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You mustlogin to add citations.Yang returns to the problem of referential ambiguity, addressed in the opening paper by Gleitman and Trueswell. Using a computational approach, he argues that “big data” approaches to resolving referential ambiguity are destined to fail, because of the inevitable computational explosion needed to keep track of contextual associations present when a word is uttered. Yang tests several computational models, two of which depend on one‐trial learning, as described in Gleitman and Trueswell’s paper. He concludes that such models outperform cross‐situational learning (...) models, thereby formalizing, reinforcing, and advancing the conclusions offered by Gleitman and Trueswell. (shrink) | |
Orienting biases for speech may provide a foundation for language development. Although human infants show a bias for listening to speech from birth, the relation of a speech bias to later language development has not been established. Here, we examine whether infants' attention to speech directly predicts expressive vocabulary. Infants listened to speech or non-speech in a preferential listening procedure. Results show that infants' attention to speech at 12 months significantly predicted expressive vocabulary at 18 months, while indices of general (...) development did not. No predictive relationships were found for infants' attention to non-speech, or overall attention to sounds, suggesting that the relationship between speech and expressive vocabulary was not a function of infants' general attentiveness. Potentially ancient evolutionary perceptual capacities such as biases for conspecific vocalizations may provide a foundation for proficiency in formal systems such language, much like the approximate number sense may provide a foundation for formal mathematics. (shrink) | |
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The roles of linguistic, cognitive, and social-pragmatic processes in word learning are well established. If statistical mechanisms also contribute to word learning, they must interact with these processes; however, there exists little evidence for such mechanistic synergy. Adults use co-occurrence statistics to encode speech–object pairings with detailed sensitivity in stochastic learning environments (Vouloumanos, 2008). Here, we replicate this statistical work with nonspeech sounds and compare the results with the previous speech studies to examine whether exclusion constraints contribute equally to the (...) statistical learning of speech–object and nonspeech–object associations. In environments in which performance could benefit from exclusion, we find a learning advantage for speech over nonspeech, revealing an interaction between statistical and exclusion processes in associative word learning. (shrink) | |
Binary judgement on under-informative utterances is the most widely used methodology to test children’s ability to generate implicatures. Accepting under-informative utterances is considered a failure to generate implicatures. We present off-line and reaction time evidence for the Pragmatic Tolerance Hypothesis, according to which some children who accept under-informative utterances are in fact competent with implicature but do not consider pragmatic violations grave enough to reject the critical utterance. Seventy-five Dutch-speaking four to nine-year-olds completed a binary and a ternary judgement task. (...) Half of the children who accepted an utterance in Experiment A penalised it in Experiment B. Reaction times revealed that these children experienced a slow-down in the critical utterances in Experiment A, suggesting that they detected the pragmatic violation even though they did not reject it. We propose that binary judgement tasks systematically underestimate children’s competence with pragmatics. (shrink) No categories |
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