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)

unconsciousexogenousbottom-upbenefit

controlledendogenoustop-downcost & benefit

RT

RT

StimulusQuality

Context

Frequency

Stim Qual X Freq

Context X Stim Qual

Context X Freq

Related cat dog 500 500

Unrelated bed dog 550 600

Neutral xxx dog 550 550

PRIME TARGET

prime target

SOA < 250 SOA > 250

RT

SOA = Stimulus Onset Asynchrony

ISI = InterStimulus Interval

time

Measures• Standard behavioral techniques

• Eye movements (EMs)

• Neuroimaging– “Electrical”: EEG, MEG, (TMS)

– “Blood flow”: PET, fMRI

MEASURE

Normal reading

TASK

fixation duration (as well aslocation and sequence of EMs)

TIME RES.

GOOD

POOR“blood flow” imaging: fMRI, PET

“electrical” imaging: EEG, MEG

various word tasks

ms-by-ms

seconds

various word tasks

naming

categorisationlexical decision

Standard word recognition paradigms (± priming, ± masking):

RT~500 ms~600 ms~800 ms

~250 ms

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 clown vs. dwarf

– spelling-to-sound regularity hint vs. pint

– neighborhood consistency made vs. gave

– morphemes• prefix vs. pseudoprefix remind vs. relish• compound vs. pseudocompound cowboy vs. carpet

Components

• Word variables– word length duke vs. fisherman

– word frequency student vs. steward

– AoA dinosaur vs. university

– ambiguity bank vs. edge, brim

– syntactic class open vs. closed; A,N,V

– concreteness tree vs. idea

– affective tone love vs. farm vs. fire

– etc.

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)

Direct route(addressed)

phonology

semantics

orthography

Indirect route(assembled)

Models• Dual-route account (Coltheart, 1978)

Direct route(addressed)

phonology

semantics

orthography

Indirect route(assembled)

Deep dyslexia- visual/semantic errors (sympathy -> orchestra)- can’t read nonwords

Models• Dual-route account (Coltheart, 1978)

Direct route(addressed)

phonology

semantics

orthography

Indirect route(assembled)

Surface dyslexia- regularization errors (broad -> brode)- Reg wds,NWs are OK (GPC rules intact)

Models• Interactive (Morton, 1969; Seidenberg & McClelland, 1989)

/m A k/

phonology

meaning

orthography

M A K E

context

Models• Modular (Forster, 1979; Fodor, 1983)

decision output

Lexicalprocessor

Syntacticprocessor

Messageprocessor

GeneralProblemSolver

input features

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)

MEASURE

Normal reading

TASK

fixation duration (as well aslocation and sequence of EMs)

TIME RES.

GOOD

POOR“blood flow” imaging: fMRI, PET

“electrical” imaging: EEG, MEG

various word tasks

ms-by-ms

seconds

various word tasks

naming

categorisationlexical decision

Standard word recognition paradigms (± priming, ± masking):

RT~500 ms~600 ms~800 ms

~250 ms

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

fixation onset

visual cortex

0 50 100 150 200 250 300 350 400

LEXICAL ACCESS

fixation onset

initiate saccade

modify EM program

shift attention, initiate EM

motor program

signal to eye

muscles

EYE MOVEMENTS

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

P1

N1

P300

N400

Numberof trials

1

2

4

8

16

EEG

ERP

(Sereno & Rayner, Trends in Cognitive Sciences, 2003)

DIVERSION

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)

Single channel ERP

-5

-4

-3

-2

-1

0

1

2

3

0 50 100 150 200 250 300 350 400

Time (ms)

Voltage(µV)

Series1

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?”

• A word frequency effect [ HF < LF ] is used as a marker (index) of successful word recognition (lexical access).

The sore on Tam-Tam’s was swollen.(HF) back(LF) rump

• Word frequency effect = differential response to commonly used high-frequency (HF) words vs. low-frequency (LF) words that occur much less often:

• If you can track frequency, you can track lexical access...

553 ms490 ms

259 ms275 ms

280 ms293 ms

(Sereno & Rayner, Trends in Cognitive Sciences, 2003)

(Sereno & Rayner, Trends in Cognitive Sciences, 2003)