Pim Levelt

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  • Cholin, J., & Levelt, W. J. M. (2009). Effects of syllable preparation and syllable frequency in speech production: Further evidence for syllabic units at a post-lexical level. Language and Cognitive Processes, 24, 662-684. doi:10.1080/01690960802348852.

    Abstract

    In the current paper, we asked at what level in the speech planning process speakers retrieve stored syllables. There is evidence that syllable structure plays an essential role in the phonological encoding of words (e.g., online syllabification and phonological word formation). There is also evidence that syllables are retrieved as whole units. However, findings that clearly pinpoint these effects to specific levels in speech planning are scarce. We used a naming variant of the implicit priming paradigm to contrast voice onset latencies for frequency-manipulated disyllabic Dutch pseudo-words. While prior implicit priming studies only manipulated the item's form and/or syllable structure overlap we introduced syllable frequency as an additional factor. If the preparation effect for syllables obtained in the implicit priming paradigm proceeds beyond phonological planning, i.e., includes the retrieval of stored syllables, then the preparation effect should differ for high- and low frequency syllables. The findings reported here confirm this prediction: Low-frequency syllables benefit significantly more from the preparation than high-frequency syllables. Our findings support the notion of a mental syllabary at a post-lexical level, between the levels of phonological and phonetic encoding.
  • Hagoort, P., & Levelt, W. J. M. (2009). The speaking brain. Science, 326(5951), 372-373. doi:10.1126/science.1181675.

    Abstract

    How does intention to speak become the action of speaking? It involves the generation of a preverbal message that is tailored to the requirements of a particular language, and through a series of steps, the message is transformed into a linear sequence of speech sounds (1, 2). These steps include retrieving different kinds of information from memory (semantic, syntactic, and phonological), and combining them into larger structures, a process called unification. Despite general agreement about the steps that connect intention to articulation, there is no consensus about their temporal profile or the role of feedback from later steps (3, 4). In addition, since the discovery by the French physician Pierre Paul Broca (in 1865) of the role of the left inferior frontal cortex in speaking, relatively little progress has been made in understanding the neural infrastructure that supports speech production (5). One reason is that the characteristics of natural language are uniquely human, and thus the neurobiology of language lacks an adequate animal model. But on page 445 of this issue, Sahin et al. (6) demonstrate, by recording neuronal activity in the human brain, that different kinds of linguistic information are indeed sequentially processed within Broca's area.
  • Van de Geer, J. P., & Levelt, W. J. M. (1963). Detection of visual patterns disturbed by noise: An exploratory study. Quarterly Journal of Experimental Psychology, 15, 192-204. doi:10.1080/17470216308416324.

    Abstract

    An introductory study of the perception of stochastically specified events is reported. The initial problem was to determine whether the perceiver can split visual input data of this kind into random and determined components. The inability of subjects to do so with the stimulus material used (a filmlike sequence of dot patterns), led to the more general question of how subjects code this kind of visual material. To meet the difficulty of defining the subjects' responses, two experiments were designed. In both, patterns were presented as a rapid sequence of dots on a screen. The patterns were more or less disturbed by “noise,” i.e. the dots did not appear exactly at their proper places. In the first experiment the response was a rating on a semantic scale, in the second an identification from among a set of alternative patterns. The results of these experiments give some insight in the coding systems adopted by the subjects. First, noise appears to be detrimental to pattern recognition, especially to patterns with little spread. Second, this shows connections with the factors obtained from analysis of the semantic ratings, e.g. easily disturbed patterns show a large drop in the semantic regularity factor, when only a little noise is added.

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