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. Project 2. - PowerPoint PPT PresentationTRANSCRIPT
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
Compensation for coarticulation (CfC): Bottom-up, top-down, and motor
influences
Viswanathan, Magnuson, & Fowler, in preparation
Compensation for coarticulation
• Perception of a front-back continuum is influenced by preceding context (Mann, 1980; Mann & Repp, 1981)
Idealized CfC data
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1 2 3 4 5 6 7 8 9
[da]-[ga] step
Percent "g" responses
No context
[al] (front)
[ar] (back)
Explanation 1: compensation for coarticulation
Canonical [d]
[d] after [r]
Canonical [g]
[g] after [l]
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
POA and F3 are confounded in English
…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
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
aR Front
aL Back
Key
al ar
aR aL
al Front
ar Back
ga-da continuum
Per
cent
age
ga
judg
men
tsPredictions : Gestural
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
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
0
20
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
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?
Project 1
Time course of spoken word recognition
Eyetracking
computer
Eye camera
Scene camera
Allopenna, Magnuson & Tanenhaus (1998)Do rhymes compete?
‘Pick up the beaker’
Allopenna et al. Results
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).
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
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
Replicated cohort and rhyme effects
Day 1
Replicated cohort and rhyme effects
Day 1 Day 2
Effects modulated by target and competitor frequency
Effects modulated by target and competitor frequency
Absent neighbors compete
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…)