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Word Recognition (Sereno, 4/04). 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:
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Word Recognition (Sereno, 4/04) 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)
Background: Basic Units of Language A. ~5,000 languages phonemes morphemes sentences conversations (sounds) & words B. Phonemes = elementary sounds of speech • phonemes are not letters... to, too, two, through, threw, shoe, clue, view • vowel & consonant phonemes • 11-144 phonemes in any given language English has ~ 40; Hawaiian has ~16 • combining phonemes is rule-governed
Wordness: For each row of 3 possible new words, which one will probably never make it : ( blick splunge rlight sbarm wumple turl mancer nserht crelurious inther iwhucr neen shace fring ngout
“free” { { “bound” Basic Units of Language C. Morphemes = smallest meaningful unit of lang. • can be a word, word stem, or affix (prefix, suffix) word: help, love word stem: spir, ceive, duce prefix/suffix: re-, dis-, un- / -less, -ful, -er • derivational & inflectional morphemes derivational – change the grammatical class V + -able = Adj (adorable, believable) V + -er = N (singer, runner) inflectional – grammatical markers V + -ed = past tense (walked) N + -s = plural (cows)
Basic Units of Language C. Words • Content vs. function (open- vs. closed-class) words Content words = carry the main meaning nouns, verbs, adjectives, adverbs Function words = grammatical words articles (a, the, this), conjunctions (and, but), prepositions (in, above) Psychological reality of the content-function word distinction in aphasia selective impairment of content (Wernicke’s) or function words (Broca’s aphasia) • Cattell (1886) & Stroop (1925)
d k word --- d k d Word superiority effect (Cattell, 1886) • Reicher (1969); Wheeler (1970) • tachistoscopic presentation • more accurate identification of the letter when stimulus is a word • pseudoword superiorty effect
GREEN RED RED BLUE BLUE GREEN BLACK BLACK BLUE GREEN RED BLUE BLACK GREEN BLACK RED RED BLUE BLUE GREEN NAME THE COLOUR OF THE INK
homographs homophones Basic Units of Language C. Words • Ambiguity 1 word form, but 2 (or more) word meanings Ex: bank (N-N, “money” vs. “river”) watch (N-V, “clock” vs. “look”) bass (N-N, “guitar” vs. “fish”) 2 word forms, but 1 pronunciation Ex: sail/sale, right/write Generally unaware of ambiguity... even though it is quite pervasive even though it affects behaviour (RT, etc)
Basic Units of Language D. Sentences • Syntax = the rule-governed system for grouping words together into phrases and sentences • Sentences introduce a concept that they are about, the subject (or noun phrase), and then propose something about that concept, the predicate (or verb phrase). Ex: “The boy hit the ball.” doeractdone-to (thematic roles) subjectpredicate
Basic Units of Language D. Sentences • Same deep structure, different surface structure “The boy hit the ball.” (active) “The ball was hit by the ball.” (passive) • Same surface structure, different deep structure [The French bottle]NP [smells.]VP [The French]NP [bottle smells.]VP THEY are boring. VISITING THEM is boring. cf. ambig. figures in perception: 1 form, 2 interpretations “The French bottle smells.” “Visiting relatives can be boring.”
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Basic Units of Language D. Sentences • Syntactic ambiguities “She hit the boy with the big stick.” “She hit the boy with the runny nose.” Interpretation depends on structural preferences (certain constructions used more often, favoured), as well as the prior discourse context.
Word Recognition (Sereno, 4/04) • Measures • Components • Models • Eye movements (EMs) • Event-related potentials (ERPs) • Word frequency & lexical ambiguity
controlled endogenous top-down cost & benefit unconscious exogenous bottom-up benefit 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)
Measures • Eye movements • Neuroimaging • “Electrical”: EEG, MEG • “Blood flow”: PET, fMRI
Standard word recognition paradigms (± priming, ± masking): naming ~500 ms lexical decision RT ~600 ms categorisation ~800 ms TASK MEASURE TIME RES. GOOD various word tasks “electrical” imaging: EEG, MEG ms-by-ms fixation duration (as well as location and sequence of EMs) Normal reading ~250 ms various word tasks “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 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
semantics phonology orthography Models • Dual-route account (Coltheart, 1978) Direct route (addressed) Indirect route (assembled)
meaning context phonology orthography Models • Interactive (Morton, 1969; Seidenberg & McClelland, 1989) M A K E /m A k/
Lexical processor Syntactic processor Message processor General Problem Solver Models • Modular (Forster, 1979; Fodor, 1983) input features decision output
Models • Hybrid • 2-stage: generate candidate set selection • (Becker & Killion; Norris; Potter)
Word Recognition (Sereno, 4/04) • Measures • Components • Models • Eye movements (EMs) • Event-related potentials (ERPs) • Word frequency & lexical ambiguity
Standard word recognition paradigms (± priming, ± masking): naming ~500 ms lexical decision RT ~600 ms categorisation ~800 ms TASK MEASURE TIME RES. GOOD various word tasks “electrical” imaging: EEG, MEG ms-by-ms fixation duration (as well as location and sequence of EMs) Normal reading ~250 ms various word tasks “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
Number of trials EEG 1 2 4 8 P300 P1 ERP 16 N400 N1
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
High-density ERP Analysis:A case of “too many notes”? DIVERSION
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)
Word frequency • Word frequency effect represents the differential response to commonly used high-frequency (HF) words versus low-frequency (LF) words that occur much less often. • Presence of word frequency effects is used as a marker of successful word recognition or lexical access.
490 ms 553 ms 259 ms 275 ms 280 ms 293 ms (Sereno & Rayner, Trends in Cognitive Sciences, 2003)