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Current methods to understand implicit motor sequence learning inadequately assess motor skill acquisition in daily life. Using fixed sequences in the serial reaction time task is not ideal as participants may become aware of the sequence, thereby changing the learning from implicit to explicit. Probabilistic sequences, in which stimuli are linked by statistical, rather than deterministic, associations can ensure that learning remains implicit. Additionally, the processes underlying the learning of motor sequences may differ based on sequence structure. Here, the authors compared the learning of fixed and probabilistic sequences to randomly ordered stimuli using a modified serial reaction time task. Both the fixed and probabilistic sequence groups exhibited learning as indicated by decreased response time and variability. In the initial stage of learning, fixed sequences exhibited both online and offline gains in response time; however, only the offline gain was observed during the learning of probabilistic sequences. These results indicated that probabilistic structures may be learned differently from fixed structures and have important implications for our current understanding of motor learning. Probabilistic sequences more accurately reflect motor skill acquisition in daily life, offer ecological validity to the serial reaction time framework, and advance our understanding of motor learning.
Prashad and Clark are with the Neuroscience and Cognitive Science Program, University of Maryland, College Park, MD, USA. Prashad, Du, and Clark are with the Department of Kinesiology, University of Maryland, College Park, MD, USA. Prashad is now with the Department of Kinesiology and Educational Psychology, Washington State University, Pullman, WA, USA. Du is now with the Department of Neurology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.