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PSY 369: Psycholinguistics

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1 PSY 369: Psycholinguistics
Language Production: Experimentally elicited speech errors & Models of language production

2 Announcements Exam 2 is coming up (Thurs, Apr. 1)
An updated review sheet is on the syllabus Speech error collections due on Thursday (if you are doing that homework assignment)

3 Brief summary Language production research
Today: Brief review of what errors tell us about correct speech Observational and experimental approaches Finishing up experimentally elicited speech errors A General Framework for language production

4 Speech errors Collecting and analyzing speech errors?
The psycholinguistic approach Observational approaches Long fruitful history Some problems Experimental approach SLIP technique Look for regularities Lexical bias Grammaticality constraint Infer underlying processes from these regularities Many units may be involved Planning in advance

5 Experimental speech errors
Can we examine speech errors in under more controlled conditions? SLIP technique: speech error elicitation technique Motley and Baars (1976) Task: Say the words silently as quickly as you can Say them aloud if you hear a ring mangy bears mashed buns angry insect ornery fly bad mug

6 Experimental Freudian slips?
Motley & Baars (1979) Hypothesis: Spoonerisms more likely when the resulting content is congruous with the situational context.

7 Experimental Freudian slips?
Motley & Baars (1979) Method: 90 males, same procedure previously used by Motley, 1980 (SLIP). 3 Context (Set) Conditions: “Electricity” - expecting to get shocked “Sex” - researcher provocatively attired female Neutral Word pairs Same word pairs in all conditions Targets were non-words (e.g. goxi furl  foxy girl) preceded by 3 phonologically biasing word pairs not semantically related Half of the potential resulting errors were sexually related (S), some were electrically related (E) (S) bine foddy -> “fine body” (E) shad bock -> “bad shock”

8 Experimental Freudian slips?

9 Experimental Freudian slips?
car tires

10 Experimental Freudian slips?
cat toys

11 Experimental Freudian slips?
can tops

12 Experimental Freudian slips?
cup trays

13 Experimental Freudian slips?
“cool tits” tool kits

14 Experimental Freudian slips?
Motley & Baars (1979) Results (number of errors, by type): Electricity set: 69 E, 31 S Sex set: E, 76 S Neutral set: 44 E, 41 S Conclusion: subjects’ speech systems are sensitive to semantic influences from their situational cognitive set.

15 Experimental Freudian slips?
Follow-up: Motley (1980) Hypothesis: subjects with high levels of sex anxiety will make more “sex” spoonerisms than those with low sex anxiety. Method: 36 males selected on the basis of high, medium, & low sex anxiety (Mosher Sex-Guilt Inventory). SLIP task same as previous experiment but with 2 additional Sex targets and 9 Neutral targets.

16 Experimental Freudian slips?
Follow-up: Motley (1980) Results: looked at difference scores (Sex - Neutral) High sex anxiety > medium > low. Overall: Sex spoonerisms > Neutral spoonerisms. Conclusion: appears to support Freud’s view of sexual anxiety being revealed in Slips of the Tongue BUT: the experimenters (Baars and Motley) went on to show that any type of anxiety, not just sexual produced similar results. SO: anxiety was at play but it was more general, so the priming was more global.

17 Experimental speech errors
Some basic findings Many of the same effects found in naturalistic errors are found in experimental errors Lexical Bias effect: error frequency affected by whether the error results in real words or non-words (Motley & Baars, 1976) Motley, (1980a) Semantic effects on phonological exchange speech errors Can isolate particular factors and get a lot of errors This technique has been found to elicit 30% of predicted speech errors. (Motley & Baars, 1976) Motley, (1980b) Situational contexts can affect frequency and type of error

18 Jane threw the ball to Bill
From thought to speech Jane threw the ball to Bill General Model of Language Production What do speech errors suggest? Fromkin (1971) Garrett (1975) (And experiments too) Productivity on multiple levels, phonological, morphemic, syntactic A lot of the utterance is planned prior to onset of the utterance

19 From thought to speech General Model of Language Production
Message level General Model of Language Production Ordered sequence of independent planning units Four levels of processing are typically proposed Typically they are ordered this way (but there is debate about the independence of the different levels) Note the similarity to models of comprehension Syntactic level Morphemic level Phonemic level Articulation

20 From thought to speech Propositions to be communicated
Message level Propositions to be communicated Syntactic level Selection and organization of lexical items Morphemic level Morphologically complex words are constructed Phonemic level Articulation Sound structure of each word is built

21 From thought to speech Propositions to be communicated Message level
Not a lot known about this step Typically thought to be shared with comprehension processes, semantic networks, situational models, etc. Syntactic level Morphemic level Slobin’s Thinking for speaking paper - get reference Early osgood and bock paper Phonemic level Articulation

22 From thought to speech Grammatical class constraint Slots and frames
Message level Grammatical class constraint Most substitutions, exchanges, and blends involve words of the same grammatical class Slots and frames A syntactic framework is constructed, and then lexical items are inserted into the slots Syntactic level Morphemic level Phonemic level Articulation

23 From thought to speech Rachel Emily Ross It was such a happy
moment when Ross kissed Rachel…

24 From thought to speech Rachel Emily Ross … Oops! I mean
“kissed Emily.”

25 From thought to speech Spreading activation SYNTACTIC FRAME LEXICON
ROSS KISS EMILY RACHEL NP S VP V(past) N Spreading activation

26 From thought to speech SYNTACTIC FRAME LEXICON ROSS KISS EMILY RACHEL
NP S VP V(past) N If the word isn’t the right grammatical class, it won’t “fit” into the slot. Grammatical class constraint:

27 From thought to speech Grammatical class constraint Slots and frames
Message level Grammatical class constraint Most substitutions, exchanges, and blends involve words of the same grammatical class Slots and frames A syntactic framework is constructed, and then lexical items are inserted into the slots Other evidence Syntactic priming Syntactic level Morphemic level Phonemic level Articulation

28 Syntactic priming Bock (1986): syntactic persistance tested by picture naming Hear and repeat a sentence Describe the picture

29 Syntactic priming a: The ghost sold the werewolf a flower Bock (1986): syntactic persistance tested by picture naming b: The ghost sold a flower to the werewolf a: The girl gave the teacher the flowers b: The girl gave the flowers to the teacher

30 Syntactic priming In real life, syntactic priming seems to occur as well Branigan, Pickering, & Cleland (2000): Speakers tend to reuse syntactic constructions of other speakers Potter & Lombardi (1998): Speakers tend to reuse syntactic constructions of just read materials

31 From thought to speech Stranding errors Message level Syntactic level
I liked he would hope you I hoped he would like you Syntactic level The inflection stayed in the same location, the stems moved Inflections tend to stay in their proper place Do not typically see errors like The beeing are buzzes The bees are buzzing Morphemic level Phonemic level Articulation

32 From thought to speech Stranding errors
Message level Stranding errors Closed class items very rare in exchanges or substitutions Two possibilities Part of syntactic frame High frequency, so lots of practice, easily selected, etc. Syntactic level Morphemic level Phonemic level Articulation

33 From thought to speech Consonant vowel regularity Message level
Consonants slip with other consonants, vowels with vowels, but rarely do consonants slip with vowels The implication is that vowels and consonants represent different kinds of units in phonological planning Syntactic level Morphemic level Phonemic level Articulation

34 From thought to speech Consonant vowel regularity
Message level Consonant vowel regularity Frame and slots in syllables Similar to the slots and frames we discussed with syntax Syntactic level Morphemic level Phonemic level Articulation

35 From thought to speech PHONOLOGICAL FRAME Word LEXICON /d/, C /g/, C
, V Syllable Onset Rhyme C V C

36 From thought to speech Message level Consonant vowel regularity
Frame and slots in syllables Evidence for the separation of meaning and sound Syntactic level Tip of the tongue Picture-word interference Morphemic level Phonemic level Articulation

37 Tip-of-the-tongue An instrument used by navigators for measuring
the angular distance of the sun, a star, etc. from the horizon Productivity on multiple levels, phonological, morphemic, syntactic A lot of the utterance is planned prior to onset of the utterance

38 Tip-of-the-tongue Uhh… It is a.. You know.. A.. Arggg.
I can almost see it, it has two Syllables, I think it starts with A ….. Productivity on multiple levels, phonological, morphemic, syntactic A lot of the utterance is planned prior to onset of the utterance TOT Meaning access No (little) phonological access What about syntax?

39 Tip-of-the-tongue “The rhythm of the lost word may be there without the sound to clothe it; or the evanescent sense of something which is the initial vowel or consonant may mock us fitfully, without growing more distinct.” (James, 1890, p. 251)

40 Tip-of-the-tongue Brown & McNeill (1966)
Low-frequency words (e.g., apse, nepotism, sampan), prompted by brief definitions. On 8.5% of trials, tip-of-the-tongue state ensued: Had to guess: word's first or last letters the number of syllables it contained which syllable was stressed

41 Tip-of-the-tongue Brown & McNeill (1966) Total of 360 TOT states:
233 ="positive TOTs" (subject was thinking of target word, and produced scorable data 127 = "negative TOTs" (subject was thinking of other word, but could not recall it) 224 similar-sound TOTs (e.g., Saipan for sampan) 48% had the same number of syllables as the target 95 similar-meaning TOTs (e.g., houseboat for sampan). 20% had same number of syllables as target. 

42 Tip-of-the-tongue Similar words come to mind about half the time
but how much is just guessing? First letter: correct 50-71% of time (vs. 10% by chance) First sound: 36% of time (vs. 6% by chance)

43 Tip-of-the-tongue Results suggest a basic split between semantics/syntax and phonology: People can access meaning and grammar but not pronunciation

44 Tip-of-the-tongue Semantics Syntax
grammatical category (“part of speech”) e.g. noun, verb, adjective Gender e.g. le chien, la vache; le camion, la voiture Number e.g. dog vs. dogs; trousers vs. shirt Count/mass status e.g. oats vs. flour

45 Tip-of-the-tongue Vigliocco et al. (1997)
Subjects (Italian speakers) presented with word definitions Gender was always arbitrary If unable to retrieve word, they answered How well do you think you know the word? Guess the gender Guess the number of syllables Guess as many letters and positions as possible Report any word that comes to mind Then presented with target word Do you know this word? Is this the word you were thinking of?

46 Vigliocco et al (1997) Vigliocco et al. (1997) Scoring + TOT
Both reported some correct information in questionnaire And said yes to recognition question - TOT Otherwise

47 Vigliocco et al (1997) Vigliocco et al. (1997) Results Conclusion
+ TOT: 84% correct gender guess - TOT: 53% correct gender guess chance level Conclusion Subjects often know grammatical gender information even when they have no phonological information Supports split between syntax and phonology in production

48 Nitty-gritty details of the model
Message level Central questions: How many levels are there? Are the stages discrete or cascading? Discrete: 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 Syntactic level Morphemic level Phonemic level Articulation

49 Doing it in time Strongest constraint may be fluency: Incrementality:
Have to get form right under time pressure. Incrementality: ‘Work with what you’ve got’ Flexibility: allows speaker to say something quickly, also respond to changing environment. Modularity: ‘Work only with what you’ve got’ Regulate flow of information.

50 Models of production As in comprehension, there are serial (modular) and interactive models Serial models - Garrett, Levelt et al. Interactive models - Stemberger, Dell Levelt’s monitoring stage (originally proposed by Baars) can explain much of the data that is said to favour interaction between earlier levels

51 Levelt’s model Four broad stages: Conceptualization Formulation
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)

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

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

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

55 Levelt’s model (see chpt 5, pg 115-117)
has stripes is dangerous TIGER (X) Lexical concepts Noun countable tigre Lemmas Fem. /tigre/ Lexemes /t/ /I/ /g/ Phonemes

56 Levelt’s model: conceptual level
has stripes is dangerous Conceptual stratum is not decomposed one lexical concept node for “tiger” instead, conceptual links from “tiger” to “stripes”, etc. TIGER (X)

57 Levelt’s model tigre First, lemma activation occurs TIGER (X) Noun
This involves activating a lemma or lemmas corresponding to the concept thus, concept TIGER activates lemma “tiger” TIGER (X) Noun countable tigre Fem.

58 Levelt’s model tigre lion First, lemma activation occurs TIGER (X)
This involves activating a lemma or lemmas corresponding to the concept thus, concept TIGER activates lemma “tiger” TIGER (X) LION (X) Noun But also involves activating other lemmas TIGER also activates LION (etc.) to some extent and LION activates lemma “lion” tigre lion Fem.

59 Levelt’s model tigre lion First, lemma activation occurs
Second, lemma selection occurs 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 TIGER (X) LION (X) Noun tigre lion Fem.

60 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/

61 Model’s assumptions 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

62 Dell’s interactive account
Dell (1986) presented the best-known interactive account 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

63 Dell’s interactive account
A moment in the production of: “Some swimmers sink” TACTIC FRAMES LEXICAL NETWORK

64 Dell’s interactive account
TACTIC FRAMES LEXICAL NETWORK information Interactive because information flows “upwards” information as well as “downwards” Cascading because processing at lower levels can start early

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

66 Model comparisons Similarities Levelt’s Dell’s Differences
Similar representations Frames and slots Insertion of representations into the frames Similarities Levelt’s Dell’s Serial Modular Interactive Cascaded Differences

67 A model of sentence 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

68 Experimental tests Picture-word interference task tiger
Participants name basic objects as quickly as possible Distractor words are embedded in the object participants are instructed to ignore these words tiger

69 Experimental tests Semantically related words can interfere with naming e.g., the word TIGER in a picture of a LION tiger

70 Basic findings However, form-related words can speed up processing
e.g., the word liar in a picture of a LION liar

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

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

73 Schriefers, Meyer, and Levelt (1990)
Auditory presentation of distractors DOT phonologically related CAT semantically related SHIP unrelated word Early Only Semantic effects

74 Schriefers, Meyer, and Levelt (1990)
Auditory presentation of distractors DOT phonologically related CAT semantically related SHIP unrelated word Late Only Phonological effects

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

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

77 Interpretation 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

78 Evidence for interactivity
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

79 Evidence for interactivity
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

80 Evidence for interactivity
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)

81 Evidence for interactivity
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

82 Results Damian & Martin (1999) early semantic inhibition
couch (unrelated) peach (semantically related) apathy (phonologically related) apricot (sem & phono related) early semantic inhibition

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

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

85 Evidence for interactivity
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

86 Evidence against interactivity
Cutting and Ferreira (1999) GAME (X) BALL (X) BALL (X) DANCE (X) game ball ball dance /ball/ Cascading Prediction: dance ball /ball/

87 Results Cutting and Ferreira (1999) Early semantic inhibition

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

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

90 Evidence for interactivity
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

91 Evidence for interactivity
Peterson & Savoy (1998) Used pictures with two synonymous names soda Subordinate Dominant Used words that were phonologically related to the non dominant name of the picture sofa couch

92 Evidence for interactivity
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?

93 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

94 Overall summary 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

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

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

97 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

98 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

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

100 Conversation Herb Clark (1996)
Face-to-face conversation - the basic setting Features Immediacy Medium Control Co-presence Visibility Audibility Instantaneity Evanescence Recordlessness Simultaneity Extemporaneity Self-determination Self-expression Co-presence: the participants share the same physical environment Visibility: the participants can see each other Audibility: the participants can hear each other Instantaneity: the participants perceive each other’s actions at no perceptible delay Evanescence: the medium fades quickly Recordlessness: the participants’ leave no record or artifact Simultaneity: the participants can produce and receive at once and simultaneously Extemporaneity: the participants formulate and execute their actions extemporaneously, in real time Self-determination: the participants determine for themselves what actions to take when Self-expression: the participants take actions as themselves Other settings may lack some of these features e.g., telephone conversations take away co-presence and visibility, which may change language use

101 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. 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.

102 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? 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.

103 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? 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?

104 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? COSTELLO: I need money to track my money? ABBOTT: It comes bundled with your computer. COSTELLO: What's bundled to my computer?

105 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".

106 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

107 Structure of a conversation
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

108 Structure of a conversation
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, … 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 Kevin: I’ll tell her, Joe: thank you Kevin: bye bye Joe: bye

109 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…

110 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

111 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

112 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?

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

114 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

115 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!)

116 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”

117 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

118 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”

119 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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