Predictive spatial cues reduce competition between items in crowded visual displays: Evidence from ERPs
Bibliographic record
Abstract
Findings from a recent EEG study by Anderson et al. (2014) support the idea that crowded visual displays prevent individuation of a target and results in the substitution of features from the distractors during target processing. An alternative explanation for this phenomenon, also known as binding errors, suggests that this misattribution may be the result of active competition between the items during encoding. In order to test this possibility, the introduction of a spatial cue may alleviate the active competition present between target and nearby distractors. In the present study, we replicated part of Anderson et al. (2014), but introduced a predictive spatial cue. Participants were required to report the orientation of a radial target among diametrical distractors presented in the periphery. Performance was compared between trials with far or near flankers as well as with the presence of either a single predictive cue or three non-predictive spatial cues. Preliminary behavioural results show an overall decrease in target accuracy in the trials during which the single spatial cue is presented, supporting the idea that competition can be reduced by a predictive attentional cue. ERP results revealed two effects: First, the N2pc was evident only in the near flanker condition when the cue was absent, suggesting that spatial cues alleviated the competition between targets and flankers. Second, the uncued conditions produced a greater amplitude in the lateralized P1 component relative to the cued conditions, possibly suggesting that this competition may be resolved in early sensory processing. The results suggest a potential mechanism by which attentional biases can reduce competition between targets and distractors during encoding. Meeting abstract presented at VSS 2015
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".