Technical Program

Paper Detail

Paper: PS-2A.29
Session: Poster Session 2A
Location: Symphony/Overture
Session Time: Friday, September 7, 17:15 - 19:15
Presentation Time:Friday, September 7, 17:15 - 19:15
Presentation: Poster
Publication: 2018 Conference on Cognitive Computational Neuroscience, 5-8 September 2018, Philadelphia, Pennsylvania
Paper Title: Evidence for chunking vs. statistical learning in motor sequence production
Manuscript:  Click here to view manuscript
Authors: Nicola J. Popp, Neda Kordjaz, Paul Gribble, Jörn Diedrichsen, University of Western Ontario, Canada
Abstract: Many complex behaviors consist of sequentially ordered actions. When acquiring a novel sequential skill, the transition between actions can be performed with increasing speed. This observation has led to the idea that the elementary actions are bound together during the learning process. Two ideas for this process have been proposed: First, statistical probabilities between different elementary actions could be acquired. Secondly, discrete groupings of elementary actions – so-called chunks - could emerge with learning. We discuss the differences between these two ideas and compare the ability of the two models to predict inter-press time intervals (IPIs) measured by a discrete sequence production task. We find a greater ability of the chunk model to predict participants’ IPIs throughout learning.