Does Crossmodal Attentional Blink Depend on Spatial Congruency?
Bibliographic record
Abstract
Although the majority of research on attentional blink (AB), an impairment in detecting the second of two sequentially presented target stimuli, has been well-established using visual targets, little research has been done on auditory AB and crossmodal AB with visual and auditory targets (Arnell and Jolicœur 1999). Similarly, AB effects have been demonstrated with spatially incongruent visual targets (Jefferies and Di Lollo 2009), but it remains unknown if AB effects will be observed when unimodal auditory targets or auditory–visual targets are spatially incongruent. The present study extends previous literature with a systematic examination of AB effects under varying unimodal and crossmodal conditions (Experiment 1) and unimodal and crossmodal AB effects when manipulating spatial congruency of targets (Experiment 2). Our main results show AB effects across all unimodal and crossmodal conditions in both experiments. AB magnitude was the strongest in congruent unimodal visual conditions and the weakest in the crossmodal condition with visual as Target 1 (T1) and auditory as Target 2 (T2). In Experiment 2, we found AB effects occur regardless of target spatial congruency. Only the unimodal visual condition showed a larger AB effect for spatially incongruent visual targets. These findings provide new insight into attentional interference across space and sensory domains.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".