Temporal Accumulation and Decision Processes in the Duration Bisection Task Revealed by Contingent Negative Variation
Bibliographic record
Abstract
The duration bisection paradigm is a classic task used to examine how humans and other animals perceive time. Typically, participants first learn short and long anchor durations and are subsequently asked to classify probe durations as closer to the short or long anchor duration. However, the specific representations of time and the decision rules applied in this task remain the subject of debate. For example, researchers have questioned whether participants actually use representations of the short and long anchor durations in the decision process rather than merely a response threshold that is derived from those anchor durations. Electroencephalographic (EEG) measures, like the contingent negative variation (CNV), can provide information about the perceptual and cognitive processes that occur between the onset of the timing stimulus and the motor response. The CNV has been implicated as an electrophysiological marker of interval timing processes such as temporal accumulation, representation of the target duration, and the decision that the target duration has been attained. We used the CNV to investigate which durations are involved in the bisection categorization decision. The CNV increased in amplitude up to the value of the short anchor, remained at a constant level until about the geometric mean (GM) of the short and long anchors, and then began to resolve. These results suggest that the short anchor and the GM of the short and long anchors are critical target durations used in the bisection categorization decision process. In addition, larger mean N1P2 amplitude differences were associated with larger amplitude CNVs, which may reflect the participant's precision in initiating timing on each trial across a test session. Overall, the results demonstrate the value of using scalp-recorded EEG to address basic questions about interval timing.
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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 | 0.010 |
| 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 | 0.001 |
| 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".