Jim Magnuson University of Connecticut and Haskins Laboratories
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Transcript of Jim Magnuson University of Connecticut and Haskins Laboratories
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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
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Project 2
Compensation for coarticulation (CfC): Bottom-up, top-down, and motor
influences
Viswanathan, Magnuson, & Fowler, in preparation
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Compensation for coarticulation
• Perception of a front-back continuum is influenced by preceding context (Mann, 1980; Mann & Repp, 1981)
Idealized CfC data
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1 2 3 4 5 6 7 8 9
[da]-[ga] step
Percent "g" responses
No context
[al] (front)
[ar] (back)
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Explanation 1: compensation for coarticulation
Canonical [d]
[d] after [r]
Canonical [g]
[g] after [l]
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Explanation 2: Sensory contrastTouch hot
Touch lukewarm
Feels cold!
Touch cold
Feels hot!
High tone
Sounds low! Sounds high!
Medium tone
Low tone
Lotto & Kluender (1997): tone explanation holds for [r l] / [d g] case -- front = high F3
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POA and F3 are confounded in English
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…but not in Tamil
Formant Place of F1 F2 F3 F4 articulation [l]
536 1050 2637 3598 Front
[r]
492 1465 1818 3016 Back
[R]
521 1448 1946 3591 Front
[L] 411 1686 1935 3146 Back
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Predictions : Gestural
aR Front
aL Back
Key
al ar
aR aL
al Front
ar Back
ga-da continuum
Per
cent
age
ga
judg
men
ts
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aR Front
aL Back
Key
al ar
aR aL
al Front
ar Back
ga-da continuum
Per
cent
age
ga
judg
men
tsPredictions : Gestural
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Predictions : Contrast
al 2637
ar 1818
ga-da continuum
Per
cent
age
ga
judg
men
tsKey
al ar
aR aL
Key
al ar
aR aL
aR 1946
aL 1935
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Predictions : Contrast
al 2637
ar 1818
ga-da continuum
Per
cent
age
ga
judg
men
tsKey
al ar
aR aL
Key
al ar
aR aL
aR 1946
aL 1935
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0
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40
60
80
100
0 1 2 3 4 5 6 7 8 9 10
ga-da continuum member
% ga judgments
alaraRaL
Results of Experiment 1
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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?
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Project 1
Time course of spoken word recognition
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Eyetracking
computer
Eye camera
Scene camera
Allopenna, Magnuson & Tanenhaus (1998)Do rhymes compete?
‘Pick up the beaker’
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Allopenna et al. Results
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Allopenna et al. Results
Linking hypothesisFixations 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).
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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
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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
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Replicated cohort and rhyme effects
Day 1
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Replicated cohort and rhyme effects
Day 1 Day 2
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Effects modulated by target and competitor frequency
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Effects modulated by target and competitor frequency
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Absent neighbors compete
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Where next
“Where is the pibo?”Find the pibo
• Individual differences• Children and impaired populations (SLI, reading disabled,
low-literacy adults, elderly adults, aphasic patients, autistic children with hyperlexia…)