A multi-representation approach to the contextual interference effect: effects of sequence length and practice

C-SMB 2.0: Integrating over 25 years of motor sequencing research with the Discrete Sequence Production task

An exhaustive review is reported of over 25 years of research with the Discrete Sequence Production (DSP) task as reported in well over 100 articles. In line with the increasing call for theory development, this culminates into proposing the second version of the Cognitive framework of Sequential Motor Behavior (C-SMB 2.0), which brings together known models from cognitive psychology, cognitive neuroscience, and motor learning. This processing framework accounts for the many different behavioral results obtained with the DSP task and unveils important properties of the cognitive system. C-SMB 2.0 assumes that a versatile central processor (CP) develops multimodal, central-symbolic representations of short motor segments by repeatedly storing the elements of these segments in short-term memory (STM). Independently, the repeated processing by modality-specific perceptual and motor processors (PPs and MPs) and by the CP when executing sequences gradually associates successively used representations at each processing level. The high dependency of these representations on active context information allows for the rapid serial activation of the sequence elements as well as for the executive control of tasks as a whole. Speculations are eventually offered as to how the various cognitive processes could plausibly find their neural underpinnings within the intricate networks of the brain.

Initial motor skill performance predicts future performance, but not learning

People show vast variability in skill performance and learning. What determines a person's individual performance and learning ability? In this study we explored the possibility to predict participants' future performance and learning, based on their behavior during initial skill acquisition. We recruited a large online multi-session sample of participants performing a sequential tapping skill learning task. We used machine learning to predict future performance and learning from raw data acquired during initial skill acquisition, and from engineered features calculated from the raw data. Strong correlations were observed between initial and final performance, and individual learning was not predicted. While canonical experimental tasks developed and selected to detect average effects may constrain insights regarding individual variability, development of novel tasks may shed light on the underlying mechanism of individual skill learning, relevant for real-life scenarios.

Association Between Cross-Limb Transfer and Practice Organization

Although the importance of practice has been evidenced in early studies of cross-limb transfer, the association between cross-limb transfer and practice organization remains unknown. The two primary means of organizing practice are constant practice (CP) and variable practice (VP). When the same generalized motor program governs the motor responses, VP produces better transfer than CP. Thus, we hypothesized that VP would generate a higher cross-limb transfer level than CP. We assigned 40 participants to CP or VP groups and conducted an experiment consisting of three phases: pre-test, practice, and transfer test. At pre-test, all participants practiced eight trials of a sequence key-pressing task with the non-dominant hand (NDH). After the pre-test, all participants performed a practice phase of 72 trials with the dominant hand, but CP and VP groups underwent their different practice schedules (CP or VP) such that the CP group practiced a sequence key-pressing task in one sequence, and the VP group practiced four different sequences randomly. On the transfer test all partcipants completed eight trials with the NDH 24 hours after the practice phase. The CP group showed better performance than the VP group on the transfer test, and we concluded that the effects of practice organization in cross-limb transfer are distinct from intra- and inter-task transfer such that the specificity of practice explained the benefits of CG.

Consolidation of motor sequence learning eliminates susceptibility of SMAproper to TMS: a combined rTMS and cTBS study

Earlier research suggested that after 210 practice trials, the supplementary motor area (SMA) is involved in executing all responses of familiar 6-key sequences in a discrete sequence production (DSP) task (Verwey, Lammens, and van Honk, 2002). This was indicated by slowing of each response 20 and 25 min after the SMA had been stimulated for 20 min using repetitive transcranial magnetic stimulation (rTMS). The present study used a similar approach to assess the effects of TMS to the more posterior SMAproper at the end of practice and also 24 h later. As expected stimulation of SMAproper with 20 min of 1 Hz rTMS and 40 s of continuous theta burst stimulation (cTBS) immediately after practice slowed sequence execution relative to a sham TMS condition, but stimulation on the day following practice did not cause slowing. This indicates that offline consolidation makes learning robust against stimulation of SMAproper. Execution of all responses in the sequence was disrupted 0, 20, and 40 min after rTMS, but after cTBS, this occurred only after 40 min. The results suggest that it is implicit sequence knowledge that is processed by the SMAproper and that consolidates.