Motion Integration for Ocular Pursuit Does Not Hinder Perceptual Segregation of Moving Objects
Bibliographic record
Abstract
When confronted with a complex moving stimulus, the brain can integrate local element velocities to obtain a single motion signal, or segregate the elements to maintain awareness of their identities. The integrated motion signal can drive smooth-pursuit eye movements (Heinen and Watamaniuk, 1998), whereas the segregated signal guides attentive tracking of individual elements in multiple-object tracking tasks (MOT; Pylyshyn and Storm, 1988). It is evident that these processes can occur simultaneously, because we can effortlessly pursue ambulating creatures while inspecting disjoint moving features, such as arms and legs, but the underlying mechanism is unknown. Here, we provide evidence that separate neural circuits perform the mathematically opposed operations of integration and segregation, by demonstrating with a dual-task paradigm that the two processes do not share attentional resources. Human observers attentively tracked a subset of target elements composing a small MOT stimulus, while pursuing it ocularly as it translated across a computer display. Integration of the multidot stimulus yielded optimal pursuit. Importantly, performing MOT while pursuing the stimulus did not degrade performance on either task compared with when each was performed alone, indicating that they did not share attention. A control experiment showed that pursuit was not driven by integration of only the nontargets, leaving the MOT targets free for segregation. Nor was a predictive strategy used to pursue the stimulus, because sudden changes in its global velocity were accurately followed. The results suggest that separate neural mechanisms can simultaneously segregate and integrate the same motion signals.
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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 | 0.001 |
| 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".