On the Generalization of Simple Alternating Category Structures.

A fundamental question in the study of human cognition is how people learn to predict the category membership of an example from its properties. Leading approaches account for a wide range of data in terms of comparison to stored examples, abstractions capturing statistical regularities, or logical rules. Across three experiments, participants learned a category structure in a low-dimension, continuous-valued space consisting of regularly alternating regions of class membership (A B A B). The dependent measure was generalization performance for novel items outside the range of the training space. Human learners often extended the alternation pattern--a finding of critical interest given that leading theories of categorization based on similarity or dimensional rules fail to predict this behavior. In addition, we provide novel theoretical interpretations of the observed phenomenon.

Sentence context influences the subjective perception of foreign accents.

We examined whether sentence context (the predictability of the final word) influences listeners' ratings of foreign-accented words. Previous work has demonstrated that accent manipulations affect listeners' processing of spoken language. We examined the converse of this relationship; whether context manipulations affect listeners' perceptions of accents. If there is a bidirectional relationship, listeners should be more likely to rate an accent as strong when the accented word is not predicted by the sentence. In Experiment 1, the results revealed that participants were significantly more likely to rate words spoken by foreign-accented speakers as "Strong Accent" in the unpredictable sentences when compared to the predictable sentences. Moreover, in Experiment 2, this effect was replicated and extended to a native speaker. These results support the idea that there is a bidirectional relationship between language processing and perceptions of accents. We discuss the practical implications for foreign-accented speakers.