Balancing Prior Knowledge and Sensory Data in a Predictive Coding Model: Insights into Coherent Motion Detection in Schizophrenia

This study introduces a biologically plausible computational model based on the predictive coding algorithm, providing insights into motion detection processes and potential deficiencies in schizophrenia. The model decomposes motion structures into individual and shared sources, highlighting a critical role of surround suppression in detecting global motion. This biologically plausible model sheds light on how the brain extracts the structure of motion and comprehends shared or coherent motion within the visual field. The results obtained from random dot stimuli underscore the delicate balance between sensory data and prior knowledge in coherent motion detection. Model testing across varying noise levels reveals longer convergence times with higher noise, consistent with psychophysical experiments showing that response duration (e.g., reaction time or decision-making time) also increases with noise levels. The model suggests that an excessive emphasis on prior knowledge extends the convergence time in motion detection. Conversely, for faster convergence, the model requires a certain level of prior knowledge to prevent excessive disturbance due to noise. These findings contribute to potential explanations for motion detection deficiencies observed in schizophrenia.