“Extended Descriptive Risk-Averse Bayesian Model” a More Comprehensive Approach in Simulating Complex Biological Motion Perception
Bibliographic record
Abstract
The ability to perceive biological motion is crucial for human survival, social interactions, and communication. Over the years, researchers have studied the mechanisms and neurobiological substrates that enable this ability. In a previous study, we proposed a descriptive Bayesian simulation model to represent the dorsal pathway of the visual system, which processes motion information. The model was inspired by recent studies that questioned the impact of dynamic form cues in biological motion perception and was trained to distinguish the direction of a soccer ball from a set of complex biological motion soccer-kick stimuli. However, the model was unable to simulate the reaction times of the athletes in a credible manner, and a few subjects could not be simulated. In this current work, we implemented a novel disremembering strategy to incorporate neural adaptation at the decision-making level, which improved the model's ability to simulate the athletes' reaction times. We also introduced receptive fields to detect rotational optic flow patterns not considered in the previous model to simulate a new subject and improve the correlation between the simulation and experimental data. The findings suggest that rotational optic flow plays a critical role in the decision-making process and sheds light on how different individuals perform at different levels. The correlation analysis of human versus simulation data shows a significant, almost perfect correlation between experimental and simulated angular thresholds and slopes, respectively. The analysis also reveals a strong relation between the average reaction times of the athletes and the simulations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".