Displaying 101 - 109 of 109
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Verdonschot, R. G., & Tamaoka, K. (
Eds. ). (2015). The production of speech sounds across languages [Special Issue]. Japanese Psychological Research, 57(1). -
Verhoef, T., Roberts, S. G., & Dingemanse, M. (2015). Emergence of systematic iconicity: Transmission, interaction and analogy. In D. Noelle, R. Dale, A. S. Warlaumont, J. Yoshimi, T. Matlock, C. D. Jennings, & P. P. Maglio (
Eds. ), Proceedings of the 37th Annual Meeting of the Cognitive Science Society (CogSci 2015) (pp. 2481-2486). Austin, Tx: Cognitive Science Society.Abstract
Languages combine arbitrary and iconic signals. How do iconic signals emerge and when do they persist? We present an experimental study of the role of iconicity in the emergence of structure in an artificial language. Using an iterated communication game in which we control the signalling medium as well as the meaning space, we study the evolution of communicative signals in transmission chains. This sheds light on how affordances of the communication medium shape and constrain the mappability and transmissibility of form-meaning pairs. We find that iconic signals can form the building blocks for wider compositional patterns -
Vosse, T., & Kempen, G. (1991). A hybrid model of human sentence processing: Parsing right-branching, center-embedded and cross-serial dependencies. In M. Tomita (
Ed. ), Proceedings of the Second International Workshop on Parsing Technologies. -
Wagner, A., & Braun, A. (2003). Is voice quality language-dependent? Acoustic analyses based on speakers of three different languages. In Proceedings of the 15th International Congress of Phonetic Sciences (ICPhS 2003) (pp. 651-654). Adelaide: Causal Productions.
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Wanrooij, K., De Vos, J., & Boersma, P. (2015). Distributional vowel training may not be effective for Dutch adults. In Scottish consortium for ICPhS 2015, M. Wolters, J. Livingstone, B. Beattie, R. Smith, M. MacMahon, J. Stuart-Smith, & J. Scobbie (
Eds. ), Proceedings of the 18th International Congress of Phonetic Sciences (ICPhS 2015). Glasgow: University of Glasgow.Abstract
Distributional vowel training for adults has been reported as “effective” for Spanish and Bulgarian learners of Dutch vowels, in studies using a behavioural task. A recent study did not yield a similar clear learning effect for Dutch learners of the English vowel contrast /æ/~/ε/, as measured with event-related potentials (ERPs). The present study aimed to examine the possibility that the latter result was related to the method. As in the ERP study, we tested whether distributional training improved Dutch adult learners’ perception of English /æ/~/ε/. However, we measured behaviour instead of ERPs, in a design identical to that used in the previous studies with Spanish learners. The results do not support an effect of distributional training and thus “replicate” the ERP study. We conclude that it remains unclear whether distributional vowel training is effective for Dutch adults. -
Weber, A., & Smits, R. (2003). Consonant and vowel confusion patterns by American English listeners. In M. J. Solé, D. Recasens, & J. Romero (
Eds. ), Proceedings of the 15th International Congress of Phonetic Sciences.Abstract
This study investigated the perception of American English phonemes by native listeners. Listeners identified either the consonant or the vowel in all possible English CV and VC syllables. The syllables were embedded in multispeaker babble at three signal-to-noise ratios (0 dB, 8 dB, and 16 dB). Effects of syllable position, signal-to-noise ratio, and articulatory features on vowel and consonant identification are discussed. The results constitute the largest source of data that is currently available on phoneme confusion patterns of American English phonemes by native listeners. -
Weber, A., & Smits, R. (2003). Consonant and vowel confusion patterns by American English listeners. In Proceedings of the 15th International Congress of Phonetic Sciences (ICPhS 2003) (pp. 1437-1440). Adelaide: Causal Productions.
Abstract
This study investigated the perception of American English phonemes by native listeners. Listeners identified either the consonant or the vowel in all possible English CV and VC syllables. The syllables were embedded in multispeaker babble at three signalto-noise ratios (0 dB, 8 dB, and 16 dB). Effects of syllable position, signal-to-noise ratio, and articulatory features on vowel and consonant identification are discussed. The results constitute the largest source of data that is currently available on phoneme confusion patterns of American English phonemes by native listeners. -
Wittenburg, P., van Kuijk, D., & Dijkstra, T. (1996). Modeling human word recognition with sequences of artificial neurons. In C. von der Malsburg, W. von Seelen, J. C. Vorbrüggen, & B. Sendhoff (
Eds. ), Artificial Neural Networks — ICANN 96. 1996 International Conference Bochum, Germany, July 16–19, 1996 Proceedings (pp. 347-352). Berlin: Springer.Abstract
A new psycholinguistically motivated and neural network based model of human word recognition is presented. In contrast to earlier models it uses real speech as input. At the word layer acoustical and temporal information is stored by sequences of connected sensory neurons which pass on sensor potentials to a word neuron. In experiments with a small lexicon which includes groups of very similar word forms, the model meets high standards with respect to word recognition and simulates a number of wellknown psycholinguistical effects. -
Zhang, Y., Yurovsky, D., & Yu, C. (2015). Statistical word learning is a continuous process: Evidence from the human simulation paradigm. In D. Noelle, R. Dale, A. Warlaumont, J. Yoshimi, T. Matlock, C. D. Jennings, & P. P. Maglio (
Eds. ), Proceedings of the 37th Annual Meeting of the Cognitive Science Society (CogSci 2015) (pp. 2422-2427). Austin: Cognitive Science Society.Abstract
In the word-learning domain, both adults and young children are able to find the correct referent of a word from highly ambiguous contexts that involve many words and objects by computing distributional statistics across the co-occurrences of words and referents at multiple naming moments (Yu & Smith, 2007; Smith & Yu, 2008). However, there is still debate regarding how learners accumulate distributional information to learn object labels in natural learning environments, and what underlying learning mechanism learners are most likely to adopt. Using the Human Simulation Paradigm (Gillette, Gleitman, Gleitman & Lederer, 1999), we found that participants’ learning performance gradually improved and that their ability to remember and carry over partial knowledge from past learning instances facilitated subsequent learning. These results support the statistical learning model that word learning is a continuous process.
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