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Language and Communication - 2002: 

Language and Communication - 2002 Word Identification: Spoken Word Recognition and Lexical Ambiguity

Spoken Word Identification: The Segmentation Problem: 

Spoken Word Identification: The Segmentation Problem How do we divide the speech stream into a series of discrete words Each part of the speech stream should be part of one and only one word (the possible word constraint) (In English) the main content-bearing words tend to begin with stressed syllables (Cutler, Norris and colleagues)

Evidence for Segmentation in Progress: Activating spurious words: 

Evidence for Segmentation in Progress: Activating spurious words Shillcock (1990) He picked up the trombone rib Lexical context does not constrain spurious access

Some Properties of Spoken Word Identification: 

Some Properties of Spoken Word Identification Shadowing and word-monitoring tasks: latencies of 250-275 msec. subtract 50-75 msec for response execution = ~200 msec to identify word = before acoustic offset Context apparently aids recognition

Models of Spoken Word Identification: 

Models of Spoken Word Identification The TRACE (Interactive Activation) Model McClelland & Elman, 1986 The Cohort Model Marslen-Wilson & Welsh, 1978 Revised, Marslen-Wilson, 1989

TRACE: 

TRACE Like the interactive-activation model of printed word recognition, TRACE has three sets of interconnected detectors Feature detectors Phoneme detectors Word detectors These detectors span different stretches of the input (feature detector span small parts, word detectors span larger parts) The input is divided into “time slices” which are processed sequentially.

TRACE - continued: 

TRACE - continued Within a set (or level) connections are inhibitory E.g. evidence that a certain stretch of the input is the word “tip” is evidence that it is NOT any other word Between a set (or level) connections are excitatory E.g. evidence that a certain stretch of the input is the sound /t/ is evidence that it might be the beginning of the word “tip” Also, evidence that the word is “tip” is evidence that its parts are /t/ /i/ /p/, so there are “top-down” (feedback) effects in TRACE as in the interactive activation model Or inhibitory.. If it’s a /t/ it isn’t the beginning of “cat”

TRACE - continued: 

TRACE - continued Accounts for context effects Can handle (some) acoustic variability (and noise) Can account for phoneme restoration (Warren - “Open the oor” heard as “Open the door”) Can account for co-articulation effects Can find word boundaries (using the possible word constraint)

Marslen-Wilson’s Cohort Model: 

Marslen-Wilson’s Cohort Model The mental representations of words are activated (in parallel) on the basis of bottom-up input (sounds), and can be de-activated by subsequent bottom-up (phonological) and top-down (contextual) input.

Uniqueness and Recognition: 

Uniqueness and Recognition When we hear the beginning of a word this activates ALL words beginning with the same sound: the “word initial cohort”. Subsequent sounds eliminate candidates from the cohort until only one remains (failure to fit with context can also eliminate candidates) t - tea, tree, trick, tread, tressle, trespass, top, tick, etc. tr - tree, trick, tread, tressle, trespass, etc. tre - tread, tressle, trespass, etc. tres - tressle, trespass, etc. tresp - trespass.

Uniqueness and Recognition: 

Uniqueness and Recognition The uniqueness point is the point at which a word becomes uniquely identifiable from its initial sound sequence E.g. “dial” dayl| “crocodile” krokod| ayl UP UP For non-words there is a deviation point: a point at which the cohort is reduced to zero E.g. “zn | owble” would be rejected with a faster RT than “thousaj | ining” DP DP

Uniqueness and Recognition: 

Uniqueness and Recognition The recognition point is the point at which, empirically, a word is actually identified Empirical studies show that recognition point correlates with (and is closely tied to) the uniqueness point. phoneme monitoring latencies correlate with a priori cohort analysis (and one way to recognise word initial phonemes is to recognise the word and to know it begins with e.g. /p/)

Effects of Material beyond the UP / DP: 

Effects of Material beyond the UP / DP Auditory lexical decision task, pairs of non-words compared with the same Deviation Point, but one resembled a real word beyond (and before) the DP. e.g. rith | l | ik rith | l | an UP|DP UP|DP The cohort model predicts same RT for both; but first word (472ms) was slower than the second (372ms), and error rate was 3.5% for the first and 0.6% for the second. Conclude that the cohort model fails to account for this phenomenon.

Frequency Effects in Spoken Word Identification: 

Frequency Effects in Spoken Word Identification Marslen-Wilson: auditory lexical decision task with pairs of words with the same length, UP, and different frequencies. e.g. DIFFIC | ULT high frequency (250ms) DIFFID | ENT low frequency (379ms) Not immediately clear how the original version of the Cohort Model accounts for this effect

The Zwitserlood experiment: 

The Zwitserlood experiment cross-modal priming c a p t i ve c a p t ai n auditory prime: visual probe: slave ship or shop priming found to both alternatives in “early” condition only more priming found to “ship” — a frequency effect

Zwitserlood - Conclusion: 

Zwitserlood - Conclusion Zwitserlood’s experiment showed that frequency of a word affects the activation level of candidates in the early stages of lexical access, hence “there are relative frequency effects within the initial cohort, so that entry in the cohort cannot be all-or-none, but varies along a continuum…some candidates are more activated than others.” pp.60 Harley.

Need to Revise the Cohort Model- Further Evidence: 

Need to Revise the Cohort Model - Further Evidence We are capable of identifying a word when mispronounced (even at the beginning e.g. “shigarette”, and (sometimes) when we only hear a word from the middle on. The original cohort model cannot account for these effects

The Revised Cohort Model: 

The Revised Cohort Model Initial activation is (still) bottom-up Competition between active elements leave one element standing out above the rest. Incompatible bottom-up evidence does not eliminate a candidate (as it does in original), but partially deactivates it. Thus, revised version of model is much more similar to TRACE The highest ranking elements are assessed in parallel with respect to the interpretation — the best fit is integrated and (hence) recognized.

Slide19: 

Activation in the Revised Cohort Model time activation dog energise elephant wombat elegant c a p t i n captain captive

Neighbourhood effects: 

Neighbourhood effects People are faster to recognise a high frequency word which only has low frequency neighbours than vice versa. This effect is compatible with the revised cohort model. However, the model predicts that the size of the cohort at any point (number of competitors) does not affect the speed at which a target is recognised, only the time to reach uniqueness. However, cohort size does affect the time course of word recognition (Luce et al.).

Spoken Word Recognition: Conclusions: 

Spoken Word Recognition: Conclusions The two leading models, TRACE and the Revised Cohort Model, have much in common Both depend on competition between partially activated candidates for the word’s identity

Language and Communication: 

Language and Communication Lexical Ambiguity

Lexical Ambiguity: 

Lexical Ambiguity What happens when a word form (visual or auditory) is associated with two (or more) meanings, rather than one (e.g. bank, straw)? The appropriate meaning is usually determined by context As readers or listeners we don’t usually notice the ambiguity, but what effect does it have?

Slide24: 

Lexical ambiguity Where is the ball? Look at that chip.

Lexical Ambiguity - MacKay 1966: 

Lexical Ambiguity - MacKay 1966 Sentence completion task After talking the right/left turn at the intersection, I…. Harder to complete after “right” (ambiguous) than “left” (unambiguous)

Lexical Ambiguity - Models: 

Lexical Ambiguity - Models Context-guided (selective) access (Schvaneveldt) Appropriate meaning is chosen by context, others are not considered But how could it work? Ordered Access (Hogaboam & Perfetti, 1975) Most common meaning checked first Accepted if it fits context Otherwise other meanings are checked Multiple Access (Swinney, 1979) All meanings accessed, context selects among them Reordered Access Model (Duffy, Morris, & Rayner, 1988)

Contradictory evidence from the 1970s: 

Contradictory evidence from the 1970s For selective access: e.g. Hogaboam & Perfetti, 1975, ambiguity detection task: Found longer RT when word used with it more common meaning (e.g. ink pen, rather than sheep pen) The accountant filled his pen with ink. The farmer put the sheep in the pen. For multiple access: e.g. Foss, 1970, phoneme monitoring: People are slower to detect /b/ in A) than in B) because “straw” is ambiguous, even though the context (“farmer”) strongly favours one meaning. Suggests both meanings accessed. (compare the old MacKay finding) A) The farmer put his straw beside the machine. B) The farmer put his hay beside the machine. However, this task is sensitive to the length of preceding words. A short word may not be fully processed before the next word begins. A longer word can be identified before their end. Problem: majority of polysemous English words are short. If this is controlled for, the effect disappears (Mehler et al., 1978). So which view is correct?

Slide28: 

Swinney, 1979 Rumour had it that for many years, the government building had been plagued with problems. The man was not surprised when he found several (spiders, roaches, and other) bugs (insects) in the corner of his room. ant sew spy In both contexts+ambiguous 1. Ant = spy, > sew 2. Ant > spy and sew Context: none (bugs or insects) or biasing (spiders, roaches, and other bugs/insects) Words: Ambiguous (bug) or unambiguous (insect)

Replicated by……..: 

Replicated by…….. Onifer & Swinney, 1981 with biased ambiguous words; Tanenhaus, et al., 1979, naming task - context-independent meaning fades after  200 msec; Seidenberg, et al., 1982, for a few 100 msec all meanings of an ambiguous word are activated regardless of semantic and syntactic constraints. Results support a modular view of sentence processing. Challenges by some, but generally accepted view that lexical ambiguity is resolved by an interaction b/w frequency and context

Syntactic context (I): 

Syntactic context (I) Tanenhaus et al. (1979) John began to watch ... look / time Syntactic context does not constrain multiple access

Syntactic context (II): 

Shillcock & Bard (1993) John decided that he would ... John decided that wood ... Syntactic context (II) plank / blank ??????????????

Lexical Ambiguity - Balanced and Biased Ambiguities: 

Lexical Ambiguity - Balanced and Biased Ambiguities The meanings of some ambiguous words (balanced) are roughly equally common For others (biased) one meaning is much more common than the other(s) Onifer & Swinney (1981) replicated Swinney’s (1979) results for biased ambiguities However, others have claimed that only balanced ambiguities show multiple access

Lexical Ambiguity - Types of Context: 

Lexical Ambiguity - Types of Context Contexts may be more or less strongly biased towards one or other meaning of an ambiguous word. Maybe selective access occurs only with strongly biasing contexts Contexts may be consistent with specific properties of one or other meaning of an ambiguous word (Tabossi)

Lexical Ambiguity - Types of Context: 

Lexical Ambiguity - Types of Context Relevant context may come either before or after ambiguous word The footballer asked “where is the ball?” “Where is the ball?” asked the footballer. A man in a tuxedo asked “where is the ball?” “Where is the ball?” asked the man in a tuxedo. Context that follows an ambiguous word is unlikely to affect the process of word identification

Lexical Ambiguity: Conclusions: 

Lexical Ambiguity: Conclusions The Swinney 1979 study provided striking evidence for multiple access Later studies have found that evidence for multiple access is clearest with balanced ambiguities and contexts that are not too constraining