Investigating the breadth and strength of perceptual control of Illusory Apparent Motion
Bibliographic record
Abstract
Recently, a stimulus called Illusory Apparent Motion (IAM) was discovered by Davidenko et al. (2017) wherein pixel textures randomly refreshing at a rate of 1.5 Hz generate the appearance of coherent apparent motion. IAM is a maximally ambiguous multistable stimulus that observers may perceive as moving coherently in a countless number of patterns (e.g., translation, shear, rotation, expansion-contraction). The current set of studies explores observers’ ability to perceptually control the appearance of IAM. The first two experiments used paradigms similar to those used with other multistable stimuli. Experiment 1 (n = 99) used a motion-priming persistence task, based on the methods of Davidenko et al. (2017), while experiment 2 (n = 76) used a dynamic report task with no priming, based on the methods of Kohler et al. (2008). In both experiments, participants successfully controlled translational motion by ‘changing’ or ‘holding’ their percepts, indicating that observers are capable of perceptually controlling IAM, similar to other multistable stimuli. Having established this, Experiment 3 (n = 43) explored the breadth of participants’ ability to perceive and control motion in IAM by testing them on 14 types of translational, shear, rotating, and expanding-contracting motion patterns. Participants were able to perceive a wide variety of motion patterns but were limited in the motion patterns they could control. Finally, Experiment 4 (n = 82) aimed to quantify the influence of perceptual control in biasing perceptions of IAM by presenting participants with a motion nulling signal (at above and below each participant’s perceptual threshold) while they attempted to control the motion. We were successful in quantifying the strength of perceptual control of IAM relative to low-level motion signals. Collectively, these studies provide evidence for the breadth and strength of observers’ ability to perceptually control IAM.
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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 | 0.009 |
| 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".