Sentence Production
• So far, we’ve seen that:
– Comprehending or producing a syntactic structure makes it more
likely you’ll produce that same structure in describing a picture
• Even when no lexical overlap beyond determiners
• Effect just as strong if only read prime silently
• So, a structure itself is primable, showing that it has some kind of
representation in the production system that’s separate from the
words in it
– Priming meaning of words to be used in picture description
makes you more likely to use structure that puts primed words
earlier in sentence
• So word meaning availability influences structure choices
• Priming word form has opposite effect, probably because form
priming makes a competing form available & that makes it harder to
produce correct form
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Subject-Verb Agreement in
Sentence Production
• When another noun comes between the Subject
Noun & the Verb in English sentences
– If number of Local Noun differs from that of Subject Noun
– It sometimes leads to agreement errors called “attraction
errors”’
– Most likely when Subject Noun singular & Local Noun plural
• The only generalization I would dare to make about our customers
are that they’re pierced.
– Shows that production system sometimes loses track of subject
while preparing and producing verb
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• Bock & Cutting (1992) used plural attraction
errors to investigate sentence production
– If Local Noun intervening between Subject Noun
& Verb is part of same clause as they are, will it
be more “attractive” to Verb?
The editor of the history books …
vs
The editor [who rejected the books] …
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Results
- Replicated earlier
findings that plural
Local Nouns much
more attractive
- And showed that’s
especially true if it’s
in same clause
- Suggests clauses kept
somewhat separate
from one another in
production
(PP or RC)
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Sound Errors in Words
• Error outcomes are almost always “legal” for the
language
– e.g., English doesn’t have any words beginning with
vl, & English
– speakers never make slips like
very flighty > vlery fighty
• Furthermore, errors that result in saying real
words are more likely than you’d expect by
chance
– barn door > darn bore is more likely than
– dart board > bart doard
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• What does “expect by chance” mean here?
– For an error to result in saying wrong real words, there
must be other words that are similar enough to the
intended words
– i.e., to provide the opportunity for a word outcome
– e.g., barn door > darn bore
–
rotten cat > cotton rat
• When you estimate how often such opportunities
are likely to arise,
– Given the vocabulary of the language
– Errors that result in words happen more often than they
should, if they were due purely to chance
• = Lexical Bias
– It’s not that word outcomes are overall more likely than
non-word outcomes
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Top-Down Processing Again
• But maybe the lexical bias is on listener’s side???
– Maybe we tend to hear errors as words if at all possible,
– Even when the speaker actually produced a non-word
• Remember the phoneme-restoration effect?
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A Technique for Inducing Sound Errors
• Present a series of word pairs
–
–
–
–
–
ball doze
bash door
bean deck
bell dark
darn bore
Interference Pairs – Read silently
Target Pair – Say aloud fast
• Can't predict when you'll have to say a pair aloud, so prepare on all trials
• Possible responses:
–
–
–
–
darn bore
barn door
barn bore
darn door
No error
Exchange
Anticipation
Perseveration
• Control the opportunities for word-producing errors
– Record the responses & analyze them carefully
– Exchanges on about 30% of the critical trials
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Some Results
• Exchanges resulting in word outcomes more likely
ball doze
bash door
bean deck
bell dog
darn bore
big dutch
bang dark
bill deal
bark doll
dart board
– barn door
More likely
bart doard
–
–
–
–
–
Less likely
• Confirms perceived pattern in spontaneous errors
– Rules out Listener Bias as full explanation of Lexical Bias
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Word Production Models
• All current theories of word production:
– Explain why errors are usually similar in either sound or
meaning to the intended target
– Have 2 stages
1. Retrieve lemma
2. Retrieve its sounds
• But they differ in:
– How separate & independent the 2 stages are
– Their mechanism for producing similarity effects
• Garrett's model vs Dell's model
= Modularity vs Interaction again!
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Garrett’s Model of Word Production
• Lexicon organized into 2 “files”
– Meaning File
• Contains lemmas + pointers to locations in Sound File
• Organized by meaning
– Sound File
• Contains word pronunciations
• Organized by sound
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• To say a word in Garrett’s model:
– Intended meaning
– Look in Meaning File and find lemma CAT
– Use CAT's pointer to find its pronunciation /kaet/ in
Sound File
• Once you go into Sound File, you’re done selecting
which word to say (i.e., which lemma to choose)
– So what you find in Sound File cannot affect lemma choice
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• In Garrett’s model:
– Whole-word errors come from over- or undershoot in Meaning File
• In right neighborhood, so find something similar in
meaning
– Sound errors come from over- or under-shoot in
Sound File
• In right neighborhood, so error should sound similar
/kaeb/
• Garrett’s model was intentionally built with independent
meaning & sound stages
– Specifically to explain why errors seem to be related in one or
the other way but not both
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Mixed Errors
= Errors that are similar in both meaning and sound to intended
word
– CAT > rat
– ORCHESTRA > sympathy
• In Garrett’s model, there’s no way for both factors to interact in
causing the error
– Something that looks like a Mixed Error is really just meaningrelated error or just sound-related & it’s a coincidence that it’s
similar in the other way, too ( CAT > rat )
– Or there were 2 independent errors, 1 at each stage
• ORCHESTRA > SYMPHONY
• SYMPHONY > sympathy
• Mixed Errors rare because coincidences & double errors are rare
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• Dell disagrees:
– English vocabulary provides very few opportunities for
Mixed Errors
– Pairs of words that are similar in both sound and meaning
like cat & rat or orchestra & sympathy are very rare
• When you take that into account, Mixed Errors
– Happen more often than you would expect by chance
• Dell’s model was built to explain why errors tend to
be related in
– Either sound or meaning or both
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Localist
^
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Garrett vs Dell
• Meaning- or Sound-related errors:
– Both models explain these
• Mixed errors:
– Garrett's model explains why these are unlikely
– While Dell's model explains why they're especially likely
– They disagree about the data
• Legal outcome bias:
– Requires an extra process in Garrett's model
•
•
•
•
Pre-articulatory Editor (usually unconscious)
Very likely to notice & prevent illegal sound combinations
Fairly likely to notice & prevent non-words
Less likely to notice an unintended word
– Natural consequence of architecture of Dell's model
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Evidence for an Editor
• Motley, Camden, & Baars (1982)
–
–
–
–
shot home
shame hear
show hand
hit shed
• People less likely to make errors resulting in taboo words
• Said unaware of possibility of saying taboo word
– But increased Galvanic Skin Response (GSR) on trials
where there was an opportunity to say a taboo word
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An Example of Testing Dell’s Model
• Lexical Bias caused by activation reverberating back & forth
– Takes time
• Prediction:
– Errors should be less likely to be words as people talk faster
– Would be virtually impossible to observe with spontaneous errors
– The prediction is confirmed when errors are elicited in the lab
• So, testing the model’s predictions led to the discovery of a new
fact about speech errors
• Model implemented as computer program (= simulation) that
“talks”
– Predictions derived from model
– Tested in studies with people
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