DD303 Language Processing

Ken Adams
Flashcards by Ken Adams, updated more than 1 year ago
Ken Adams
Created by Ken Adams over 5 years ago


DD303 Ch8 Language Processing

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What are the main differences between spoken & written word recognition? Spoken: >Some aspects innate >Primary >Need to segment words >Extended in time but each word exists only briefly Written: >Learned (but likely we use mechanisms for spoken lang recognition) >Secondary >Segmentation is done by white space on page >Extended in space but words are permanent Suggests, different models needed to explain each
Segmenting spoken words - 2 methods? Pre-lexical (bottom-up): >Characteristics indicate boundary >English - stressed 1st syllable - Cutler & Carter >Different for other languages >Cutler & Norris - word spot task, embedded in nonsense syllables >BUT must be alternative for words that break rules (eg guitar, yacht) Lexical models (top-down): >Phonological representation (knowledge of what word sounds like) >Recognise each word & predict boundary >Match sounds against store (mental lexicon) >Marslen-Wilson & Welsh - problems w/ short words?
Saffran et al (1996)? >Babies - head turning experiment >Artificial language >Id words in 2 mins >Suggests implicit learning ability via co-occurrence of syllables & statistical properties
Parallel activation in spoken word recognition? Simultaneous hearing & evaluation Content processed before end of word Marslen-Wilson et al ('78 & '87) - Cohort model Word start - initial cohort Word continues - list of matches reduces Single word recognised - uniqueness point Cross modal priming & semantic similarity Gaskell & Marslen-Wilson ('02) - # words simultaneously activated limited; suggests priming effect weakens
Lexical competition - connectionist models eg TRACE - McCelland & Elman 3 levels - 1) Phonetic features; 2) Phonemes; 3) Word Activation - bottom-up - nodes representing phonemes activated when hear a word When all phonemes for a word are activated, activation spreads to the word Amount depends on closeness of match Phonemes c, o, en, f = "confess" & "confetti" Inhibition @ word level = competitive part of model Support - McQueen et al ('84) - segmentation via word spotting
Visual word recognition - IAC model McClelland & Rumelhart ('81) Top-down 3 levels: 1) visual features; 2) letters; 3) words Explains word superiority effect (letters recognised faster if in string ie word) BUT effect can be explained with no top-down feedback using spelling representations in mental lexicon (Grainger & Jacobs '94) Top-down feedback needed for word recognition? = Contentious
Interaction of spoken & written words - DRC model Coltheart et al ('01) Dual Route Cascade Model Assembled (rule-based, regular words) vs addressed (lexical, irregular words) phonology Speed of written word naming = assess which route used Regular faster than irregular (only low frequency) Regular use either route, irregular only one route - gives advantage for regular words Phonology important even in silent reading (e.g. rose, rows) (Van Orden, 1987) Glushko ('79) neighbouring word properties impact recognition rate - eg save, wave, gave - have BUT : Jared ('02) - word naming times affected much more by consistency than regularity
Eye movements in reading Active - coordinated Saccades Fixations - 200ms (dependent on word frequency - Rayner & Duffy) O'Regan & Jacobs - recognise words faster @ optimal viewing position - mid-word Shillock et al - info content greatest mid-word; less info at word ending ~10% saccades backwards - more with low predictability/ambiguity Text is present 'ahead' as well as 'before' - parallel process? Flanker effect = yes; normal reading evidence = no
What is the mental lexicon? Store Semantic r'ships - how meanings related Semantic content - meaning Phonological Orthographic
Morphology? Size of units in lexicon Morpheme - smallest meaningful sub-unit Inflections - plural; tense (-ed, -ing) Derivations - change word type (weak>weakly=adjective>verb) Irregular forms - mouse>mice Share morphemes - depart, department = unrelated 2 cognitive approaches: Full-listing: all words stored in entirety Decompositional: Taft & Forster - words broken into morphemes & stored w/ links Support:Marslen-Wilson et al ('94) - words w/ same morpheme prime strongly; faster processing prime & target Related meanings (cruel>cruelty) NOT shared prefix (casual>casualty)
Accessing Meaning - Semantic Representations? Type of info accessed when word recognised? Spreading activation models (eg Collins & Loftus) - Connectionist: links = semantic r'ship; meanings related Vs Featural theories - meanings=set of concepts stored in Lexicon (Both models poorly specified & data can often be interpreted to fit either)
Semantic priming? Semantic priming experiments: e.g. bread>butter, cheddar>cheese Associative links between prime & target Lucas ('00) - Non associative (e.g. horse and sheep, much weaker link & data robust) BUT evidence different visual & perceptual properties resulting in some degree of priming (e.g. size/shape)
Semantic ambiguity? Homonyms (e.g. bank – river vs money) Context allows selection of appropriate meaning, but how? >Autonomous (all meanings accessed then select contextually appropriate one) >Interactive (context rules out some meanings before they are fully activated) >Swinney ('79) supported autonomous model - Regardless of bias, both possible words primed; BUT if delay (1 sec) between stimuli, contextually appropriate word primed >Lucas ('99) supported interactive model - more priming re appropriate meanings then inappropriate
Processing sentences? Sentences are novel Implies process has to be constructive, rather than just recognition Parsing = deciding each word’s syntactic (grammatical) role in the sentence to understand the sentence
Incremental model of parsing? Syntactic structure built incrementally as each word encountered Most evidence for this one Tyler & Marslen-Wilson ('77) Ambiguous phrase "landing planes" Preceding context determines if landing = adjective or verb Speed of response dependent on prior context Supports incremental parsing Incompatible with delayed model - it wouldn't predict an effect of appropriateness, as waiting to the end of the sentence, to parse
Garden path model of parsing? "The horse raced past the barn fell" Fraizer ('79) Influential >Incremental & autonomous >Misinterpretations constantly corrected >Listener might be ‘led down the garden path’ >Serial model – parser makes only one potential parse at a time >Parsing based on info from sentence itself, not context (syntactic info only)
Constraint-based models of parsing? MacDonald et al ('94) Parsing = parallel & interactive Multiple interpretations evaluated in parallel No autonomous component Info from lexicon added during evaluation process Evidence: Multiple meanings of ambiguous words briefly activated Frequency of meanings (in the language) determines activations of alternatives Biasing contexts increases activation of an alternative
Is parsing autonomous? For: Ferreira & Clifton ('86) - eye-tracking re verb in "Defendant examined..." - garden path effect found Against: Trueswell et al - garden path sentences could be affected by meaning: same contexts as F&C not as constraining as their example Parsing can be affected by semantic plausibility of various parses of the system
Constraints on parsing? Ambiguity reduced by intonation - listeners use this (Warren) Tannenhaus et al ('95) "Put the apple on the towel in the box” environmental info used to resolve ambiguity & reduce/remove garden path effect
Starr & Rayner ('01) - key points? >Eye movements in reading claimed to reflect high level cognitive processes >Studies showing effects of parafoveal (right of fixation) info uptake support models of parallel processing in reading. >Challenges serial spotlight models of attention in reading >Frequency & predictability effects on fixation times suggest top-down cognitive influences of lexical & comprehension processes >Supports models of integrated & parallel processing (MacDonald et al '94)
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