Individual differences in the scope of spatial attention
Bibliographic record
Abstract
Studies of spatial attention typically average effects across participants without considering individual differences in attentional scope. Individual differences in how attention is deployed and distributed may be associated with differences in visual working memory (VWM): individuals with smaller VWM capacity may attend to smaller regions of space. It is also possible that personality traits could be associated with attentional mode – e.g., individuals high in openness may show a broader scope. We explored the relationship between the spatial distribution of attention, VWM capacity and personality through Inhibition of Return (IOR), a well-studied phenomenon characterized by a reaction time (RT) cost for targets at cued locations relative to targets at uncued locations. Important for the present study, IOR spreads beyond the cued location as the inhibition decreases as a function of distance from the cue (Bennett & Pratt, 2001). Participants also completed a measure of personality and VWM capacity, with the hypothesis that the spatial distribution of IOR would differ between individuals. On each trial of the IOR task, the cue was centred in one of four quadrants on the screen, which was followed by a target, appearing at varying distances from the cue (invisible grid composed of 11 x 11 spatial locations). The spatial distribution of IOR was determined for each individual by calculating the slope of the regression line between cue-target distance and RT (negative slope indicates faster RT with increase in distance). A steeper negative slope suggests efficient release from IOR and a more localized/focal IOR, whereas a shallow negative slope suggests more diffuse IOR. Our results show personality and VWM predict the slope/distribution of IOR, providing evidence of individual differences in the spatial distribution of IOR, which may result from differences in the allocation of attention to the cue. Meeting abstract presented at VSS 2013
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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 | 0.001 | 0.008 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".