Towards a lexical fuzzy logical model of perception: The time-course of information in lexical identification of face-to-face speech

Jesse, A. (2005). Towards a lexical fuzzy logical model of perception: The time-course of information in lexical identification of face-to-face speech. PhD Thesis, University of California, Santa Cruz.
In face-to-face communication, information from the face as well as from the voice contributes to the identification of spoken words. This dissertation investigates the time-course of the evaluation and integration of visual and auditory speech in audiovisual word identification. A large-scale audiovisual gating study extends previous research on this topic by (1) using a set of words that includes all possible initial consonants in English in three vowel contexts, (2) tracking the information processing for individual words not only across modalities, but also over time, and (3) testing quantitative models of the time-course of multimodal word recognition. There was an advantage in accuracy for audiovisual speech over auditory-only and visual-only speech. Auditory performance was, however, close to ceiling while performance on visual-only trials was near the floor of the scale, but well above chance. Visual information was used at all gates to identify the presented words. Information theoretic feature analyses of the confusion matrices revealed that the auditory signal is highly informative about voicing, manner, frication, duration, and place of articulation. Visual speech is mostly informative about place of articulation, but also about frication and duration. The auditory signal provides more information about the place of articulation for back consonants, whereas the visual signal provides more information for the labial consonants. The data were sufficient to discriminate between models of audiovisual word recognition. The Fuzzy Logical Model of Perception (FLMP; Massaro, 1998) gave a better account of the confusion matrix data than additive models of perception. A dynamic version of the FLMP was expanded to account for the evaluation and integration of information over time. This dynamic FLMP provided a better description of the data than dynamic additive competitor models. The present study builds a good foundation to investigate the role of the complex interplay between stimulus information and the structure of the lexicon. It provides an important step in building a formal representation of a lexical dynamic FLMP that can account not only for the time-course of speech information and its perceptual processing, but also for lexical influences.
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