The Influence of Aging on Audio-Visual Temporal Order Judgments
Bibliographic record
Abstract
Perception of naturalistic events often requires observers to integrate stimuli across visual, auditory, and tactile modalities. There is some evidence that the temporal resolution of cross-modal integration may be impaired in older subjects. For example, Poliakoff et al. (2006) reported that just noticeable differences (JNDs) in a visual-tactile temporal order judgment (TOJ) task were larger in older subjects than younger subjects. However, little is known about how aging affects the temporal precision of audio-visual integration, especially for non-speech stimuli. We therefore measured the effects of aging on the precision of temporal order judgments in 11 younger (19–26) and 8 older (70+) subjects. On each trial, a subject was presented with a single visual stimulus (i.e., Gabor pattern) and a single auditory stimulus (i.e., Gaussian-damped tone), and the task was to determine which stimulus was presented first. The stimuli were brief (<10 ms) and clearly super-threshold. The method of constant stimuli was used to vary the temporal offset between stimuli, and the resulting psychometric function for each subject was used to estimate the point of subjective simultaneity (PSS) and JND (i.e., the standard deviation of the best-fitting cumulative normal). One younger subject who had a JND that was more than 5 standard deviations above the mean was declared an outlier and not included in further analyses. For the remaining subjects, the mean PSS (t(16) = 0.14, p = 0.88) and JND (t(16) = 0.08, p = 0.93) did not differ between age groups. Hence, we found no evidence that aging affects the temporal precision of auditory-visual temporal judgments, at least in this simple task. Currently we are investigating the extent to which this result generalizes to more complex audiovisual events.
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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 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".