The dynamics of audio-visual integration capacity as a function of environmental difficulty, stimulus factors, and experience
Bibliographic record
Abstract
The capacities of unimodal processes such as visual and auditory working memory, multiple object tracking, and attention have been heavily researched in the psychological science literature. In recent years there has been an increase in the amount of research into multimodal processes such as the integration of auditory and visual stimuli, but to my knowledge, there has only been a single published article to date investigating the capacity of audiovisual integration, which found that the capacity of audiovisual integration is limited to a single item. The purpose of this dissertation is to elucidate some of the factors that contribute to the capacity of audiovisual integration, and to illustrate that the interaction of these respective factors makes the capacity a fluid, dynamic property. Chapter 1 reviews the literature coming from multimodal integration research, as well as from unimodal topics that are pertinent to the factors that are being manipulated in the dissertation: namely, working memory, multiple object tracking, and attention. Chapter 2 considers the paradigmatic structure employed by the single study on audiovisual integration capacity and breaks down the component factors of proactive interference and temporal predictability, which contribute to the environmental complexity of the scenario, in the first illustration of the flexibility of capacity of audiovisual integration. Chapter 3 explores the effects of stimulus factors, considering the effects of crossmodal congruency and perceptual chunking on audiovisual integration capacity. Chapter 4 explores the variability of audiovisual integration capacity within an individual over time by means of a training study. Chapter 5 summarizes the findings of the research within, discusses some overarching themes with regard to audiovisual integration capacity including how information is processed through integration and how these findings could be applied to real-life scenarios, suggests some avenues for future research such as further manipulations of modality and SOA, and draws conclusions and answers to the research questions. This research extends what is known about audiovisual integration capacity, both in terms of its numerical value and the factors that play a role in its establishment. It also demonstrates that there is no overarching limitation on the capacity of audiovisual integration, as the initial paper on this topic suggests, but rather that it is a process subject to multiple factors, and can be changed depending on the situation in which integration is occurring.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".