Speech perception Relating features of hearing to the perception of speech.
Reading & Speech Perception
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Transcript of Reading & Speech Perception
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Reading & Speech Perception
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Connectionist Approach
• E.g., Seidenberg and McClelland (1989) and Plaut (1996).
• Central to these models is the absence of any lexicon. Instead, rely on distributed representations
• The model has no stored information about words and ‘… knowledge of words is encoded in the connections in the network.’
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ContextGrammar
pragmatics
Semanticsmeaning
Orthographyprint
Phonologyspeech
Phonological pathway
Semantic pathway
Connectionist framework for lexical processing, adapted from Seidenberg and McClelland (1989) and Plaut et al (1996).
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Plaut et al. (1996)
Graphemes(input)
Hidden units
Phonemes(output)
/th/ /ih/ /k/
th i ck Orthographyprint
Phonologyspeech
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Plaut et al. (1996) Simulations
• Network learned from 3000 written-spoken word pairs by backpropagation. Performance of the network closely resembled that of adult readers
• Predictions:– Irregular slower than regular:
RT( Pint ) > RT( Pond ) – Frequency effect:
RT( Cottage ) > RT( House )– Consistentency effects for nonwords:
RT( MAVE ) > RT( NUST )– Lesions led to decreases in performance on irregular
words, especially low frequency words
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Deep Dyslexia: example patient
Semantic Errors
canoe kayakonion orangewindow shadepaper pencilnail fingernailache Alka Seltzer
Visual Errorsfear flagrage race
Nonwords:no responsesubstitution of visually similar word (fank -> bank)
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Simulations of Deep Dyslexia
Semanticsmeaning
Orthographyprint
Phonologyspeech
Plaut and Shallice (1993); Hinton, Plaut and Shallice (1993)
Next slide only shows this portion of model
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Structure of Model
Grapheme units: one unit for each letter/position pair
Hidden units to allow a non-linear mapping
Sememe units: one per feature of the meaning
Recurrently connected clean-up units: to capture regularities among sememes
Cleanup units: part of a feedback loop that adjusts the sememe output to match the meaning of words precisely
Plaut and Shallice (1993); Hinton, Plaut and Shallice (1993)
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Structure of Model• Grapheme units: one
unit for each letter/position pair
• Intermediate units: learning (nonlinear) associations between letters and meaning units
• Sememe (Meaning) units: representation based on semantic features
• Cleanup units: part of a feedback loop that adjusts the sememe output to match the meaning of words precisely
Plaut and Shallice (1993); Hinton, Plaut and Shallice (1993)
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What the network learns
• Learning was done with back-propagation
• The network created semantic attractors: each word meaning is a point in semantic space and has its own basin of attraction.
• For a demonstration of attractor networks with visual patterns: http://www.cbu.edu/~pong/ai/hopfield/hopfieldapplet.html
• Damage to the sememe or clean-up units can change the boundaries of the attractors. This explains semantic errors. Meanings fall into a neighboring attractor.
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Semantic Space and Effects of Network Damage
• Activations of meaning units can be represented in high-dimensional semantic space
• With network damage, regions of attraction change
• Semantic Errors:
“BED” “COT”
• Visual Errors:
“CAT” “COT”
Plaut and Shallice (1993); Hinton, Plaut and Shallice (1993)
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SPEECH PERCEPTION &
CONTEXT EFFECTS
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Differences among items that fall into different categories are exaggerated, and differences among items that fall into the same category are minimized.
(from Rob Goldstone, Indiana University)
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Categorization
Perceptual Similarity
categorical perception
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Some physical continua are perceived continuouslyE.g.:• Color• Pitch• Loudness• Brightness• Angle• Weight• Etc.
Per
cen
t “L
oud”
res
pons
es
Magnitude of Stimulus (e.g. Loudness)
Some are not …
Per
cen
t re
spon
ses
Magnitude of Stimulus
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Examples
• from “LAKE” to “RAKE”– http://www.psych.ufl.edu/~white/Cate_per.htm
• from /da/ to /ga/
Good /ga/Good /da/
1 2 3 4 5 6 7 8
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Identification: Discontinuity at Boundary
% o
f /g
a/
resp
onse
100%
0%
50%
Token
1 2 3 4 5 6 7 8
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Pairwise discrimination
Good /ga/Good /da/
1 2 3 4 5 6 7 8
Discriminate these pairs
Discriminate these pairs
(straddle the category boundary)
Discriminate these pairs
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Pairwise Discrimination(same/different)
0102030405060708090
100
1_2 2_3 3_4 4_5 5_6 6_7 7_8
Pair of stimuli
% C
orre
ct D
iscr
imin
atio
n
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What Happened?
1 2 3 4 5 6 7 8
1 2 3 4 5 6 7 8
Physical World
Perceptual Representation
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Categorical Perception
• Identification influences discrimination
• This an example of how high level cognitive processes (i.e., categorization) can influence perceptual processes
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Lexical Identification Shift
Ganong (1980) J. Exp. Psych: HPP 6, 110-125
• Identification experiment
• VOT continuum• word at one end,
non-word at the other
Bias to interpret sounds as words
nonword-word: dask-taskword-nonword: dash-tash
short VOT (d) long VOT (t)
% /d/
100
0
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Phonemic restoration
• If a speech sound is replaced by a noise (a cough or a buzz), then listeners think they have heard the speech sound anyway. Furthermore, they cannot tell exactly where the noise was in the utterance. For instance:
Auditory presentation Perception
Legislature legislatureLegi_lature legi latureLegi*lature legisture
It was found that the *eel was on the axle. wheel
It was found that the *eel was on the shoe. heel
It was found that the *eel was on the orange. peel
It was found that the *eel was on the table. meal
Warren, R. M. (1970). Perceptual restorations of missing speech sounds. Science, 167, 392-393.
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Phoneme monitoring (PM)
• Subjects hear words, and have to press a button as soon as they hear a pre-specified target phoneme. Easy form: the target phoneme is always in the same position; Difficult form: the target phoneme can occur anywhere in the words.
• Phoneme monitoring is faster in high frequency words than in low frequency words or in nonwords in the easy form. This suggests that there is top-down influence.
there are two ways in which we identify phonemes, either via top-down information or via bottom-up information.
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TRACE model
• Similar to interactive activation model but applied to speech recognition
• Connections between levels are bi-directional and excitatory top-down effects
• Connections within levels are inhibitory producing competition between alternatives
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TRACE model
• Phonemes activate word candidates.
• Candidates compete with each other
• Winner completes missing phoneme information
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TRACE model• Phonemes are processed one at a time• System activates candidate words that are consistent
with current information• Candidates compete with each other• Winner is selected and competitors are inhibited
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Effect of Word Frequency on Eye Fixations
X
bench
bed
bell
lobster
“Pick up the bench”
(Dahan, Magnuson, & Tanenhaus, 2001)
More fixations are directed to high-frequency related distractor than low-frequency distractorPictures of these objects
= bench= bed= bell= lobster