Associative learning accelerates the temporal dynamics of covert exogenous spatial attention
Bibliographic record
Abstract
Introduction: Abrupt onsets trigger the reflexive allocation of covert attention, speeding visual information processing and improving discriminability at exogenously attended locations. In the lab, neutral stimuli (e.g., small circles) are typically used to study exogenous attention. Abrupt onsets encountered in daily life, however, often carry meaning (e.g., in Gmail, onsets in the lower right visual field not only capture attention, but also signal incoming Instant Messages, thanks to a learned association). Does the behavioral signature of covert exogenous spatial attention change when elicited by meaning-imbued onsets? Methods: On each trial, covert attention was manipulated with a peripheral onset, and two Gabor patches were briefly presented at 8° eccentricity (left/right of fixation). In line with a response cue, observers reported the target Gabor's orientation (clockwise/counterclockwise of vertical). Exogenous cues were valid (small circle presented near target location) or invalid (presented near distractor location); cue validity was 50%, cue-target SOA varied (33-133ms), and the cue was equally likely to be black or white in color. One color became meaning-imbued: following correct responses, observers were shown a randomly selected Emoji at fixation, but only when the peripheral onset had been rendered in the meaning-imbued color. Thus, observers learned to associate one type of onset (black or white peripheral circle, counterbalanced across observers) with the presentation of a novel, visually-pleasing stimulus; the other onset type provided a meaning-non-imbued baseline for each observer. A centrally-presented "X" followed all incorrect responses. Results: Both types of onsets modulated task performance (evidenced by increased accuracy and faster RTs for valid, relative to invalid, cues), but meaning-imbued onsets accelerated the timecourse: cueing effects were significantly larger at early SOAs for Emoji-predictive onsets, compared to non-predictive onsets. Conclusion: The temporal dynamics of covert exogenous spatial attention are accelerated when attentional allocation is triggered by a meaning-imbued onset. Meeting abstract presented at VSS 2018
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".