Searching for a "super foot" with evolutionary-guided adaptive psychophysics
Bibliographic record
Abstract
The walking direction of a biological entity is conveyed by both global structure-from-motion information and local motion signals. Global and local cues also carry distinct inversion effects. In particular, the local motion-based inversion effect is carried by the feet of the walker. Here, we searched for a “super foot”, defined as the motion of a single dot that conveys maximal directional information and carries a large inversion effect, by using a psychophysical procedure driven by a multi-objective evolutionary algorithm (MOEA). We report on two rounds of searches involving the evolution of 25–27 generations each (1000 trials/generation) conducted via a web-based interface. The search involved an eight-dimensional space spanned by amplitudes and phases of a 2nd-order fourier representation of the dot's motion in the image plane. On each trial, observers were presented with multiple copies of a “foot” chosen from a population of feet stimuli for the current generation and were required to indicate whether the perceived stimulus was right- or left- facing. The stimuli were shown at upright and inverted orientations. Upon completion of a generation, each stimulus was evaluated for its “fitness” based upon its ability to convey direction and carry an inversion effect from observer accuracy rates. The fittest stimuli were then selected to form a subsequent generation for testing via methods of crossover and mutation. We show that the MOEA was effective at driving increases in accuracy rates for the upright stimuli and increases in the inversion effect, quantified as the difference between upright and inverted stimuli, across generations. We show further that the two rounds of searches, beginning at different points in space, converge towards the same region. We characterize the “super foot” in relation to current theories about the importance of gravity-constrained dynamics for biological motion perception.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".