Proceedings of the Annual Meeting of the Cognitive Science Society

Consider a classic statistical learning (SL) paradigm, where participants hear an uninterrupted stream of syllables in seemingly random order. In fact, the sequence is generated by repeating 4 word-like patterns, each comprised of 3 syllables. After brief exposure, adults and infants can discriminate ‘words' from the sequence from other syllable sequences (‘nonwords' that did not occur in exposure). If syllables have a fixed duration (e.g., 333.3 ms), syllable rate is fixed (e.g., 3/s or 3hz) and so is word rate (e.g., 1hz). If EEG is acquired during exposure, neural phase-locking is observed, initially to the syllable rate, and gradually to the word rate. This has been interpreted as a neural index of word learning. We tested whether two models that can simulate human SL behavior could simulate neural entrainment (Simple Recurrent Net- works [SRNs] or multi-layer perceptrons [MLPs, feedforward neural networks]). Both models could, although SRNs provided a better fit to correlations observed between entrainment and behavior. We also discovered that raw input sequences (even for a single syllable) have rhythmic properties that generate apparent ‘entrainment' when treated like EEG signals – without learning. We discuss theoretical implications for SL and challenges for interpreting phase-locked entrainment.