講者：張智宏 副教授 （國立中央大學認知神經科學研究所）
講題：Human Motor Sequence Learning: Distributional Analysis in Manual and Speech Serial Reaction Time Tasks
時間：2022年5月5日（星期四）14:10 – 16:00
Acquisition of sequential motor skill is a crucial part of human motor learning. Sequential motor learning has been examined extensively with the Serial Reaction Time Task (SRTT). Extant studies on SRTT have focused exclusively on the evolvement of the central tendency of RT throughout learning blocks, which largely ignored the progression of essential characteristics of RT distribution in learning. Moreover, most studies investigate manual SRTT and ignored sequence learning in effectors other than hands. In the first study, we carried out distributional analysis on the performance of manual and speech SRTT by fitting the shifted-Wald distribution on blocked RTs, and compared the trends of learning as revealed in parameters capturing the onset, variation around mode, and right-tail mass of distribution. The results indicate that all shift-Wald parameters of the RT distribution decrease as the task proceeds, reflecting the general trend of learning. Interestingly, speech appears to contain stronger variation than manual response throughout different phase of learning. The learning index of these various measures were found to be comparable between response modalities. MEG on the same paradigm revealed that RT and GAMMA, but not BETA, modulation are sensitive to learning in both effectors. There are distinct loci for manual and speech sequence production, and no between-effector difference in the strength of sequential learning as revealed in either behavioral or MEG learning indices. As such, our study demonstrated a richer and fruitful approach of re-examining sequence learning in SRTT by close inspection on RT distributions and MEG spectrum in different effectors.