Evaluating the Effect of Semantic Congruency and Valence on Multisensory Integration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it