Processing Time of Contour Integration: The Role of Colour, Contrast, and Curvature
Bibliographic record
Abstract
We investigated the temporal properties of the red - green, blue-yellow, and luminance mechanisms in a contour-integration task which required the linking of orientation across space to detect a 'path'. Reaction times were obtained for simple detection of the stimulus regardless of the presence of a path, and for path detection measured by a yes/no procedure with path and no-path stimuli randomly presented. Additional processing times for contour integration were calculated as the difference between reaction times for simple stimulus detection and path detection, and were measured as a function of stimulus contrast for straight and curved paths. We found that processing time shows effects not apparent in choice reaction-time measurements. (i) Processing time for curved paths is longer than for straight paths. (ii) For straight paths, the achromatic mechanism is faster than the two chromatic ones, with no difference between the red-green and blue-yellow mechanisms. For curved paths there is no difference in processing time between mechanisms. (iii) The extra processing time required to detect curved compared to straight paths is longest for the achromatic mechanism, and similar for the red - green and blue-yellow mechanisms. (iv) Detection of the absence of a path requires at least 50 ms of additional time independently of chromaticity, contrast, and path curvature. The significance of these differences and similarities between postreceptoral mechanisms is discussed.
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 | 0.001 | 0.007 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".