2. Psy1302 Psychology of Language Lecture 14 & 15 Speech Production

3. Comprehension vs. Production

4. S NP VP V NP /th/.../uh/.../d/.../ah/.../g/.../ch/...etc.

5. Creating Sentences Our brain does not store all sentences we might ever need to produce. Our brain does not store all sentences we might ever need to produce. We must construct and plan our speech using our knowledge of language We must construct and plan our speech using our knowledge of language The main issue of speech production concern the processes by which units come to be selected and then combined in a particular order. The main issue of speech production concern the processes by which units come to be selected and then combined in a particular order.

6. Studying Speech Production HOW? HOW? Can we find evidence that we build structure on the fly? Can we find evidence that we build structure on the fly? –Yes... e.g. Slips of the Tongue Much of our initial knowledge of speech production comes from Much of our initial knowledge of speech production comes from –Slips of the tongue –Tip of the tongue phenomenon –Disfluencies

7. Slips of the Tongue Freudian Slips Presidential Slip during campaign (Reported in Newsweek, 1992): I don’t want to run the risk of ruining what is a lovely recession. “reception” not “recession”

8. Slips of the Tongue Malapropisms Webster definition: the usually unintentionally humorous misuse or distortion of a word or phrase Origin: slips named after Mrs. Malaprop (mal à propos), a fictional character in a Richard Sheridan play (The Rivals). O, he will dissolve my mystery! O, he will dissolve my mystery! He was a man of great statue. He was a man of great statue. Thomas Menino, Boston mayor Thomas Menino, Boston mayor Republicans understand the importance of bondage between a mother and child. Republicans understand the importance of bondage between a mother and child. Dan Quayle, Vice President Dan Quayle, Vice President http://www.fun-with-words.com/malapropisms.html

9. Slips of the Tongue Spoonerisms Webster definition: a transposition of usually initial sounds of two or more words (as in tons of soil for sons of toil) Origin: slips named after Rev. William Archibald Spooner You have hissed all my mystery lectures. You have hissed all my mystery lectures. He is a shoving leopard to his flock. He is a shoving leopard to his flock. Three cheers for our queer old dean! Three cheers for our queer old dean! Anglican Priest Dean of Oxford http://www.fun-with-words.com/spoon_history.html

10. Slips of the Tongue science Knowing which slips are possible and which are not constrains theories of production Knowing which slips are possible and which are not constrains theories of production Models of speech production need to account for these regularities in slips Models of speech production need to account for these regularities in slips

11. Slips of the hand Newkirk, Klima, Penderson & Bellugi (1980) Corpus of 131 errors in ASL Corpus of 131 errors in ASL –77 videotaped –54 reported observations Errors like slips of the tongue Errors like slips of the tongue –Exchanges –Anticipations –Perseverations Digression…

15. Types of Error Misordering Misordering –Substitution Exchange Exchange Anticipation Anticipation Perseveration Perseveration –Addition Anticipatory addition Anticipatory addition Perseveration addition Perseveration addition –Shift –Deletion Noncontextual error Noncontextual error –Substitution –Addition –Deletion –Blend (word level)

17. Observations Exchanged segments are from the same level Exchanged segments are from the same level Implies you would never hear: Implies you would never hear: – Phoneme Level with Word Level “The cl is marketosed.” (the market is closed) “The cl is marketosed.” (the market is closed)

18. Observations Exchanged segments tend to be from the same kind of segment Exchanged segments tend to be from the same kind of segment –Consonant onset with consonant onset –Vowel with vowel Etc… –Verb with verb –Noun with noun Implies you would never hear: Implies you would never hear: –Vowel with Consonant “Hauow thld” (hallow thud) “Hauow thld” (hallow thud) –Onset with Rhyme “Udallow thud” (hallow thud) “Udallow thud” (hallow thud)

19. Observations Sound substitutions Sound substitutions –Often close to each other –Not necessarily similar in grammatical category and often similar in sound Implies TYPICAL ERROR: I saw you fight a liar in the back quad, in fact you have... UNCOMMON ERROR: I saw you light a fire in the yack quad, in fact boo have...

20. Observations Sound substitutions Sound substitutions –Often close to each other –Not necessarily similar in grammatical category and often similar in sound Word substitutions can cross phrasal boundaries Word substitutions can cross phrasal boundaries –Often far apart, crossing phrasal boundaries –Often same grammatical category and dissimilar in sound Independence of stem morphemes from derivational morpheme Independence of stem morphemes from derivational morpheme

21. Errors at Multiple Levels Dell (1991)

22. S NP VP V NP /th/.../uh/.../d/.../ah/.../g/.../ch/...etc. Stages of Assembly

23. Language production requires assembling multiple levels of linguistic structure accurately and fluently, in real time. Language production requires assembling multiple levels of linguistic structure accurately and fluently, in real time. Three levels: Three levels: –Conceptualization –Formulation –Articulation S NP VP V NP /th/.../uh/.../d/.../ah/.../g/.../ch/...etc. The dog chased the cat.

24. Formulation What did the speech errors tell us about formulation? What did the speech errors tell us about formulation? Separation between accessing semantics/syntax (meaning/grammar) and phonology (pronunciation) of word Separation between accessing semantics/syntax (meaning/grammar) and phonology (pronunciation) of word

25. Formulation Distributional properties of errors suggest Grammatical Encoding stage Grammatical Encoding stage –Puts words in order –Sounds irrelevant –Syntactic relations relevant –Wide scope planning Phonological Encoding stage Phonological Encoding stage –Puts phonemes in order –Sounds are relevant –Syntax is irrelevant –Narrow scope planning

26. Language production requires assembling multiple levels of linguistic structure accurately and fluently, in real time. Language production requires assembling multiple levels of linguistic structure accurately and fluently, in real time. Three levels: Three levels: –Conceptualization –Formulation –Articulation Grammatical Encoding Phonological Encoding

27. Single Word Production Lemma retrieval: Lemma retrieval: –select a word that matches needed meaning and grammatical category Lexeme retrieval: Lexeme retrieval: –retrieve the sound of a word p. 111-113 of Carroll Digression…

28. Why might you believe in a distinction between Lexeme and Lemma? Tip of the tongue Tip of the tongue –Can retrieve lemma without lexeme know the meaning, first letter, syllables, and stress pattern but can’t generate the word!!! know the meaning, first letter, syllables, and stress pattern but can’t generate the word!!! Digression…

29. Picture Naming Tasks Name that picture Name that picture Sometimes with Print or Audio Distractor Sometimes with Print or Audio Distractor Vary Stimulus Onset Assynchrony (SOA) Vary Stimulus Onset Assynchrony (SOA) SOA timeline Negative SOA 0 ms Positive SOA Digression…

30. Picture Naming Tasks Name that picture Name that picture Sometimes with Print or Audio Distractor Sometimes with Print or Audio Distractor Vary Stimulus Onset Assynchrony (SOA) Vary Stimulus Onset Assynchrony (SOA) SOA timeline goat 0 ms -150 ms Hear: Digression…

31. Picture Naming Task Name that picture Name that picture Sometimes with Print or Audio Distractor Sometimes with Print or Audio Distractor Vary Stimulus Onset Assynchrony (SOA) Vary Stimulus Onset Assynchrony (SOA) SOA timeline goat 0 ms 150 ms Hear: Digression…

32. Schriefer, Meyer, & Levelt (1990) Semantic distractor: (e.g. goat for sheep) Semantic distractor: (e.g. goat for sheep) Inhibition occurs at SOA = -150ms (Before presentation of picture) Phonological distractor: (e.g. sheet for sheep) Phonological distractor: (e.g. sheet for sheep) Facilitation occurs between SOA = 0 to 150 ms (After presentation of picture) No facilitation at SOA = -150 ms “goat” activates Goat Lemma competes with Sheep Lemma for selection, causing inhibition. “sheet” activates sounds and is similar in sound to “sheep”, facilitating production. Suggest phonological encoding follows lexical selection Finding is consistent with model we are going to see Digression…

33. Language production requires assembling multiple levels of linguistic structure accurately and fluently, in real time. Language production requires assembling multiple levels of linguistic structure accurately and fluently, in real time. Three levels: Three levels: –Conceptualization –Formulation –Articulation Grammatical Encoding Phonological Encoding Functional Processing Positional Processing

34. Garrett’s Model

35. Functional Processing Positional Processing Grammatical Encoding LEMMA LEXEME

36. Planning a sentence She handed him a broccoli. She handed him a broccoli.

37. Message Level – Intended meaning AGENT THEME RECIPIENT ACTION LEMMA RETRIEVAL Feminine Pronominal Masculine Pronominal Vegetable floret Act of Transferring POSSIBLE ERRORS? SEMANTIC SUBSTITUTION e.g. BROCCOLI  CAULIFLOWER

38. AGENT RECIPIENT THEME ACTION she him broccoli hand FUNCTIONAL ASSIGNMENT VERB ARGUMENTS CASE ASSIGNMENTS POSSIBLE ERRORS? WRONG CASE ASSIGNMENT e.g. Female pronoun-nominative (SHE), Male pronoun-dative (HIM)  Female pronoun-dative (HER), Male pronoun- nominative (HIM).

39. she him hand FUNCTIONAL PROCESSING broccoli indefiinite POSSIBLE ERRORS? STRANDING He ordered up ending some broccoli. SHIFTS – often inflections NOT root She was hand himming some broccoli Suggests processing of inflectional (and derivational) morphology at this level POSITIONAL PROCESSING

40. Errors and Stages Intended Message: She handed him some broccoli She handed him some broccoli Likely Error He handed her some broccoli He handed her some broccoli Unlikely Error Her handed he some broccoli Her handed he some broccoli Him handed she some broccoli Him handed she some broccoli

41. Common Themes Garden Path Theory (when we talked about comprehension)? And notions of: –Modularity –Informational Encapsulation (e.g., Syntactic Parser: access to grammatical function categories, but not thematic information in the initial parse) Garrett’s Model (when we talked about production)? Same ideas of Modularity and Information Encapsulation: –Discrete Processing –Functional Processing – lemmas (access to grammatical function but not phonological structure) –Positional Processing and Phonological Processing – lexemes (access to phonological structure but not grammatical function)

42. Issues: Discrete-stage processing Strict Feedforward (Completion of one stage before the next) Cascading processing (Partial information sent to the lower level) Interactive processing Feedback (Lower level affect higher level)

43. Are the stages discrete or cascading? Production Issues Levelt et al. (1991) lemma level lexeme level /sheep/ STAGE 1 STAGE 2 GOAT SHEEP /goat/ ? ?

44. Are stages discrete or cascading? How do we test? Are stages discrete or cascading? How do we test? Does sheep prime goal? ? lemma level lexeme level /sheep/ STAGE 1 STAGE 2 goat /goat/ /goal/ /sheet/ SHEEP

45. Discrete Processing lemma level lexeme level /sheep/ STAGE 1 STAGE 2 goat Does sheep prime goal? /sheet/ Discrete Processing says NO! SHEEP

46. lemma level lexeme level /sheep/ STAGE 1 STAGE 2 goat /goat/ /goal/ /sheet/ Cascading Processing Does sheep prime goal? Cascading Processing says YES! SHEEP

47. lemma level (Semantic) lexeme level (Phonology) /sheep/ goat /goat/ /goal/ /sheet/ /sheep/ goat /sheet/ CASCADING DISCRETE Cascade or Discrete? sheep

48. Primary Task: Name the Picture Secondary Task: Lexical Decision Naming: 600 ms 150 ms125 ms325 ms VLemLex Lexical decision: goal or goat or sheet or mukle (button yes/no-rt) Mediated Priming Paradigm Does sheep prime goal? Levelt et al. (1991): No.

49. lemma level couch lexeme level /couch/ STAGE 1 STAGE 2 sofa /sofa/ /soda/ Peterson & Savoy (1998): Yes it does: couch primes soda via sofa sheep – goat: categorical associates sofa – couch: near synonyms Peterson & Savoy (1998)

50. Production Issues Are the stages interactive? (Levelt, no; Dell, yes) lemma level cat lexeme level /cat/ Levelt FOGDOGCATRATMAT frdkmaeotg Onsets Vowels Codas Dell

51. Gary Dell’s Model Like the TRACE model Like the TRACE model FOGDOGCATRATMAT frdkmaeotg Onsets Vowels Codas Interactive processing Feedback (Lower level affect higher level)

52. Message: Cat

53. Message: Some swimmers sink.

54. A 2-step Interactive Model of Lexical Access in Production FOGDOGCATRATMAT frdkmaeotg OnsetsVowels Codas Semantic Features Adapted from Gary Dell, “ Producing words from pictures or from other words”

55. Activate semantic features of CAT FOGDOGCATRATMAT frdkmaeotg OnsetsVowels Codas Semantic Features Adapted from Gary Dell, “ Producing words from pictures or from other words”

56. 1. Lemma Access: Activation spreads through network FOGDOGCATRATMAT frdkmaeotg OnsetsVowels Codas Adapted from Gary Dell, “ Producing words from pictures or from other words”

57. Activation after 8 steps FOGDOGCATRATMAT frdkmaeotg OnsetsVowels Codas Adapted from Gary Dell, “ Producing words from pictures or from other words”

58. 1. Lemma Access: Most active word from proper category is selected and linked to syntactic frame FOGDOGCATRATMAT frdkmaeotg OnsetsVowels Codas Adapted from Gary Dell, “ Producing words from pictures or from other words” NP N

59. 2. Phonological Access: Jolt of activation is sent to selected word FOGDOGCATRATMAT frdkmaeotg OnsetsVowels Codas Adapted from Gary Dell, “ Producing words from pictures or from other words” NP N

60. 2. Phonological Access: Activation spreads through network FOGDOGCATRATMAT frdkmaeotg OnsetsVowels Codas Adapted from Gary Dell, “ Producing words from pictures or from other words” NP N

61. 2. Phonological Access: Most activated phonemes are selected FOGDOGCATRATMAT frdkmaeotg OnsetsVowels Codas Adapted from Gary Dell, “ Producing words from pictures or from other words” Syl On Vo Co

62. Errors (top-down) Semantic: Shared features activate semantic neighbors FOGDOGCATRATMAT frdkmaeotg OnsetsVowels Codas Adapted from Gary Dell, “ Producing words from pictures or from other words” NP N

63. Errors (bottom-up) Phoneme-word feedback activates formal neighbors FOGDOGCATRATMAT frdkmaeotg OnsetsVowels Codas Adapted from Gary Dell, “ Producing words from pictures or from other words” NP N

64. Errors (top-down & bottom-up) neighbors activated by both top-down & bottom-up sources FOGDOGCATRATMAT frdkmaeotg OnsetsVowels Codas Adapted from Gary Dell, “ Producing words from pictures or from other words” NP N

65. Errors (top-down & bottom-up) Selection of incorrect phonemes FOGDOGCATRATMAT frdkmaeotg OnsetsVowels Codas Adapted from Gary Dell, “ Producing words from pictures or from other words” Syl On Vo Co

66. Interactive or Discrete? Bad Dean  Dad Bean Back Deal  Dack Beal Other: DAD Other: BEAN Target: BAD Target: DEAN C1 /b/ C2 /d/ V1 /ae/ V2 /i/ C3 /d/ C4 /n/ Target: BACK Target: DEAL C1 /b/ C2 /d/ V1 /ae/ V2 /i/ C3 /k/ C4 /l/

67. Lexical Bias Effect Words in the lexicon influence sound substitutions Words in the lexicon influence sound substitutions Experimental Data: Experimental Data: –Probabilities calculated from speech error corpus Sound substitutions resulting in words is higher than chance Sound substitutions resulting in words is higher than chance –Inducing speech errors in laboratory using the Speech Error Generation Paradigm Sound substitutions resulting in words is more likely to happen than those not resulting in words Sound substitutions resulting in words is more likely to happen than those not resulting in words Bad Dean  Dad Bean; Dad Dean; Bad Bean Back Deal  Dack Beal; Dack Deal; Back Beal

68. Speech Error Generation Paradigm Instructions: Instructions: –You will see word pairs on the screen. –Read the words to yourself silently, –But be prepared to say the words out loud. –When you see “????????” on the screen, –Say the last word pair out loud as quickly as possible.

69. YELL NOTSEED REAPSAME ROPE????????

70. LAMB TOYLOOM TENTLET TANKTIME LINE????????

71. BID MEEKBUD MEEKBIG MENMAD BACK????????

72. BALL DOZEBASH DOORBEAN DECKBELL DARKDARN BORE????????

73. BIG DUTCHBANG DOLLBILL DEALBARK DOGDART BOARD????????

74. Speech Error Generation Paradigm (Dell 1986) Additionally: Speech Rate Manipulation 3 groups of participants: 500 ms 700 ms 1000 ms What kind of error?

75. Speech Error Generation Paradigm (Dell 1986) Repeated Phoneme (Same Vowel) Non-Repeated Phoneme (Diff Vowel) Word Outcome Bead Dean  Deed Bean Bad Dean  Dad Bean No Word Outcome Beak Deal  Deak Beak Back Deal  Dack Beal HIGH error rate LOW error rate

76. Predictions? Target: DEAN Target: BAD C1 /b/ C2 /d/ V1 /ae/ V2 /i/ C3 /d/ C4 /n/ Target: Bead Target: Dean C1 /b/ C2 /d/ V2 /i/ C3 /d/ C4 /n/ Bad Dean  Dad Bean Bead Dean  Deed Bean Other: DAD Other: BEAN Other: DEED Other: BEAN

77. LOWEST error rate HIGHEST error rate Word Outcome Repeated Phoneme No Repeated Phoneme Non-Word Outcome > > >>

78. Speech Rate and Errors 1. ____ speech rate, more errors 500 ms: 112; 700 ms: 89; 1000 ms: 55 errors 2. Full exchanges occur at _____ speech rate 500 ms: 39 (35%); 700 ms: 26 (29%); 1000 ms: 7 (13%) 3. Lexical bias effect should increase for _____ speech rate 500 ms: 51 (46%); 700 ms: 54 (61%); 1000 ms: 34 (62%) 4. Repeated phoneme effect should increase with _____ speech rate 500 ms: 51 (46%); 700 ms: 48 (54%); 1000 ms: 29 (53%) faster slower

79. Corpus Estimates Dell & Reich (1981) Asked naïve students to collect speech errors for a month Asked naïve students to collect speech errors for a month From corpus take 2 word speech errors (e.g., pitch fork  fitch pork) From corpus take 2 word speech errors (e.g., pitch fork  fitch pork) –Calculate percentage of sound exchanges that resulted in words –Compare percentage to estimated chance that an exchange would result in a word.

80. Estimating Chance

82. First position (Pitch  Fitch) Second position (Fork  Pork) 60 50 40 30 % resulting in words (e.g. Pitch Fork  Fitch Pork) * Data for anticipation and perseveration similar: % resulting in words is higher than chance estimates. Actual Data Chance Estimate Results

83. Interactive or Discrete? Distributional properties of errors suggest Grammatical Encoding stage Grammatical Encoding stage –Puts words in order –Sounds irrelevant –Syntactic relations relevant –Wide scope planning Phonological Encoding stage Phonological Encoding stage –Puts phonemes in order –Sounds are relevant –Syntax is irrelevant –Narrow scope planning ??? Do phonological factors influence rates of word substitution? Broccoli  Cauliflower (NO SOUND SIMILARITY) Present  Pressure (SOUND SIMILARITY) According to discrete processing, selection of lexical items should not be influenced by sound similarity. Q: are word substitutions with sound substitutions greater than chance?

84. Dell & Reich (1981) continued From same corpus created by naïve students, found 289 word substitutions Determine type of word substitution: Determine type of word substitution: –semantic or other Divide the word and target up by phoneme segments and calculate for each segment whether the sound matches Divide the word and target up by phoneme segments and calculate for each segment whether the sound matches Compare with chance estimate that is based on proportions of phonemes at each segment Compare with chance estimate that is based on proportions of phonemes at each segment

86. Things to think about New data mustered to support the interactive view. New data mustered to support the interactive view. But could the discrete processing view account for the new data as well? But could the discrete processing view account for the new data as well? Could we save Garrett’s model by a fix? Could we save Garrett’s model by a fix? –E.g. self-monitoring?