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Jim Magnuson University of Connecticut and Haskins Laboratories

Two Projects (1) Time course of spoken word recognition (2) Compensation for coarticulation: Bottom-up, top-down, and motor influences. Jim Magnuson University of Connecticut and Haskins Laboratories. Project 2.

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Jim Magnuson University of Connecticut and Haskins Laboratories

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  1. Two Projects(1) Time course of spoken word recognition(2) Compensation for coarticulation: Bottom-up, top-down, and motor influences Jim Magnuson University of Connecticut and Haskins Laboratories

  2. Project 2 Compensation for coarticulation (CfC): Bottom-up, top-down, and motor influences Viswanathan, Magnuson, & Fowler, in preparation

  3. Compensation for coarticulation • Perception of a front-back continuum is influenced by preceding context (Mann, 1980; Mann & Repp, 1981)

  4. Canonical[d] Canonical[g] [d] after [r] [g] after [l] Explanation 1: compensation for coarticulation

  5. Feels hot! Feels cold! Explanation 2: Sensory contrast High tone Low tone Touch hot Touch cold Medium tone Touch lukewarm Sounds low! Sounds high! Lotto & Kluender (1997): tone explanation holds for [r l] / [d g] case -- front = high F3

  6. POA and F3 are confounded in English

  7. …but not in Tamil

  8. Key al ar aR aL Predictions : Gestural Percentage ga judgments ga-da continuum

  9. Percentage ga judgments ga-da continuum Key al ar aR aL Predictions : Gestural

  10. Key al ar aR aL Key al ar aR aL Predictions : Contrast Percentage ga judgments ga-da continuum

  11. Percentage ga judgments ga-da continuum Key al ar aR aL Key al ar aR aL Predictions : Contrast

  12. Results of Experiment 1

  13. Where next • Bottom-up: what dynamic information is specifying POA? • Top-down: lexical bias, orthographic bias • Motoric: do subject articulator positions or gestures influence CfC? • Is timing important?

  14. Project 1 Time course of spoken word recognition

  15. Allopenna, Magnuson & Tanenhaus (1998)Do rhymes compete? Eye Eye camera tracking computer Scene camera ‘Pick up the beaker’

  16. Allopenna et al. Results

  17. Allopenna et al. Results Linking hypothesis Fixations depend on (1) lexical activation and (2) the possible referents. Predictions are based on (1) lexical activation/competition of entire lexicon and (2) response probabilities calculated from the four possible items (Luce choice rule).

  18. Artificial LexiconsMagnuson, Tanenhaus, Dahan, & Aslin (2003) • We need to covary multiple interacting dimensions to understand time course • Words in natural languages do not fall into convenient levels • Artificial lexicon affords fine control over lexical variables • But: can people learn artificial words quickly enough and well enough? • Manipulate frequency, neighborhood density • Replicate: • Cohort and rhyme • Frequency • Absent competitor

  19. Method • 16 participants learned a 16-word lexicon • Words refer to shapes  • 7 contiguous cells randomly filled in a 5x5 grid • Random word  picture mapping for each subject • Four sets like: pibo pibu dibo dibu • Allows high- and low-frequency (HF vs. LF) items with HF or LF neighbors

  20. Replicated cohort and rhyme effects Day 1

  21. Replicated cohort and rhyme effects Day 1 Day 2

  22. Effects modulated by target and competitor frequency

  23. Effects modulated by target and competitor frequency

  24. Absent neighbors compete

  25. “Where is the pibo?” Find the pibo Where next • Individual differences • Children and impaired populations (SLI, reading disabled, low-literacy adults, elderly adults, aphasic patients, autistic children with hyperlexia…)

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