BCS 260: Psychology of Language
Dec. 8, 2000
Models of Word Recognition
I. Stages of Spoken Word Recognition
A Initial contact
á First contact with the lexicon after hearing some speech
á Precise form of contact: depends on theories of speech recognition
1. LAFS (lexical access from spectra, Klatt): spectrographic
2. motor theory (analysis-by-synthesis): articulatory gestures
3. phonemic
á Causes certain lexical representations to ÒactivateÓ
B. Lexical selection
á Activation continues to accumulate (or, die off)
á Type of activation depends on the model being considered
1. all-or-nothing activation
2. better fit leads to higher degrees of activation
3. may be affected by properties of the words, such as frequency
á Pool of possible candidates being considered therefore changes over time
C. Word recognition
á End point of the selection phase
á Is reached when only a single candidate remains in the pool
á ÒUniqueness pointÓÑpoint in a word at which is can be uniquely identified
1. null scenario: word recognition occurs at uniqueness point
2. heavily-biasing contexts: word may be identified before uniqueness point
3. in some models, certain words might not be recognized until after the
uniqueness point
D. Lexical Access & Integration
á Lexical access: Point at which lexically-stored information (phonological,
morphological, semantic, etc.) becomes available
á Integration: Working the meaning of the word into the overall meaning of the
sentence
II. Models of Word Recognition
A. Context and Word Recognition
á Context: Anything other than whatÕs actually present in the immediate speech
stream; includes prior sensory input (previous context) as well as higher
knowledge sources (semantic, syntactic, etc.).
BCS 260: Psychology of Language
Dec. 8, 2000
1. Autonomous models: Context can affect integration of a wordÕs meaning,
but can have no effect on stages prior to word recognition. (bottom-up)
2. Interactive models: Context can affect the stages prior to word
recognition (top-down)
á Non-structural context: contextual information from within the same level of
processing; does not require top-down processing flow from higher levels. Ex:
semantic priming of ÒuniversityÓ by Òprofessor.Ó
á Structural context: contextual information from a higher level of processing;
requires top-down processing flow. Ex: (speech recognition) phoneme
restoration; (word recognition) next word in ÒI saw the ÉÓ must be an adj. or
noun (does this affect word recognition?)
1. Semantic: Words appropriate for a given semantic context are recognized
(i.e., responded to) more quickly than inappropriate or neutral words.
Question: because of quicker processing, or because of post-perceptual
factors?
2. Interpretive: Refers to use of pragmatic, discourse, or real-world knowledge
in word recognition. ÒShe went to the polls and cast a vote for N_____.Ó
+ Problems with context-based arguments: Finding an experimental paradigm that
can differentiate between ÒrealÓ context effects and post-perceptual effects.
B. Cohort model (Marslen-Wilson)
á Cohort: (gen.) A group of things that share some characteristic (like age)
(psycholinguistics) A group of words that are in a common candidate set
during lexical selection; usu. defined in a L to R manner
á Ex: rabbit [U4E,W]: Cohort 1: all words beginning with [r]
Cohort 2: all words beginning with [r4]
Cohort 3: all words beginning with [r4E]
Cohort 4: all words beginning with [r4E,]
Cohort 5: all words beginning with [r4E,W]
á No. of Cohorts determined by the words uniqueness point
á Evidence provided in support of Cohort:
1. Shadowing: Speech to shadow had mispronunciations
Mispronunciations varied according to:
Number of features changes (= amount of distortion)
Location (1st, 2nd, 3rd syllable) (= number of candidates)
Contextual clues to the correct word (appropriateness)
Speakers are supposed to mimic the errors
Fluent restorations about 50% of the time
Fluent restorations more likely with slight distortion, with words highly
predictable from context, and when distortion is in final syllable
BCS 260: Psychology of Language
Dec. 8, 2000
2. Mispronunciation monitoring
higher accuracy rates for word-initial mispronunciations
3. Gating: subjects presented with increasingly more and more of a word
Words without context: need about 333 ms. to identify
Word in context: need about 199 ms. to identify
Claimed to show that context does not affect candidate generation,
since candidates are freely generated up till the point of recognition
C. TRACE model (McClelland, Elman, Rumelhart)
á Structured as a connectionist model: a set of interconnected nodes
á 1st layer: feature nodes; 2nd layer: phoneme nodes: 3rd layer: word nodes
á Nodes in the same layer: inhibitory connections (competition)
á Nodes in different layers: bidirectional connections (bottom-up and top-down)
á Advantages of the TRACE model:
Does not require knowing where the left edge of the word is
Can ÒrecoverÓ if a given segment (even the first one) is missed
Bidirectional connections account for phoneme restoration & other
contextual effects on speech recognition
á Possible disadvantage: Predicts a lot of top-down information flow; should
permeate all levels of word recognition.
á Current research: Is this a disadvantage, or are there, in fact, lots of context
effects in all levels of word recognition?
BCS 260: Psychology of Language
Dec. 8, 2000
Study Questions: Final Exam
Six of the following questions will be chosen to appear on the question sheet for the final
exam. Each student will choose three questions from that set to answer. In addition,
there will be a set of review questions from sections 1 and 2Ñthese questions will be
drawn from the previous study question sheets. Paper will be provided during the
midterm, students need bring only some writing implement.
1. Explain the concept of constituency, and why a grammar that employs constituency is
superior in accounting for syntactic competence than a grammar without
constituency. Is there any evidence for constituency in performance?
2. Explain what the idea of transformations, and how they relate to the idea of dualstructure
syntax. Explain why we use transformations, instead of just making more
phrase structure rules. What aspects of a personÕs syntactic competence are best
explained using the concept of transformations?
3. What is the derivational theory of complexity, and what sorts of predictions are made
by this theory? Review some of the experimental findings that are pertinent to this
theory, and explain why they do/donÕt support it.
4. What does the term Ògarden-path phenomenonÓ refer to, and how does it relate to the
idea of syntactic ambiguity? Explain the link between garden path phenomena and
parsing heuristics. Give examples of at least three heuristics, providing cases that
exemplify how the heuristic is supposed to work.
5. How does the idea of long-distance dependency relate to the idea of transformations?
Is there any evidence that long-distance dependencies are psychologically real? Give
both clinical and experimental evidence on this topic.
6. Explain the difference between serial and parallel search models of syntactic parsing,
and go over whether there is evidence from conceptual/pragmatic and discourse
context that favors one or the other.
7. How do the ideas of segmentation and invariance relate to speech recognition?
8. Many psychologists debate whether speech processing is strictly bottom-up, or
whether it also contains the possibility to top-down effects. Explain the difference
between top-down and bottom-up processing. How do contextual effects on speech
perception fit into this debate? Give at least two examples of context effects on
speech recognition, explaining each.
9. Give an overview of the Cohort model of word recognition. Provide at leave 2 pieces
of experimental evidence that have been offerred in support of this model. Be sure to
explain how the experimental findings support the theory.
10. Give an overview of the TRACE model of word recogntion. Explain at least two of
the advantages claimed to follow from the TRACE model. How does this model fit
in to the debate between autonomous and interactive models for word recognition?