Eye-tracking and gating experiments examined reference comprehension with fluent (Click on the red. . .) and disfluent (Click on [pause] thee uh red . . .) instructions while listeners viewed displays with 2 familiar (e.g., ice cream cones) and 2 unfamiliar objects (e.g., squiggly shapes). Disfluent instructions made unfamiliar objects more expected, which influenced listeners' on-line hypotheses from the onset of the color word. The unfamiliarity bias was sharply reduced by instructions that the speaker had object agnosia, and thus difficulty naming familiar objects (Experiment 2), but was not affected by intermittent sources of speaker distraction (beeps and construction noises; Experiments 3). The authors conclude that listeners can make situation-specific inferences about likely sources of disfluency, but there are some limitations to these attributions.

Knowledge of musical rules and structures has been reliably demonstrated in humans of different ages, cultures, and levels of music training, and has been linked to our musical preferences. However, how humans acquire knowledge of and develop preferences for music remains unknown. The present study shows that humans rapidly develop knowledge and preferences when given limited exposure to a new musical system. Using a non-traditional, unfamiliar musical scale (Bohlen-Pierce scale), we created finite-state musical grammars from which we composed sets of melodies. After 25-30 min of passive exposure to the melodies, participants showed extensive learning as characterized by recognition, generalization, and sensitivity to the event frequencies in their given grammar, as well as increased preference for repeated melodies in the new musical system. Results provide evidence that a domain-general statistical learning mechanism may account for much of the human appreciation for music.

When natural language input contains grammatical forms that are used probabilistically and inconsistently, learners will sometimes reproduce the inconsistencies; but sometimes they will instead regularize the use of these forms, introducing consistency in the language that was not present in the input. In this paper we ask what produces such regularization. We conducted three artificial language experiments, varying the use of determiners in the types of inconsistency with which they are used, and also comparing adult and child learners. In Experiment 1 we presented adult learners with scattered inconsistency – the use of multiple determiners varying in frequency in the same context – and found that adults will reproduce these inconsistencies at low levels of scatter, but at very high levels of scatter will regularize the determiner system, producing the most frequent determiner form almost all the time. In Experiment 2 we showed that this is not merely the result of frequency: when determiners are used with low frequencies but in consistent contexts, adults will learn all of the determiners veridically. In Experiment 3 we compared adult and child learners, finding that children will almost always regularize inconsistent forms, whereas adult learners will only regularize the most complex inconsistencies. Taken together, these results suggest that regularization processes in natural language learning, such as those seen in the acquisition of language from non-native speakers or in the formation of young languages, may depend crucially on the nature of language learning by young children.

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