Abstract
Computational models that simulate individuals’ movements in pursuit-tracking tasks have been used to elucidate mechanisms of human motor control. Whilst there is evidence that individuals demonstrate idiosyncratic control-tracking strategies, it remains unclear whether models can be sensitive to these idiosyncrasies. Perceptual control theory (PCT) provides a unique model architecture with an internally set reference value parameter, and can be optimized to fit an individual’s tracking behavior. The current study investigated whether PCT models could show temporal stability and individual specificity over time. Twenty adults completed three blocks of 15 1-min, pursuit-tracking trials. Two blocks (training and post-training) were completed in one session and the third was completed after 1 week (follow-up). The target moved in a one-dimensional, pseudorandom pattern. PCT models were optimized to the training data using a least-mean-squares algorithm, and validated with data from post-training and follow-up. We found significant inter-individual variability (partial η2: .464–.697) and intra-individual consistency (Cronbach’s α: .880–.976) in parameter estimates. Polynomial regression revealed that all model parameters, including the reference value parameter, contribute to simulation accuracy. Participants’ tracking performances were significantly more accurately simulated by models developed from their own tracking data than by models developed from other participants’ data. We conclude that PCT models can be optimized to simulate the performance of an individual and that the test-retest reliability of individual models is a necessary criterion for evaluating computational models of human performance.
Highlights
Computational models that simulate individuals’ movements in pursuit-tracking tasks have been used to elucidate mechanisms of human motor control
We conclude that Perceptual control theory (PCT) models can be optimized to simulate the performance of an individual and that the test-retest reliability of individual models is a necessary criterion for evaluating computational models of human performance
Estimated control parameters were highly consistent over time, whilst individual differences in control strategies were discriminated by the computational model
Summary
Computational models that simulate individuals’ movements in pursuit-tracking tasks have been used to elucidate mechanisms of human motor control. It has been established that humans display idiosyncratic invariants in some movement parameters (Morasso, 1981) These characteristic individual Btraits^ should be evident between individuals’ manual tracking behavior and show temporal stability within individuals. We review the evidence for such idiosyncrasies in individual tracking performance, and outline a model derived from the perceptual control theory (PCT; Powers, 1973) that is capable of capturing. The current study explores the potential for this computational model to individually characterize 20 individuals’ control strategies and differentially simulate their performance. The construction of dynamic models of pursuit-tracking performance in healthy and atypical populations has helped to elucidate the nature of individual differences in tracking
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