Asymmetrical monitoring of subjective asynchronies: a metacognitive generalized STEARC effect
Bibliographic record
Abstract
Previous studies have demonstrated that human participants can keep track of the magnitude and direction of their trial-to-trial errors in temporal, spatial, and numerical estimates, collectively referred to as "metric error monitoring." These studies investigated metric error monitoring in an explicit timing/counting context. However, many of our judgments may also depend on temporal mismatches between stimuli where the temporal information is not processed explicitly, which eventually brings about the simultaneity perception. We investigated whether participants can monitor errors in their simultaneity perception. We tested participants in temporal orer judgment (TOJ) task, where they judged which of the two consecutive stimuli (one on each side of the screen) appeared first and reported their confidence rating for each TOJ. The results of all four experiments showed that the confidence judgements for correct judgments increased and for incorrect judgments decreased with longer absolute SOA. A more granular analysis showed that participants could only monitor their errors for left-first and bottom-first judgments, which suggests a metacognitive spatial-temporal association of response codes (STEARC) effect.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".