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It is well documented that humans can extract patterns from continuous input through Statistical Learning (SL) mechanisms. The exact computations underlying this ability, however, remain unclear. One outstanding controversy is whether learners extract global clusters from the continuous input, or whether they are tuned to local co-occurrences of pairs of elements. Here we adopt a novel framework to address this issue, applying a generative latent-mixture Bayesian model to data tracking SL as it unfolds online using a self-paced learning paradigm. This framework not only speaks to whether SL proceeds through computations of global patterns versus local co-occurrences, but also reveals the extent to which specific individuals employ these computations. Our results provide evidence for inter-individual mixture, with different reliance on the two types of computations across individuals. We discuss the implications of these findings for understanding the nature of SL and individual-differences in this ability., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Recent research on the effects of letter transposition in Indo-European Languages has shown that readers are surprisingly tolerant of these manipulations in a range of tasks. This evidence has motivated the development of new computational models of reading that regard flexibility in positional coding to be a core and universal principle of the reading process. Here we argue that such approach does not capture cross-linguistic differences in transposed-letter effects, nor do they explain them. To address this issue, we investigated how a simple domain-general connectionist architecture performs in tasks such as letter-transposition and letter substitution when it had learned to process words in the context of different linguistic environments. The results show that in spite of of the neurobiological noise involved in registering letter-position in all languages, flexibility and inflexibility in coding letter order is also shaped by the statistical orthographic properties of words in a language, such as the relative prevalence of anagrams. Our learning model also generated novel predictions for targeted empirical research, demonstrating a clear advantage of learning models for studying visual word recognition.

The Parallel Distributed Processing (PDP) framework is built on neural-style computation, and is thus well-suited for simulating the neural implementation of cognition. However, relatively little cognitive modeling work has concerned neural measures, instead focusing on behavior. Here, we extend a PDP model of reading-related components in the Event-Related Potential (ERP) to simulation of the N400 repetition effect. We accomplish this by incorporating the dynamics of cortical post-synaptic potentials--the source of the ERP signal--into the model. Simulations demonstrate that application of these dynamics is critical for model elicitation of repetition effects in the time and frequency domains. We conclude that by advancing a neurocomputational understanding of repetition effects, we are able to posit an interpretation of their source that is both explicitly specified and mechanistically different from the well-accepted cognitive one., (Copyright © 2014 Elsevier Inc. All rights reserved.)