the-motor-implicit-motor-sequence-learning-foot-stepping-serial

The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task

This protocol describes a modified serial reaction time (SRT) task used to study implicit motor sequence learning. Unlike the classic SRT task that ...

University of Maryland, College Park

Implicit sequence learning is ubiquitous in our daily life. However, it is unclear whether the initial acquisition of sequences results from learning to chunk items (i.e., chunk learning) or learning the underlying statistical regularities (i.e., statistical learning). By grouping responses with or without a distinct chunk or statistical structure into segments and comparing these responses, previous studies have demonstrated both chunk and statistical learning. However, few studies have considered the response sequence as a whole and examined the temporal dependency of the entire sequence, where the temporal dependencies could disclose the internal representations of chunk and statistical learning. Participants performed a serial reaction time (SRT) task under different stimulus interval conditions. We found that sequence learning reflected by reaction time (RT) rather than motor improvements represented by movement time (MT). The temporal dependency of RT and MT revealed that both RT and MT displayed recursive patterns caused by biomechanical effects of response locations and foot transitions. Chunking was noticeable only in the presence of the recurring RT or MT but vanished after the recursive component was removed, implying that chunk formation may result from biomechanical constraints rather than learning itself. In addition, we observed notable first-order autocorrelations in RT. This trial-to-trial association enhanced as learning progressed regardless of stimulus intervals, reflecting the internal cognitive representation of the first-order stimulus contingencies. Our results suggest that initial acquisition of implicit sequences may arise from first-order statistical learning rather than chunk learning.

New insights into statistical learning and chunk learning in implicit sequence acquisition.

Timing control, such as producing movements at a given rate or synchronizing movements to an external event, has been studied through a finger-tapping task where timing is measured at the initial contact between finger and tapping surface or the point when a key is pressed. However, the point of peak force is after the time registered at the tapping surface and thus is a less obvious but still an important event during finger tapping. Here, we compared the time at initial contact with the time at peak force as participants tapped their finger on a force sensor at a given rate after the metronome was turned off (continuation task) or in synchrony with the metronome (sensorimotor synchronization task). We found that, in the continuation task, timing was comparably accurate between initial contact and peak force. These two timing events also exhibited similar trial-by-trial statistical dependence (i.e., lag-one autocorrelation). However, the central clock variability was lower at the peak force than the initial contact. In the synchronization task, timing control at peak force appeared to be less variable and more accurate than that at initial contact. In addition to lower central clock variability, the mean SE magnitude at peak force (SEP) was around zero while SE at initial contact (SEC) was negative. Although SEC and SEP demonstrated the same trial-by-trial statistical dependence, we found that participants adjusted the time of tapping to correct SEP, but not SEC, toward zero. These results suggest that timing at peak force is a meaningful target of timing control, particularly in synchronization tapping. This result may explain the fact that SE at initial contact is typically negative as widely observed in the preexisting literature.

Yue Du

Department of Kinesiology

University of Maryland, College Park

The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task