Abstract
Predictive coding is an emerging theoretical framework for explaining human perception and behavior. The proposed underlying mechanism is that signals encoding sensory information are integrated with signals representing the brain's prior prediction. Imbalance or aberrant precision of the two signals has been suggested as a potential cause for developmental disorders. Computational models may help to understand how such aberrant tendencies in prediction affect development and behavior. In this study, we used a computational approach to test the hypothesis that parametric modifications of prediction ability generate a spectrum of network representations that might reflect the spectrum from typical development to potential disorders. Specifically, we trained recurrent neural networks to draw simple figure trajectories, and found that altering reliance on sensory and prior signals during learning affected the networks' performance and the emergent internal representation. Specifically, both overly strong or weak reliance on predictions impaired network representations, but drawing performance did not always reflect this impairment. Thus, aberrant predictive coding causes asymmetries in behavioral output and internal representations. We discuss the findings in the context of autism spectrum disorder, where we hypothesize that too weak or too strong a reliance on predictions may be the cause of the large diversity of symptoms associated with this disorder.
Highlights
Predictive coding is a general theory that has been proposed as an underlying principle of cognitive processes in the brain [1,2,3]
It has been suggested that predictive coding could account for symptoms, such as hypersensitivities and the detail-focused processing style of individuals with autism spectrum disorder (ASD): Stronger reliance on sensory signals at the expense of prior predictions could alter information processing and affect perception and behavior [9, 10, 16, 17]
We evaluated how parametric modifications of the prediction ability of a computational model affect behavioral output and the quality of emergent internal network representations
Summary
Predictive coding is a general theory that has been proposed as an underlying principle of cognitive processes in the brain [1,2,3] According to this theory, new bottom-up sensory signals are integrated with top-down prior predictions to form posterior perceptions. The relative reliance on predictions at the expense of sensory signals is an important parameter: attending to one’s own predictions is necessary to exploit previous experiences, but attending to Prediction Deficits Trigger Asymmetries sensory signals is required for learning new and previously unobserved patterns. How humans integrate these two signals depends on the situational context and previous experiences. It has been suggested that predictive coding could account for symptoms, such as hypersensitivities and the detail-focused processing style of individuals with ASD: Stronger reliance on sensory signals at the expense of prior predictions could alter information processing and affect perception and behavior [9, 10, 16, 17]
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