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PSY 369: Psycholinguistics Language Production: Models of language production.

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1 PSY 369: Psycholinguistics Language Production: Models of language production

2 Announcements Exam 2 is coming up (Thurs, Apr. 1) An updated review sheet is on the syllabusreview sheet

3 Brief roadmap Language production research Today: Models of Language production Levelt’s model Dell’s model Some of the characteristics that distinguish the models Some of the research to investigate the models Tuesday: Conversation – Production and Comprehension come together

4 Nitty-gritty details of the model Message level Morphemic levelSyntactic level Phonemic level Articulation A central question: How do we do speak so quickly and fluently? Are the stages discrete or cascading? Discrete (modular): must complete before moving on Cascade: can get started as soon as some information is available Is there feedback? Top-down only (serial processing) Garrett, Levelt Bottom up too (interactive processing) Dell, Stemberger, McKay

5 Two different models TACTIC FRAMESLEXICAL NETWORK Dell (1986)Levelt (1989)

6 Levelt’s model Four broad stages: Conceptualization Deciding on the message (= meaning to express) Formulation Turning the message into linguistic representations Grammatical encoding (finding words and putting them together) Phonological encoding (finding sounds and putting them together) Articulation Speaking (or writing or signing) Monitoring (via the comprehension system)

7 Formalization on the Syntax side of the model Works in parallel with the lexicon side Levelt’s model Functional processing: Assignment of roles Direct object Grammatical subject

8 Formalization on the Syntax side of the model Works in parallel with the lexicon side Levelt’s model Positional processing: Build syntactic tree NP VP S VNP

9 Tip of tongue state when lemma is retrieved without word-form being retrieved Levelt’s model Involves lexical retrieval: Semantic/syntactic content (lemmas) Phonological content (lexemes or word-forms) Formalization on the Lexicon side of the model

10 has stripesis dangerous TIGER (X) Fem. Noun countable tigre /tigre/ /t//I//g/ Lexical concepts Lemmas Lexemes Phonemes Levelt’s model (see chpt 5, pg )

11 has stripesis dangerous TIGER (X) Levelt’s model: conceptual level Conceptual level is not decomposed one lexical concept node for “tiger” instead, conceptual links from “tiger” to “stripes”, etc. Fem. Noun tigre /tigre/ /t//I//g/ countable

12 TIGER (X) Fem. Noun tigre Levelt’s model: meaning & syntax First, lemma activation occurs This involves activating a lemma or lemmas corresponding to the concept thus, concept TIGER activates lemma “tiger” has stripesis dangerous /tigre/ /t//I//g/ countable

13 TIGER (X) Fem. Noun tigre Levelt’s model: meaning & syntax First, lemma activation occurs This involves activating a lemma or lemmas corresponding to the concept thus, concept TIGER activates lemma “tiger” But also involves activating other lemmas TIGER also activates LION (etc.) to some extent and LION activates lemma “lion” LION (X) lion /tigre/ /t//I//g/ has stripesis dangerous

14 TIGER (X) Fem. Noun tigre Levelt’s model: meaning & syntax First, lemma activation occurs Second, lemma selection occurs LION (X) lion Selection is different from activation Only one lemma is selected Probability of selecting the target lemma (“tiger”) ratio of that lemma’s activation to the total activation of all lemmas (“tiger”, “lion”, etc.) Hence competition between semantically related lemmas /tigre/ /t//I//g/ has stripesis dangerous

15 Morpho-phonological encoding (and beyond) The lemma is now converted into a phonological representation called “word-form” (or “lexeme”) If “tiger” lemma plus plural (and noun) are activated Leads to activation of morphemes tigre and s Other processes too Stress, phonological segments, phonetics, and finally articulation /tigre/ /t//I//g/ has stripesis dangerous Fem. Nouncountable tigre TIGER (X)

16 Modularity Later processes cannot affect earlier processes No feedback between the word-form (lexemes) layer and the grammatical (lemmas) layer Also, only one lemma activates a word form If “tiger” and “lion” lemmas are activated, they compete to produce a winner at the lemma stratum Only the “winner” activates a word form (selection) The word-forms for the “losers” aren’t accessed Model’s assumptions

17 Dell’s interactive account Dell (1986) presented the one of the best-known interactive accounts other similar accounts exist (e.g., Stemberger, McKay) Network organization 3 levels of representation Semantics (decomposed into features) Words and morphemes phonemes (sounds) These get selected and inserted into frames

18 Dell (1986) A moment in the production of: “Some swimmers sink” TACTIC FRAMESLEXICAL NETWORK Dell’s interactive account

19 as well as “downwards” information Interactive because information flows “upwards” Dell (1986) Cascading because processing at lower levels can start early TACTIC FRAMESLEXICAL NETWORK Dell’s interactive account

20 these send activation back to the word level, activating words containing these sounds (e.g., “log”, “dot”) to some extent Dell (1986) this activation is upwards (phonology to syntax) and wouldn’t occur in Levelt’s account FURRYBARKS doglog /a//g//d//l/ MAMMAL e.g., the semantic features mammal, barks, four-legs activate the word “dog” this activates the sounds /d/, /o/, /g/ dot /t/ Dell’s interactive account

21 Model comparisons Levelt’s Dell’s Similar representations Frames and slots Insertion of representations into the frames Serial Modular External monitor (comprehension) Interactive Cascaded Similarities Differences

22 Testing Models of language production Experimental investigations of some of these issues Time course - cascading vs serial Picture word interference Separation of syntax and semantics Subject verb agreement Abstract syntax vs surface form Syntactic priming

23 tiger Picture-word interference task Task: Participants name basic objects as quickly as possible Distractor words are embedded in the object (or presented aloud) Participants are instructed to ignore these words Experimental tests

24 Semantic interference Meaning related words can slow down naming the picture e.g., the word TIGER in a picture of a LION Experimental tests tiger Picture-word interference task

25 Form-related words can speed up processing e.g., the word liar in a picture of a LION liar Experimental tests Picture-word interference task Semantic interference

26 Experiments manipulate timing: picture and word can be presented simultaneously liar time liar or one can slightly precede the other We draw inferences about time-course of processing liar Experimental tests

27 SOA (Stimulus onset asynchrony) manipulation -150 ms (word …150 ms … picture) 0 ms (i.e., synchronous presentation) +150 ms (picture …150ms …word) Schriefers, Meyer, and Levelt (1990) DOT phonologically related CAT semantically related SHIP unrelated word Evidence against interactivity

28 Schriefers, Meyer, and Levelt (1990) DOT phonologically related CAT semantically related SHIP unrelated word Early Only Semantic effects Late Only Phonological effects Evidence against interactivity

29 Schriefers, Meyer, and Levelt (1990) Also looked for any evidence of a mediated priming effect hat dog DOG (X)CAT (X) cat /cat//hat/ /t//a//k//h/ Found no evidence for it Evidence against interactivity

30 Early semantic inhibition Late phonological facilitation Fits with the assumption that semantic processing precedes phonological processing No overlap suggests two discrete stages in production an interactive account might find semantic and phonological effects at the same time Interpretation

31 Mixed errors Both semantic and phonological relationship to target word Target = “cat” semantic error = “dog” phonological error = “hat” mixed error = “rat” Occur more often than predicted by modular models if you can go wrong at either stage, it would only be by chance that an error would be mixed Evidence for interactivity

32 Dell’s explanation The process of making an error The semantic features of dog activate “cat” Some features (e.g., animate, mammalian) activate “rat” as well “cat” then activates the sounds /k/, /ae/, /t/ /ae/ and /t/ activate “rat” by feedback This confluence of activation leads to increased tendency for “rat” to be uttered Also explains the tendency for phonological errors to be real words (lexical bias effect) Sounds can only feed back to words (non-words not represented) so only words can feedback to sound level Evidence for interactivity

33 A number of recent experimental findings appear to support interaction under some circumstances (or at least cascading models) Damian & Martin (1999) Cutting & Ferreira (1999) Peterson & Savoy (1998) Evidence for interactivity

34 Damian and Martin (1999) Picture-Word interference The critical difference: the addition of a “semantic and phonological” condition Picture of Apple peach (semantically related) apathy (phonologically related) apricot (sem & phono related) couch (unrelated) peach Evidence for interactivity

35 Damian & Martin (1999) early semantic inhibition couch (unrelated) peach (semantically related) apathy (phonologically related) apricot (sem & phono related) Evidence for interactivity

36 Damian & Martin (1999) late phonological facilitation (0 and ms) early semantic inhibition couch (unrelated) peach (semantically related) apathy (phonologically related) apricot (sem & phono related) Evidence for interactivity

37 Damian & Martin (1999) late phonological facilitation (0 and ms) Shows overlap, unlike Schriefers et al. early semantic inhibition couch (unrelated) peach (semantically related) apathy (phonologically related) apricot (sem & phono related) Evidence for interactivity

38 Cutting and Ferreira (1999) Picture-Word interference The critical difference: Used homophone pictures Related distractors could be to the depicted meaning or alternative meaning “game” “dance” “hammer” (unrelated) Only tested -150 SOA dance Evidence for interactivity

39 ball BALL (X) ball /ball/ DANCE (X) dance GAME (X) game Cascading Prediction:danceball/ball/ Cutting and Ferreira (1999) Evidence for interactivity

40 Early semantic inhibition Cutting and Ferreira (1999) Evidence for interactivity

41 Early Facilitation from a phonologically mediated distractor Early semantic inhibition Cutting and Ferreira (1999) Evidence of cascading information flow (both semantic and phonological information at early SOA) Evidence for interactivity

42 Peterson & Savoy (1998) Slightly different task Prepare to name the picture If “?” comes up name it ? Evidence for interactivity

43 Peterson & Savoy (1998) Slightly different task Prepare to name the picture If “?” comes up name it If a word comes up instead, name the word liar Manipulate Word/picture relationship SOA Evidence for interactivity

44 Peterson & Savoy (1998) Used pictures with two synonymous names Used words that were phonologically related to the non dominant name of the picture sofacouch DominantSubordinat e soda Evidence for interactivity

45 Peterson & Savoy Found evidence for phonological activation of near synonyms: Participants slower to say distractor soda than unrelated distractor when naming couch Soda is related to non-selected sofa Remember that Levelt et al. assume that only one lemma can be selected and hence activate a phonological form Levelt et al’s explanation: Could be erroneous selection of two lemmas? Evidence for interactivity

46 Can the two-stage account be saved? Evidence for interaction is hard to reconcile with the Levelt account However, most attempts are likely to revolve around the monitor Basically, people sometimes notice a problem and screen it out Levelt argues that evidence for interaction really involves “special cases”, not directly related to normal processing

47 Levelt et al.’s theory of word production: Strictly modular lexical access Syntactic processing precedes phonological processing Dell’s interactive account: Interaction between syntactic and phonological processing Experimental evidence is equivocal, but increasing evidence that more than one lemma may activate associated word-form Overall summary

48 Conversational interaction “the horse raced past the barn” Conversation is more than just two side-by- side monologues. “the kids swam across the river”

49 Conversational interaction “The horse raced past the barn” Conversation is a specialized form of social interaction, with rules and organization. “Really? Why would it do that?”

50 Conversation Herb Clark (1996) Joint action People acting in coordination with one another doing the tango driving a car with a pedestrian crossing the street The participants don’t always do similar things Autonomous actions Things that you do by yourself Participatory actions Individual acts only done as parts of joint actions

51 Conversation Herb Clark (1996) Speaking and listening Traditionally treated as autonomous actions Contributing to the tradition of studying language comprehension and production separately Clark proposed that they should be treated as participatory actions

52 Conversation Herb Clark (1996) Speaking and listening Component actions in production and comprehension come in pairs SpeakingListening A vocalizes sounds for B A formalizes utterances for B A means something for B B attends to A’s vocalizations B identifies A’s utterances B understands A’s meaning The actions of one participant depend on the actions of the other

53 Conversation Herb Clark (1996) Face-to-face conversation - the basic setting Features Co-presence Visibility Audibility Instantaneity Evanescence Recordlessness Simultaneity Extemporaneity Self-determination Self-expression ImmediacyMediumControl Other settings may lack some of these features e.g., telephone conversations take away co-presence and visibility, which may change language use

54 Meaning and understanding ABBOTT: Super Duper computer store. Can I help you? COSTELLO: Thanks. I'm setting up an office in my den, and I'm thinking about buying a computer. ABBOTT: Mac? COSTELLO: No, the name is Lou. ABBOTT: Your computer? COSTELLO: I don't own a computer. I want to buy one. ABBOTT: Mac? COSTELLO: I told you, my name is Lou. ABBOTT: What about Windows? COSTELLO: Why? Will it get stuffy in here? ABBOTT: Do you want a computer with windows? COSTELLO: I don't know. What will I see when I look in the windows? ABBOTT: Wallpaper. COSTELLO: Never mind the windows. I need a computer and software. ABBOTT: Software for windows? COSTELLO: No. On the computer! I need something I can use to write proposals, track expenses and run my business. What have you got? ABBOTT: Office.

55 Meaning and understanding COSTELLO: Yeah, for my office. Can you recommend anything? ABBOTT: I just did. COSTELLO: You just did what? ABBOTT: Recommend something. COSTELLO: You recommended something? ABBOTT: Yes. COSTELLO: For my office? ABBOTT: Yes. COSTELLO: OK, what did you recommend for my office? ABBOTT: Office. COSTELLO: Yes, for my office! ABBOTT: I recommend office with windows. COSTELLO: I already have an office and it has windows!OK, lets just say, I'm sitting at my computer and I want to type a proposal. What do I need? ABBOTT: Word. COSTELLO: What word? ABBOTT: Word in Office. COSTELLO: The only word in office is office. ABBOTT: The Word in Office for Windows.

56 Meaning and understanding COSTELLO: Which word in office for windows? ABBOTT: The Word you get when you click the blue "W.” COSTELLO: I'm going to click your blue "w" if you don't start with some straight answers. OK, forget that. Can I watch movies on the Internet? ABBOTT: Yes, you want Real One. COSTELLO: Maybe a real one, maybe a cartoon. What I watch is none of your business. Just tell me what I need! ABBOTT: Real One. COSTELLO: If it ユ s a long movie I also want to see reel 2, 3 and 4. Can I watch them? ABBOTT: Of course. COSTELLO: Great, with what? ABBOTT: Real One. COSTELLO; OK, I'm at my computer and I want to watch a movie.What do I do? ABBOTT: You click the blue "1.” COSTELLO: I click the blue one what? ABBOTT: The blue "1.” COSTELLO: Is that different from the blue "W"? ABBOTT: The blue 1 is Real One and the blue W is Word. COSTELLO: What word?

57 Meaning and understanding ABBOTT: The Word in Office for Windows. COSTELLO: But there are three words in "office for windows"! ABBOTT: No, just one. But it ユ s the most popular Word in the world. COSTELLO: It is? ABBOTT: Yes, but to be fair, there aren't many other Words left. It pretty much wiped out all the other Words. COSTELLO: And that word is real one? ABBOTT: Real One has nothing to do with Word. Real One isn't even Part of Office. COSTELLO: Stop! Don't start that again. What about financial bookkeeping you have anything I can track my money with? ABBOTT: Money. COSTELLO: That's right. What do you have? ABBOTT: Money. COSTELLO: I need money to track my money? ABBOTT: It comes bundled with your computer. COSTELLO: What's bundled to my computer? ABBOTT: Money.

58 Meaning and understanding COSTELLO: Money comes with my computer? ABBOTT: Yes. No extra charge. COSTELLO: I get a bundle of money with my computer? How much? ABBOTT: One copy. COSTELLO: Isn't it illegal to copy money? ABBOTT: Microsoft gave us a license to copy money. COSTELLO: They can give you a license to copy money? ABBOTT: Why not? THEY OWN IT! (LATER) COSTELLO: How do I turn my computer off?? ABBOTT: Click on "START".

59 Meaning and understanding Common ground Knowledge, beliefs and suppositions that the participants believe that they share Members of cultural communities Shared experiences What has taken place already in the conversation Common ground is necessary to coordinate speaker’s meaning with listener’s understanding

60 Conversations are purposive and unplanned Typically you can’t plan exactly what you’re going to say because it depends on another participant Conversations look planned only in retrospect Conversations have a fairly stable structure Structure of a conversation

61 Joe: (places a phone call) Kevin: Miss Pink’s office - hello Joe: hello, is Miss Pink in Kevin: well, she’s in, but she’s engaged at the moment, who is it? Joe: Oh it’s Professors Worth’s secretary, from Pan-American college Kevin: m, Joe: Could you give her a message “for me” Kevin: “certainly” Joe: u’m Professor Worth said that, if Miss Pink runs into difficulties,.. On Monday afternoon,.. With the standing subcommittee,.. Over the item on Miss Panoff, … Structure of a conversation Kevin: Miss Panoff? Joe: Yes, that Professor Worth would be with Mr Miles all afternoon,.. So she only had to go round and collect him if she needed him, … Kevin: ah, … thank you very much indeed, Joe: right Kevin: Panoff, right “you” are Joe: right Kevin: I’ll tell her, Joe: thank you Kevin: bye bye Joe: bye

62 Structure of a conversation Action sequences: smaller joint projects to fulfill a goal Adjacency pairs Opening the conversation Kevin: Miss Pink’s office - hello Joe: hello,.. Exchanging information about Pink Joe:.., is Miss Pink in Kevin: well, she’s in, but she’s engaged at the moment…

63 Structure of a conversation Action sequences: smaller joint projects to fulfill a goal Adjacency pairs Exchanging the message from Worth Joe: u’m Professor Worth said that, if Miss Pink runs into difficulties,.. On Monday afternoon,.. With the standing subcommittee,.. Over the item on Miss Panoff, … Closing the conversation Kevin: I’ll tell her, Joe: thank you Kevin: bye bye Joe: bye

64 Opening conversations Need to pick who starts Turn taking is typically not decided upon in advance Potentially a lot of ways to open, but we typically restrict our openings to a few ways Address another Request information Offer information Use a stereotyped expression or topic

65 Opening conversations Has to resolve: The entry time Is now the time to converse? The participants Who is talking to whom? Their roles What is level of participation in the conversation? The official business What is the conversation about?

66 EavesdropperAll listeners Identifying participants Conversation often takes place in situations that involve various types of participants and non-participants Bystander Side participants All participants Speaker Addressee

67 Taking turns Typically conversations don’t involve two (or more) people talking at the same time Individual styles of turn-taking vary widely Length of a turn is a fairly stable characteristic within a given individual’s conversational interactions Standard signals indicate a change in turn: a head nod, a glance, a questioning tone

68 Taking turns Typically conversations don’t involve two (or more) people talking at the same time Three implicit rules (Sacks et al, 1974) Rule 1: Current speakers selects next speaker Rule 2: Self-selection: if rule 1 isn’t used, then next speaker can select themselves Rule 3: current speaker may continue (or not) These principles are ordered in terms of priority The first is the most important, and the last is the least important Just try violating them in an actual conversation (but debrief later!)

69 Taking turns Typically conversations don’t involve two (or more) people talking at the same time Use of non-verbal cues Drop of pitch Drawl on final syllable Termination of hand signals Drop in loudness Completion of a grammatical clause Use of stereotyped phrase “you know”

70 Negotiating topics Keep the discourse relevant to the topic (remember Grice’s maxims) Coherence again Earlier we looked at coherence within a speaker, now we consider it across multiple speakers Must use statements to signal topic shifts

71 Closing conversations Closing statements Must exit from the last topic, mutually agree to close the conversation, and coordinate the disengagement signal the end of conversation (or topic) “okay” Justifying why conversation should end “I gotta go” Reference to potential future conversation “later dude”

72 Summary “People use language for doing things with each other, and their use of language is itself a joint action.” Clark (1996, pg387) Conversation is structured But, that structure depends on more than one individual Models of language use (production and comprehension) need to be developed within this perspective


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