1. 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 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

2. 12/01/10Psyc / Ling / Comm 525 Fall10 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

3. 12/01/10Psyc / 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] …

4. 12/01/10Psyc / 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)

5. 12/01/10Psyc / 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

6. 12/01/10Psyc / 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

7. 12/01/10Psyc / 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?

8. 12/01/10Psyc / Ling / Comm 525 Fall10 Present a series of word pairs –ball doze –bash door Interference 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 boreNo 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 A Technique for Inducing Sound Errors

9. 12/01/10Psyc / Ling / Comm 525 Fall10 Some Results Exchanges resulting in word outcomes more likely –ball dozebig dutch –bash doorbang dark –bean deckbill deal –bell dogbark doll –darn boredart board –barn doorbart doard More likelyLess likely Confirms perceived pattern in spontaneous errors –Rules out Listener Bias as full explanation of Lexical Bias

10. 12/01/10Psyc / 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 model vs Dell's model = Modularity vs Interaction again!

11. 12/01/10Psyc / 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

12. 12/01/10Psyc / 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

13. 12/01/10Psyc / 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

14. 12/01/10Psyc / 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

15. 12/01/10Psyc / 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

16. 12/01/10Psyc / Ling / Comm 525 Fall10 Localist ^

17. 12/01/10Psyc / Ling / Comm 525 Fall10

18. 12/01/10Psyc / Ling / Comm 525 Fall10

19. 12/01/10Psyc / Ling / Comm 525 Fall10

20. 12/01/10Psyc / Ling / Comm 525 Fall10

21. 12/01/10Psyc / Ling / Comm 525 Fall10

22. 12/01/10Psyc / Ling / Comm 525 Fall10

23. 12/01/10Psyc / Ling / Comm 525 Fall10

24. 12/01/10Psyc / Ling / Comm 525 Fall10

25. 12/01/10Psyc / Ling / Comm 525 Fall10

26. 12/01/10Psyc / 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

27. 12/01/10Psyc / 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

28. 12/01/10Psyc / Ling / Comm 525 Fall10

29. 12/01/10Psyc / 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