Spatial Effects on Temporal Categorisation
Bibliographic record
Abstract
We examined the influence of spatial factors in temporal processing. Participants categorised as short or long empty intervals marked by two brief flashes delivered from locations differing in height and depth (experiment 1), or from two of three locations on a vertical plane (experiment 2). The perceived duration of intervals, as determined by the point of subjective equality, was affected by the height and depth of the signals (experiment 1). Experiment 2 showed that the point of fixation plays a critical role in perceived duration. The duration of an interval located in the upper visual field is perceived as longer when participants fixate the higher visual source and shorter when the fixation point is set in the middle; this latter result also generally applies when the fixation point is in the lower source. Finally, for the sensitivity level, there was a significant segment (upper versus lower) x direction (descending versus ascending) interaction in experiment 1; a similar interaction effect varied according to the fixation point in experiment 2. In experiment 2, the Weber fractions were around 0.22. Most results can be explained in terms of the need to shift attention from one visual source--for marking time intervals--to another.
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 | 0.019 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".