Evaluating the Effect of Semantic Congruency and Valence on Multisensory Integration
Bibliographic record
Abstract
ABSTRACT Previous studies demonstrate that semantics, the higher level meaning of multi-modal stimuli, can impact multisensory integration. Valence, an affective response to images, has not yet been tested in non-priming response time (RT) or temporal order judgement (TOJ) tasks. This study aims to investigate both semantic congruency and valence of non-speech audiovisual stimuli on multisensory integration via RT and TOJ tasks (assessing processing speed (RT), point of subjective simultaneity (PSS), and time-window when multisensory stimuli are likely to be perceived as simultaneous (Temporal Binding Window; TBW)). Forty participants (mean age: 26.25; females=17) were recruited from Prolific Academic resulting in 37 complete datasets. Both congruence and valence have a significant main effect on RT (congruent and high valence decrease RT) as well as an interaction effect (congruent/high valence condition being significantly faster than all others). For TOJ, images high in valence require visual stimuli to be presented significantly earlier than auditory stimuli in order for the audio and visual stimuli to be perceived as simultaneous. Further, a significant interaction effect of congruence and valence on the PSS revealed that the congruent/high valence condition was significantly earlier than all other conditions. A subsequent analysis shows there is a positive correlation between the TBW width ( b -values) and RT (as the TBW widens, the RT increases) for the categories that differed most from 0 in their PSS (Congruent/High and Incongruent/Low). This study provides new evidence that supports previous research on semantic congruency and presents a novel incorporation of valence into behavioural responses.
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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 | 0.007 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".