Evaluating the Effect of Semantic Congruency and Valence on Multisensory Integration
Bibliographic record
Abstract
Previous studies have found that semantics, the higher-level meaning of stimuli, can impact multisensory integration; however, less is known about the effect of valence, an affective response to stimuli. This study investigated the effects of both semantic congruency and valence of non-speech audiovisual stimuli on multisensory integration via response time (RT) and temporal-order judgement (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)]. Through an online study with 40 participants (mean age: 26.25 years; females = 17), we found that both congruence and valence had a significant main effect on RT (congruency and positive valence decrease RT) and an interaction effect (congruent/positive valence condition being significantly faster than all others). For TOJ, there was a significant main effect of valence and a significant interaction effect where positive valence (compared to negative valence) and the congruent/positive condition (compared to all other conditions) required visual stimuli to be presented significantly earlier than auditory stimuli to be perceived as simultaneous. A subsequent analysis showed a positive correlation between TBW width and RT (as TBW widens, RT increases) for the categories that were furthest from true simultaneity in their PSS (Congruent/Positive and Incongruent/Negative). 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 | 0.009 |
| 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) | 0.002 | 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".