Predictive cues narrow the window of spatial attention in crowded visual displays: Evidence from ERPs
Bibliographic record
Abstract
Our limited capacity for processing and filtering relevant information often results in binding errors when a target is presented in a crowded display. It was recently suggested that substitution of features between target and distractors might be the consequence of a failure to individuate the target during processing and that the N2pc event-related potential component is a reliable index of this phenomenon. Another theory, however, suggests that active competition between items during processing can account for these substitution errors. In this study, we introduced spatial cues to attempt to alleviate the active competition between items. Participants were presented with peripheral displays of far or near flankers that were either cued or uncued, and they were instructed to report the orientation of a radial line target among diametrical distractors. In the first experiment, the spatial cue was introduced before the visual display, while the second experiment presented a retro-cue. Behavioural results indicate that the guess rate is significantly reduced when targets are preceded by a predictive spatial cue compared to when retro-cues or neutral cues are present. Both experiments also produced an early positive contralateral component (P2pc) that was significantly reduced by the presence of pre-cues only. These results suggest that predictive spatial pre-cues may allow for a downscaling of the window of attention, which is reflected by the early lateralized P2 component, and facilitates the processing of information within a more restricted area. Modulating this window of attention helps resolve competition/individuation, as reflected by the decreased guess rate. In sum, the P2pc effect appears to reflect the biasing of spatial attentional during encoding. Meeting abstract presented at VSS 2016
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".