Phase Integration Bias Predicts Performance in a Motion Binding Task
Bibliographic record
Abstract
Studies of motion binding often examine the integration of components such as orientation and speed. We examined motion binding using a task requiring only integration of relative phase (Cali et al., VSS, 2014). Observers discriminated clockwise and counter-clockwise motion in a stimulus comprising four sets of linearly arranged dots, two moving horizontally and two moving vertically along sinusoidal trajectories differing in phase. Across conditions, noise jitter could be added along the trajectory perpendicular to each dot’s motion. Interestingly, as originally showed by Lorenceau (Vision Res., 1996), noise improved discrimination accuracy, consistent with the notion that noise acts as a grouping cue encouraging perception of global motion. Furthermore, when noise was absent from the stimulus, accuracy was not at chance, but significantly below chance; observers consistently reported motion in the incorrect direction. Here we test the hypothesis that observers perceive reverse motion because their representation of the relative phase of the motion components is systematically biased. We asked observers to adjust the relative phase of motion components to produce the most compelling clockwise or counter-clockwise motion with stimuli that did or did not contain noise. We also measured discrimination accuracy for clockwise and counter-clockwise motion. We found that i) phase adjustment error was significantly greater with no noise; ii) discrimination accuracy was significantly below chance with no noise; and iii) the correlation between phase adjustment error and discrimination accuracy were significant in both noise conditions. Our results support the hypothesis that observers misperceive the direction of motion without noise because their representation of the relative phases of motion components is biased. This bias may occur because observers sample the motion components sequentially in the zero noise condition and simultaneously in the high noise condition. More generally, this result suggests the presence of an integration bias in other motion tasks. Meeting abstract presented at VSS 2015
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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 | 0.002 | 0.012 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".