Sentence Production

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 Psyc / Ling / Comm 525 Fall10

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 Iwould dare to make about our customersare that they’re pierced. • Shows that production system sometimes loses track of subject while preparing and producing verb Psyc / Ling / Comm 525 Fall10

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] … Psyc / Ling / Comm 525 Fall10

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) Psyc / Ling / Comm 525 Fall10

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 Psyc / Ling / Comm 525 Fall10

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 Psyc / Ling / Comm 525 Fall10

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? Psyc / Ling / Comm 525 Fall10

A Technique for Inducing Sound Errors • Present a series of word pairs • ball doze • bash doorInterference Pairs – Read silently • bean deck • bell dark • darn boreTarget 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 doorExchange • barn boreAnticipation • darn doorPerseveration • Control the opportunities for word-producing errors • Record the responses & analyze them carefully • Exchanges on about 30% of the critical trials Psyc / Ling / Comm 525 Fall10

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 likelyLess likely • Confirms perceived pattern in spontaneous errors • Rules out Listener Bias as full explanation of Lexical Bias Psyc / Ling / Comm 525 Fall10

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 modelvsDell's model = ModularityvsInteraction again! Psyc / Ling / Comm 525 Fall10

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 Psyc / Ling / Comm 525 Fall10

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 Psyc / Ling / Comm 525 Fall10

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 Psyc / Ling / Comm 525 Fall10

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 Psyc / Ling / Comm 525 Fall10

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 Psyc / Ling / Comm 525 Fall10

Localist ^ Psyc / Ling / Comm 525 Fall10

Psyc / Ling / Comm 525 Fall10

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 Psyc / Ling / Comm 525 Fall10

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 Psyc / Ling / Comm 525 Fall10

Psyc / Ling / Comm 525 Fall10

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 Psyc / Ling / Comm 525 Fall10

Sentence Production