Expert Attention: Attentional allocation depends on the differential development of multisensory number representations
Bibliographic record
Abstract
Traditional models developed within cognitive psychology suggest that attention is deployed flexibly and irrespective of differences in expertise with to-be-attended stimuli. However, everyday environments are inherently multisensory and observers differ in familiarity with particular unisensory representations (e.g., number words, in contrast with digits). To test whether the predictions of the traditional models extend to such naturalistic settings, six-year-olds, 11-year-olds and young adults (N=83) searched for predefined numerals amongst a small or large number of distractor digits, while distractor number words, digits or their combination were presented peripherally. Concurrently presented number words and audiovisual stimuli that were compatible with the target digit facilitated young children’s selective attention. In contrast, for older children and young adults number words and audiovisual stimuli that were incompatible with their visual targets resulted in a cost on reaction time. These findings suggest that multisensory and familiarity-based influences interact dynamically as they shape selective attention. Therefore, models of selective attention should include multisensory and familiarity-dependent constraints.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".