WORD RECOGNTION
(Sereno, 2/06)
I. Introduction to psycholinguistics
II. Basic units of language
III. Word recognition
IV. Word frequency & lexical ambiguity
III. Word Recognition
How long does it take to recognise a visual word?
– What is meant by “recognition” or “lexical access”?
– Can lexical access be accurately measured?
– What factors affect lexical access and when?
The “magic moment” (Balota, 1990) of lexical access:
“At this moment, presumably there is recognition that the
stimulus is a word, and access of other information (such
as the meaning of the word, its syntactic class, its sound,
and its spelling) would be rapid if not immediate.”
(Pollatsek & Rayner, 1990)
III. Word Recognition
•
•
•
•
•
Measures
Components
Models
Eye movements (EMs)
Event-related potentials (ERPs)
Measures
• Standard behavioral techniques
• Eye movements (EMs)
• Neuroimaging
– “Electrical”: EEG, MEG, (TMS)
– “Blood flow”: PET, fMRI
Measures
• Standard behavioural techniques
– lexical decision, naming, categorisation; also
RSVP, self-paced reading
– priming, masking, lateralised presentation
– Donders (1868): subtractive method
• assumes strictly serial stages of processing
• additive vs. interactive effects
– automatic vs. strategic (Posner & Snyder, 1975)
controlled
unconscious
endogenous
exogenous
top-down
bottom-up
cost & benefit
benefit
Stim Qual X Freq
RT
Context
Stimulus
Quality
Frequency
Context X Stim Qual
Context X Freq
RT
PRIME
RT
SOA < 250 SOA > 250
TARGET
Related
cat
dog
500
500
Unrelated
bed
dog
550
600
Neutral
xxx
dog
550
550
time
prime
target
ISI = InterStimulus Interval
SOA = Stimulus Onset Asynchrony
Measures
• Standard behavioral techniques
• Eye movements (EMs)
• Neuroimaging
– “Electrical”: EEG, MEG, (TMS)
– “Blood flow”: PET, fMRI
TASK
MEASURE
TIME RES.
various word tasks
“electrical” imaging: EEG, MEG
ms-by-ms
Normal reading
fixation duration (as well as
location and sequence of EMs)
~250 ms
GOOD
Standard word recognition paradigms (± priming, ± masking):
naming
lexical decision
categorisation
various word tasks
RT
~500 ms
~600 ms
~800 ms
“blood flow” imaging: fMRI, PET
seconds
POOR
Components
• Orthography of language
– English vs. Hebrew or Japanese
• Language skill
– beginning (novice) vs. skilled (expert) reader
– easy vs. difficult text
Components
• Intraword variables
– word-initial bi/tri-grams
– spelling-to-sound regularity
– neighborhood consistency
– morphemes
• prefix vs. pseudoprefix
• compound vs. pseudocompound
clown vs. dwarf
hint vs. pint
made vs. gave
remind vs. relish
cowboy vs. carpet
Components
• Word variables
– word length
– word frequency
– AoA
– ambiguity
– syntactic class
– concreteness
– affective tone
– etc.
duke vs. fisherman
student vs. steward
dinosaur vs. university
bank vs. edge, brim
open vs. closed; A,N,V
tree vs. idea
love vs. farm vs. fire
Components
• Extraword variables
– contextual predictability
The person saw the...
moustache.
The barber trimmed the...
– syntactic complexity
Mary took the book.
*Mary took the book was good.
Mary knew the book.
Mary knew the book was good.
*Mary hoped the book. Mary hoped the book was good.
– discourse factors (anaphora, elaborative inferences)
He assaulted her with his weapon.... ...knife...
stabbed
Models
• Dual-route account (Coltheart, 1978)
semantics
phonology
Indirect route
(assembled)
Direct route
(addressed)
orthography
Models
• Dual-route account (Coltheart, 1978)
Deep dyslexia
- visual/semantic errors
(sympathy -> orchestra)
- can’t read nonwords
semantics
phonology
Indirect route
(assembled)
Direct route
(addressed)
orthography
Models
• Dual-route account (Coltheart, 1978)
Surface dyslexia
- regularization errors
(broad -> brode)
- Reg wds,NWs are OK
(GPC rules intact)
phonology
Indirect route
(assembled)
semantics
Direct route
(addressed)
orthography
Models
• Interactive (Morton, 1969; Seidenberg & McClelland, 1989)
context
meaning
orthography
phonology
MAKE
/m A k/
Models
• Modular (Forster, 1979; Fodor, 1983)
Message
processor
Syntactic
processor
General
Problem
Solver
Lexical
processor
input features
decision output
Models
• Hybrid
– 2-stage: generate candidate set  selection
– (Becker & Killion; Norris; Potter)
III. Word Recognition
•
•
•
•
•
Measures
Components
Models
Eye movements (EMs)
Event-related potentials (ERPs)
TASK
MEASURE
TIME RES.
various word tasks
“electrical” imaging: EEG, MEG
ms-by-ms
Normal reading
fixation duration (as well as
location and sequence of EMs)
~250 ms
GOOD
Standard word recognition paradigms (± priming, ± masking):
naming
lexical decision
categorisation
various word tasks
RT
~500 ms
~600 ms
~800 ms
“blood flow” imaging: fMRI, PET
seconds
POOR
Tools of choice:
• Recording eye movements in reading
• Recording ERPs in language tasks
Eye Movements (EMs)
Best on-line measure of visual word
recognition in the context of normal
reading:
• Fast (avg fixation time ≈ 250 ms)
• Ecologically valid task
• Eye-mind span is tight
EYE MOVEMENTS
fixation
onset
visual
cortex
shift attention,
initiate EM
motor program
signal
to eye initiate
muscles saccade
fixation
onset
modify EM
program
LEXICAL ACCESS
0
50
100
150
200
250
300
350
400
ERPs
Best real-time measure of brain activity
associated with the perceptual and cognitive
processing of words:
• Continuous ms-by-ms record of events
• Early, exogenous components (before 200 ms)
should reflect lexical processing
Number
of trials
EEG
1
2
4
8
P1
P300
ERP
16
N1
N400
(Sereno & Rayner, Trends in Cognitive Sciences, 2003)
High-density ERP Analysis:
A case of “too many notes”?
High-density ERP Analysis:
Typical approaches for space & time
• Pick ‘n choose favourite electrode and ERP
component
High-density ERP Analysis:
Typical approaches for space & time
• Pick ‘n choose favourite electrode and ERP
component
• Hunt down where/when the effect is strongest and
gather data from those electrodes/time window
High-density ERP Analysis:
Typical approaches for space & time
• Pick ‘n choose favourite electrode and ERP
component
• Hunt down where/when the effect is strongest and
gather data from those electrodes/time window
• Procrustean regions analysis (turtle shell) or series of
pre-set time windows (eg, 50, 100, 200 ms)
High-density ERP Analysis:
Typical approaches for space & time
• Pick ‘n choose favourite electrode and ERP
component
• Hunt down where/when the effect is strongest and
gather data from those electrodes/time window
• Procrustean regions analysis (turtle shell) or series of
pre-set time windows (eg, 50, 100, 200 ms)
• Spatial and/or temporal principal component
analysis (PCA)
Scalp topography of the N1 @ 132-192 ms
SF1 loadings
Voltages
(Sereno, Brewer, & O’Donnell, Psychological Science, 2003)
Scalp topography of the N1 @ 132-192 ms
SF1 loadings
Voltages
± 0.7 factor loading contours
WORD RECOGNTION
(Sereno, 1/05)
I. Introduction to psycholinguistics
II. Basic units of language
III. Word recognition
IV. Word frequency & lexical ambiguity
Frequency: “When is access?”
• Word frequency effect = differential response to commonly
used high-frequency (HF) words vs. low-frequency (LF)
words that occur much less often:
The sore on Tam-Tam’s
(HF) backwas swollen.
(LF) rump
• A word frequency effect [ HF < LF ] is used as a marker
(index) of successful word recognition (lexical access).
• If you can track frequency, you can track lexical access...
490 ms
553 ms
259 ms
275 ms
280 ms
293 ms
(Sereno & Rayner, Trends in Cognitive Sciences, 2003)
(Sereno & Rayner, Trends in Cognitive Sciences, 2003)