Bistability and hysteresis in the proximity-based grouping of dot lattices
Bibliographic record
Abstract
Grouping by proximity is a fundamental principle of human vision where elements close to each other are perceived as a group. This study examined the hysteresis effect in proximity-based grouping of dots in rectangular lattices in the presence of orientation biases. To induce perceptual switching, interdot distances in a specific direction were gradually increased or decreased on a trial-by-trial basis during ascending and descending sequences. To address orientation biases, stimuli were presented along two axes, each with two orientations. This design allowed investigation of perceptual switching between vertical and horizontal grouping in the cardinal axis and between 45° and 135° grouping in the oblique axis. Data from 34 participants were analyzed by fitting psychometric functions and the Lotka-Volterra-Haken (LVH) model-a dynamical model of neural population competition-with statistical analysis using linear mixed-effects models. The results revealed a strong preference for the vertical orientation in the cardinal axis and a slight bias toward 135° in the oblique axis. A significant hysteresis effect was found in both axes, with ascending transition points consistently exceeded descending points. Interestingly, this effect remained unaffected by stimulus axis or orientation, indicating robustness against orientation biases. A significant positive correlation in hysteresis across axes suggests it may represent a personal perceptual characteristic. The LVH model's numerical simulation effectively captured the dynamic behavior of competing responses and their bifurcation during perceptual switching. The model demonstrated acceptable accuracy in estimating transition points and simulating individual responses. Moreover, its parameters reflected the observed data patterns and provided a mechanistic account of perceptual switching in proximity-based grouping.
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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 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".