Transcript Syllabus P140C (68530) Cognitive Science

Reading & Speech Perception
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.’
Context
Grammar
pragmatics
Semantics
meaning
Orthography
print
Semantic
pathway
Phonology Phonological
pathway
speech
Connectionist framework for lexical processing, adapted from Seidenberg
and McClelland (1989) and Plaut et al (1996).
Plaut et al. (1996)
th
i
Orthography
print
ck
Graphemes
(input)
Hidden units
Phonology
speech
Phonemes
(output)
/th/
/ih/
/k/
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
Deep Dyslexia: example patient
Semantic Errors
canoe  kayak
onion  orange
window  shade
paper  pencil
nail  fingernail
ache  Alka Seltzer
Visual Errors
fear  flag
rage  race
Nonwords:
no response
substitution of visually similar
word (fank -> bank)
Simulations of Deep Dyslexia
Semantics
meaning
Orthography
print
Next slide only shows this portion of model
Plaut and Shallice (1993); Hinton, Plaut and Shallice (1993)
Phonology
speech
Structure of Model
Recurrently connected clean-up units:
to capture regularities among sememes
Sememe units: one per feature of the
meaning
Hidden units to allow a non-linear mapping
Grapheme units: one unit for each letter/position pair
Plaut and Shallice (1993); Hinton, Plaut and Shallice (1993)
Cleanup units: part of
a feedback loop that
adjusts the sememe
output to match the
meaning of words
precisely
Structure of Model
Plaut and Shallice (1993); Hinton, Plaut and Shallice (1993)
•
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
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.
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)
SPEECH PERCEPTION
&
CONTEXT EFFECTS
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)
Categorization
categorical
perception
Perceptual Similarity
Some are not …
Magnitude of Stimulus (e.g. Loudness)
Percent responses
E.g.:
• Color
• Pitch
• Loudness
• Brightness
• Angle
• Weight
• Etc.
Percent “Loud” responses
Some physical continua are perceived continuously
Magnitude of Stimulus
Examples
• from “LAKE” to “RAKE”
– http://www.psych.ufl.edu/~white/Cate_per.htm
• from /da/ to /ga/
Good /da/
1
2
Good /ga/
3
4
5
6
7
8
Identification: Discontinuity at Boundary
% of /ga/ response
100%
50%
0%
1
2
3
4
5
Token
6
7
8
Pairwise discrimination
Good /da/
1
Good /ga/
2
Discriminate
these pairs
3
4
5
Discriminate
these pairs
(straddle the
category
boundary)
6
7
8
Discriminate
these pairs
% Correct Discrimination
Pairwise Discrimination
(same/different)
What Happened?
1
2
3
4
5
6
Physical World
7
Perceptual Representation
1 2 3 4
5 6 7 8
8
Categorical Perception
• Identification influences discrimination
• This an example of how high level cognitive processes
(i.e., categorization) can influence perceptual processes
Lexical Identification Shift
• Identification
experiment
• VOT continuum
• word at one end,
non-word at the
other
 Bias to interpret
sounds as words
nonword-word: dask-task
word-nonword: dash-tash
100
% /d/
0
short VOT (d)
long VOT (t)
Ganong (1980) J. Exp. Psych: HPP 6, 110-125
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
Legi_lature
Legi*lature
legislature
legi 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.
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.
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
TRACE model
• Phonemes activate word
candidates.
• Candidates compete with
each other
• Winner completes
missing phoneme
information
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
Effect of Word Frequency on Eye Fixations
“Pick up the bench”
= bench
= bed
= bell
= lobster
lobster
bench
X
bell
bed
Pictures of these objects
More fixations are directed to highfrequency related distractor than lowfrequency distractor
(Dahan, Magnuson, & Tanenhaus, 2001)