Higher order, multifeatural object encoding by the oculomotor system
Bibliographic record
Abstract
Previous behavioral and physiological research has demonstrated that as the behavioral relevance of potential saccade goals increases, they elicit more competition during target selection processing as evidenced by increased saccade curvature and neural activity. However, these effects have only been demonstrated for lower order feature singletons, and it remains unclear whether more complicated featural differences between higher order objects also elicit vector modulation. Therefore, we measured human saccades curvature elicited by distractors bilaterally flanking a target during a visual search saccade task and systematically varied subsets of features shared between the two distractors and the target, referred to as objective similarity (OS). Our results demonstrate that saccades deviated away from the distractor highest in OS to the target and that there was a linear relationship between the magnitude of saccade deviation and the number of feature differences between the most similar distractor and the target. Furthermore, an analysis of curvature over the time course of the saccade demonstrated that curvature only occurred in the first 20-30 ms of the movement. Given the multifeatural complexity of the novel stimuli, these results suggest that saccadic target selection processing involves dynamically reweighting vector representations for movement planning to several possible targets based on their behavioral relevance. NEW & NOTEWORTHY We demonstrate that small featural differences between unfamiliar, higher order object representations modulate vector weights during saccadic target selection processing. Such effects have previously only been demonstrated for familiar, simple feature singletons (e.g., color) in which features characterize entire objects. The complexity and novelty of our stimuli suggest that the oculomotor system dynamically receives visual/cognitive information processed in the higher order representational networks of the cortical visual processing hierarchy and integrates this information for saccadic movement planning.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".