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Several studies found that word duration is predicted by its informativity (average past predictability) above and beyond predictability in the current context, suggesting retrieval of phonetically-specific tokens from memory. We show that a significant effect of informativity can emerge from noise in predictability estimates. We generate durations from a model in which 38% of log duration is predicted by log probability, as in our actual data, but the rest is normally-distributed noise. Estimated probability for each word in each context is then generated from a binomial distribution with success probability from the real sample and size matching context frequency. We compute informativity and fit the regression model we fit to the real data. Informativity is significant in 100% of simulations, even though probability is the only true predictor, although the effect of informativity is smaller in simulation (0.7 < b < 0.10) than in the actual corpus (b = 0.12).

In the 1990s, language acquisition researchers and theoretical linguists developed an interest in learning mechanisms, and learning theorists rediscovered the verbal learning tradition. Nonetheless, learning theory and language acquisition continued to develop largely independently, which has stymied progress in both fields. However, exciting progress is happening in applying learning theory to language, and, more recently, in using language learning data to advance domain‐general learning theory. These developments raise hopes for a bidirectional flow of information between the fields. The importance of language data for learning theory and of learning theory for understanding language is briefly discussed. [ABSTRACT FROM AUTHOR]

Language learning often involves predicting categorical outcomes based on a set of cues. Error in predicting a categorical outcome is the difference between zero or one and the outcome's current level of activation. The current activation level of a categorical outcome is argued to be a non-linear, logistic function of activation the outcome receives from the cues. Crucially, the logistic activation function asymptotically approaches zero and one without ever reaching or overshooting them. This allows error-driven learning to avoid settling on spurious associations between cues and outcomes that never co-occur ("spurious excitement"). In an artificial language experiment, humans are also not observed to show spurious excitement. The logistic activation function is compared to alternative solutions to spurious excitement, and shown to have important advantages. It enables one-shot learning and steep, S-shaped learning curves, and explains why cue competition in language learning can be overcome with additional training. [ABSTRACT FROM AUTHOR]