“Extended Descriptive Risk-Averse Bayesian Model” a More Comprehensive Approach in Simulating Complex Biological Motion Perception

• Published in: Biomimetics (Basel, Switzerland) Vol. 9; no. 1; p. 27
• Main Authors: Misaghian, Khashayar, Lugo, J. Eduardo, Faubert, Jocelyn
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.01.2024
• Subjects: Analysis; Bayesian; Bayesian analysis; biological motion; Correlation analysis; Decision making; dorsal pathway; hierarchical simulation model; Information processing; Methods; Motion detection; Motion detectors; Motion perception (Vision); Neurons; Neurophysiology; Optic flow; Perception; reaction time; Simulation methods; Social interactions; Visual system; Japan; reaction time; hierarchical simulation model; biological motion; Bayesian; dorsal pathway
• ISSN: 2313-7673; 2313-7673
• DOI: 10.3390/biomimetics9010027

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.