and extending across the screen) was presented throughout the trial. Using generalized linear mixed modeling (GLMM), results from 9 participants confirmed the expected result that in the absence of the landmark, performance decreased significantly (from a baseline of ~90% correct) as the set size increased (p < .001). More importantly, the presence of the landmark ameliorated this decrease in performance, providing significantly better performance for set sizes of 3 and higher (p = .029). These preliminary results suggest that egocentric and allocentric mechanisms may combine to provide optimal performance in transsaccadic integration of multiple objects. 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 | 0.002 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".