12/01/10Psyc / Ling / Comm 525 Fall10 Sentence Production So far, we’ve seen that: –Comprehending or producing a syntactic structure makes it more likely

12/01/10 Psyc / Ling / Comm 525 Fall10

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


Subject-Verb Agreement inSentence 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


• 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] …


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)


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


• 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


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?


• Present a series of word pairs– ball doze– bash door Interference Pairs – Read silently– bean deck– bell dark– darn bore 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 No error– barn door Exchange– barn bore Anticipation– darn door Perseveration

• Control the opportunities for word-producing errors– Record the responses & analyze them carefully– Exchanges on about 30% of the critical trials

A Technique for Inducing Sound Errors


Some Results

• Exchanges resulting in word outcomes more likely– ball doze big dutch– bash door bang dark– bean deck bill deal– bell dog bark doll– darn bore dart board

– barn door bart doard More likely Less likely

• Confirms perceived pattern in spontaneous errors– Rules out Listener Bias as full explanation of Lexical

Bias


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 lemma2. 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!


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


• 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


• In Garrett’s model:– Whole-word errors come from over- or

under-shoot 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


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

meaning-related 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


• 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


Localist

^











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


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



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

Documents

12/01/10Psyc / Ling / Comm 525 Fall10 Sentence Production So far, we’ve seen that: –Comprehending or producing a syntactic structure makes it more likely