The role of perceptual load and sensory degradation on cross-modal selective attention
Bibliographic record
Abstract
To understand our sensory environment, our perceptual system must employ selective attention; the ability to attend to target information while ignoring distracting information. In the uni–modal domain the main determinant of selective attention success is capacity limitation, where only when processing capacity is taxed by the target (high load; HL) is distraction eliminated (perceptual load theory; PLT). Conversely, data limits while also increasing task demands, do not benefit selective attention as these limits are often driven by sensory degradation (SD) such that placing additional resources towards the target is not beneficial. Investigations of PLT to the cross–modal domain have produced mixed results, and no study has yet directly contrasted the impact of capacity and data limits in the cross–modal domain. The present dissertation focused on examining the impact of Perceptual Load (PL) and SD on cross–modal selective attention, in addition to examining how these factors would interact with the attended modality and individual differences (ID) in attentional control. Experiment 1 used a go–no–go manipulation of PL to show that distractor effects were not reduced at HL compared to low load (LL) condition and instead displayed trends for increased distraction under HL regardless of the attended modality. Experiment 2 used the addition of noise to create SD, and found that distractor processing increased under SD, again regardless of the attended modality. Experiment 1 and 2 used a uni–modal measure of attentional control, and overall both studies did not find a consistent pattern of correlation with cross–modal selective attention, suggesting important differences between the two. Experiment 3 used a single manipulation to create HL and SD conditions in a single experiment, and also found that both HL and SD showed trends of increased distraction relative to LL conditions. Overall the current dissertation suggests that capacity limitations arise at the modality level, and so do not impact cross–modal selective attention. As such, the findings of the current dissertation suggest there is no difference between capacity and data limited conditions in the cross–modal domain. Results are interpreted within a cross–modal selective attention framework.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".