On the consequences of perceptual organization via good continuation in depth
Bibliographic record
Abstract
We have previously reported that mid-level configural effects – specifically, perceived closure – play a significant role in processing the binocular disparity of line segments (Deas & Wilcox, 2014). Here we demonstrate that the Gestalt cue of good continuity has a stereoscopic counterpart (‘good continuation in depth’) that operates when the relative disparity of neighbouring features varies smoothly. This disparity-based grouping cue negatively influences perceived depth (Experiment 1) but in similar stimuli enhances detectability (Experiment 2). In our first study we assessed the effect of good stereoscopic continuation on perceived depth magnitude using a touch-sensitive sensor. First, the relative separation in depth between two isolated dots was compared to estimates made when intermediate elements were added to form a continuous disparity gradient. We found that the perceived separation in depth between end dots systematically declined as intervening dots were added. Importantly, veridical depth was restored when the disparity of the intermediate dots was jittered. In Experiment 2, the same dot configurations were used in a visual search paradigm to evaluate if disparity-based grouping has a positive impact on search time. Observers searched for a target, defined by good stereoscopic continuation, among distractors which contained depth jitter, or vice versa. By modulating the disparity profile of the target relative to the distractors, we found that detection was dramatically more efficient when the target path contained a continuous disparity gradient. Our results demonstrate the operation of a disparity-based grouping cue that corresponds to the Gestalt principle of good continuity in depth. We posit that this disparity-based grouping may be partly responsible for the well-documented underestimation of perceived slant. Meeting abstract presented at VSS 2015
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 | 0.000 | 0.003 |
| 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 | 0.001 |
| 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".