Individual differences in multiple object tracking, attentional cueing, and age account for variability in the capacity of audiovisual integration
Bibliographic record
Abstract
There has been a recent increase in individual differences research within the field of audio-visual perception (Spence & Squire, 2003), and furthering the understanding of audiovisual integration capacity with an individual differences approach is an important facet within this line of research. Across four experiments, participants were asked to complete an audiovisual integration capacity task (cf. Van der Burg et al., 2013; Wilbiks & Dyson, 2016; 2018), along with differing combinations of additional perceptual tasks. Experiment 1 employed a multiple object tracking task and a visual working memory task. Experiment 2 compared performance on the capacity task with that of the attention network test. Experiment 3 examined participants’ focus in space through a Navon task and vigilance through time. Having completed this exploratory work, in Experiment 4 we collected data again from the tasks that were found to correlate significantly across the first three experiments and entered them into a regression model to predict capacity. The current research provides a preliminary explanation of the vast individual differences seen in audiovisual integration capacity in previous research, showing that by considering an individual’s multiple object tracking span, focus in space, and attentional factors, we can account for up to 34.3% of the observed variation in capacity. Future research should seek to examine higher-level differences between individuals that may contribute to audiovisual integration capacity, including neurodevelopmental and mental health differences.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| 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".