PubMed · 341792
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J R Erickson, M R Jones. 1978. Thinking.. https://doi.org/10.1146/annurev.ps.29.020178.000425
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This study explores the role that gesture plays in the earliest stages of language learning. We describe how one-word speakers use gesture in combination with speech in their spontaneous communications, and interpret gesture presented in combination with speech in an experimental situation. Forty one-word speakers (ages 1;2.22 to 2;4.6) were videotaped in a free-play session which provided data on the child's spontaneous gesture and speech production. The children were also given a comprehension task in which the presence and absence of gesture were systematically varied in relation to speech. We found that (1) all of the children spontaneously produced gestures in combination with speech, and (2) all of the children were able to understand gesture when it was presented in combination with speech, not only when the gesture was redundant with speech but also when the gesture substituted for speech. These data suggest that, even at this young age, gesture naturally forms an integrated system with speech in both production and comprehension.
Adaptive network and exemplar-similarity models were compared on their ability to predict category learning and transfer data. An exemplar-based network (Kruschke, 1990a, 1990b, 1992) that combines key aspects of both modeling approaches was also tested. The exemplar-based network incorporates an exemplar-based category representation in which exemplars become associated to categories through the same error-driven, interactive learning rules that are assumed in standard adaptive networks. Experiment 1, which partially replicated and extended the probabilistic classification learning paradigm of Gluck and Bower (1988a), demonstrated the importance of an error-driven learning rule. Experiment 2, which extended the classification learning paradigm of Medin and Schaffer (1978) that discriminated between exemplar and prototype models, demonstrated the importance of an exemplar-based category representation. Only the exemplar-based network accounted for all the major qualitative phenomena; it also achieved good quantitative predictions of the learning and transfer data in both experiments.