TRACX 2.0: A memory-based, biologically-plausible model of sequence segmentation and chunk extraction

TRACX (French, Addyman, & Mareschal, 2011) is a recursive connectionist system that implicitly extracts chunks from sequence data. It can account for experiments on infant statistical learning and adult implicit learning, as well as realworld phoneme data, and an experiment using backward transitional probabilities that simple recurrent networks cannot account for. One criticism of TRACX, however, is the implausibility in a connectionist model of if-then-else statements. In particular, one of these statements controls what data is copied from the model’s internal memory into its input, based on a hard error threshold. We, therefore, developed a more biologically-plausible version of TRACX devoid of if-then-else statements, relying only on spreading activation and without any learning error threshold. This new model, TRACX 2.0, performs essentially as well as the original TRACX model and, in addition, has two fewer parameters than the original and accounts for the graded nature of chunks.