Psychometric Assessment of the Temporal Bisection Task with Discrete and Continuous Response Formats
Bibliographic record
Abstract
Abstract The temporal bisection task has long been used to study time perception as well as measure individual differences in time perception ability. The task involves training participants on short and long reference durations before presenting intermediate durations and asking participants to classify them as ‘short’ or ‘long’. However, there is little information about how well the bisection task measures individual differences in timing ability. To bridge this gap, we assessed the psychometric properties of measures obtained from a classic temporal bisection task: Weber ratio and percent correct. Because measures with binary responses tend to require many trials to reach adequate reliability, we also assessed the psychometric properties of a modified bisection task which used a continuous response format. In this task, participants represented intermediate durations on a visual analogue scale. Estimation error was used as the outcome measure. Participants ( n = 46) completed the classic and modified bisection tasks twice across two sessions approximately one week apart. The modified bisection task had excellent internal consistency and test–retest reliability, while the classic task had fair to good internal consistency and good test–retest reliability. Overall, estimation error had the highest reliability, followed by percent correct, and then Weber ratio. In terms of validity, there was excellent convergent validity between the classic and modified bisection tasks. As an exploratory analysis, we assessed how the number of trials affected the reliability of each outcome measure across the two tasks. Based on this, we make recommendations on how to optimize reliability for both tasks in future research.
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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 | 0.010 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".