Statistical learning of syllable sequences as trajectories through a perceptual similarity space

Learning from sequential statistics is a general capacity common to many model systems. One form of statistical learning (SL) – learning to segment “words’ from continuous streams of speech syllables in which the only segmentation cue is ostensibly the transitional (or conditional) probability from one syllable to the next – has been studied in great detail. Typically, this phenomenon is modeled as the calculation of probabilities over discrete, featureless units. Here we present an alternative model, in which sequences are learned as trajectories through a similarity space. A simple recurrent network that coded syllables using representations capture the similarity relations among syllables correctly simulated the result of a classic SL study. We then used the simulations to identify a set of “words” that produces the reverse of the typical SL, i.e., part-words are predicted to be more familiar than words. Results from two experiments are consistent with simulation results.