2. PSY 369: Psycholinguistics Representing language

18. Some of the big questions “the horse raced past the barn” Production How do we turn our thoughts into a spoken or written output?

19. Some of the big questions Comprehension “the horse raced past the barn” How do we understand language that we hear/see?

20. Some of the big questions Comprehension Production Representation How do we store linguistic information, how do we retrieve that information? Lexicon Semantic Analysis Syntactic Analysis Word Recognition Letter/phoneme Recognition Formulator Grammatical Encoding Phonological Encoding Articulator Conceptualizer Thought

21. Lexicon Semantic Analysis Syntactic Analysis Word Recognition Letter/phoneme Recognition Formulator Grammatical Encoding Phonological Encoding Articulator Conceptualizer Thought

22. Storing linguistic information How are words stored? What are they made up of? How are word related to each other? How do we use them? Mental lexicon The representation of words in long term memory Lexical Access: How do we activate (retrieve) the meanings (and other properties) of words?

23. Theoretical Metaphors: Access vs. retrieval Retrieval - getting information from the representation Activate - finding the representation Often used interchangeably, but sometimes a distinction is made Here it is

24. Theoretical Metaphors: Access vs. retrieval Retrieval - getting information from the representation Activate - finding the representation Often used interchangeably, but sometimes a distinction is made Open it up and see what’s inside

25. Lexical primitives Word primitives Morpheme primitives Economical - fewer representations Slow retrieval - some assembly required Decomposition during comprehension Composition during production Need a lot of representations Fast retrieval horsehorsesbarnbarnshorse-sbarn

26. Lexical primitives Lexical Decision task (e.g., Taft, 1981) See a string of letters As fast as you can determine if it is a real English word or not “yes” if it is “no” if it isn’t Typically speed and accuracy are the dependent measures

27. table

28. vanue

29. daughter

30. tasp

31. cofef

32. hunter

33. Lexical primitives Lexical Decision task table vanue daughter tasp cofef hunter Yes No Yes No Yes

34. Lexical primitives Lexical Decision task daughter hunter

35. Lexical primitives Lexical Decision task daughter hunter Pseudo-suffixed Multimorphemic daught hunt-er Takes longer This evidence supports the morphemes as primitives view

36. Lexical primitives May depend on other factors What kind of morpheme Inflectional (e.g., singular/plural, past/present tense) Derivational (e.g., drink --> drinkable, infect --> disinfect) Frequency of usage High frequency multimorphemic (in particular if derivational morphology) may get represented as a single unit e.g., impossible vs. imperceptible Compound words Semantically transparent Buttonhole Semantically opaque butterfly

37. Lexical organization How are the lexical representations organized? Alphabetically? Initial phoneme? Semantic categories? Grammatical class? Something more flexible, depending on your needs?

38. Lexical organization Factors that affect organization Phonology Frequency Imageability, concreteness, abstractness Grammatical class Semantics

39. Lexical organization Phonology Words that sound alike may be stored “close together” Tip of the tongue phenomenon (TOT) Brown and McNeill (1966) More likely to approximate target words with similar sounding words than similar meanings What word means to formally renounce the throne? abdicate

40. Lexical organization Frequency Typically the more common a word, the faster (and more accurately) it is named and recognized Typical interpretation: easier to retrieve (or activate) However, Balota and Chumbley (1984) Frequency effects depend on task Lexcial decision - big effect Naming - small effect Category verifcation - no effect A canary is a bird. T/F

41. Lexical organization Imageability, concreteness, abstractness Umbrella Lantern Freedom Apple Knowledge Evil Try to imagine each word

42. Lexical organization Imageability, concreteness, abstractness Umbrella Lantern Freedom Apple Knowledge Evil Try to imagine each word How do you imagine these?

43. Lexical organization Imageability, concreteness, abstractness Umbrella Lantern Freedom Apple Knowledge Evil More easily remembered More easily accessed

44. Lexical organization Grammatical class Grammatical class constraint on substitution errors “she was my strongest propeller” (proponent) “the nation’s dictator has been exposed” (deposed) Word association tasks Associate is typically of same grammatical class

45. Lexical organization Grammatical class Open class words Content words (nouns, verbs, adjectives, adverbs) Closed class words Function words (determiners, prepositions, …)

46. Lexical organization Semantics Free associations (see the “cat” demo in earlier lecture) Most associates are semantically related (rather than phonologically for example) Semantic Priming task For the following letter strings, decide whether it is or is not an English word

47. tasp

48. nurse

49. doctor

50. fract

51. slithest

52. shoes

53. doctor

54. Lexical organization Semantic Priming task nurse shoes Responded to fasterRelated Unrelated “Priming effect” doctor

55. Lexical organization Semantics Words that are related in meaning are linked together Semantic networks

56. Lexical organization Another possibility is that there are multiple levels of representation, with different organizations at each level Sound based representationsMeaning based representationsGrammatical based representations

57. Semantic Networks Words can be represented as an interconnected network of sense relations Each word is a particular node Connections among nodes represent semantic relationships

58. Collins and Quillian (1969) Animal has skin can move around breathes Lexical entry Semantic Features Collins and Quillian Hierarchical Network model Lexical entries stored in a hierarchy Semantic features attached to the lexical entries

59. Collins and Quillian (1969) Animal has skin can move around breathes has fins can swim has gills has feathers can fly has wings Bird Fish Representation permits cognitive economy Reduce redundancy of semantic features

60. Collins and Quillian (1969) Animal has skin can move around breathes Fish has fins can swim has gills Bird has feathers can fly has wings Canary can sing is yellow Ostrich has long legs is fast can’t fly Local level features may contradict higher level features

61. Collins and Quillian (1969) Testing the model Semantic verification task An A is a B True/False An apple is a fruit

62. Collins and Quillian (1969) Testing the model Semantic verification task An A is a B True/False An robin has wings

63. Collins and Quillian (1969) Testing the model Semantic verification task An A is a B True/False A robin is a bird

64. Collins and Quillian (1969) Testing the model Semantic verification task An A is a B True/False A robin is an animal

65. Collins and Quillian (1969) Testing the model Semantic verification task An A is a B True/False A dog has teeth

66. Collins and Quillian (1969) Testing the model Semantic verification task An A is a B True/False A fish has gills

67. Collins and Quillian (1969) Testing the model Semantic verification task An A is a B True/False Use time on verification tasks to map out the structure of the lexicon. An apple has teeth

68. Collins and Quillian (1969) Testing the model SentenceVerification time Robins eat worms 1310 msecs Robins have feathers 1380 msecs Robins have skin 1470 msecs A category size effect: The higher the location of B, the longer the reaction time (“A is a B” or “A has a B”) Participants do an intersection search

69. Collins and Quillian (1969) Animal has skin can move around breathes Bird has feathers can fly has wings Robin eats worms has a red breast Robins eat worms

70. Collins and Quillian (1969) Animal has skin can move around breathes Bird has feathers can fly has wings Robin eats worms has a red breast Robins eat worms

71. Collins and Quillian (1969) Animal has skin can move around breathes Bird has feathers can fly has wings Robin eats worms has a red breast Robins have feathers

72. Collins and Quillian (1969) Animal has skin can move around breathes Bird has feathers can fly has wings Robin eats worms has a red breast Robins have feathers

73. Collins and Quillian (1969) Animal has skin can move around breathes Bird has feathers can fly has wings Robin eats worms has a red breast Robins have skin

74. Collins and Quillian (1969) Animal has skin can move around breathes Bird has feathers can fly has wings Robin eats worms has a red breast Robins have skin

75. Collins and Quillian (1969) Problems with the model Effect may be due to frequency of association “A robin breathes” is less frequent than “A robin eats worms” Assumption that all lexical entries at the same level are equal The Typicality Effect A whale is a fish vs. A horse is a fish Which is a more typical bird? Ostrich or Robin.

76. Collins and Quillian (1969) Animal has skin can move around breathes Fish has fins can swim has gills Bird has feathers can fly has wings Robin eats worms has a red breast Ostrich has long legs is fast can’t fly

77. Semantic Networks Prototypes: Some members of a category are better instances of the category than others Fruit: Apple vs. pomegranate What makes a prototype? More central semantic features What type of dog is a prototypical dog What are the features of it? We are faster at retrieving prototypes of a category than other members of the category

78. Spreading Activation Models Collins & Loftus (1975) Words represented in lexicon as a network of relationships Organization is a web of interconnected nodes in which connections can represent: categorical relations degree of association typicality

79. Semantic Networks street car bus vehicle red Fire engine truck roses blue orange flowers fire house apple pear tulips fruit

80. Semantic Networks Retrieval of information Spreading activation Limited amount of activation to spread Verification times depend on closeness of two concepts in a network

81. Semantic Networks Fire engine truckbusvehiclecar red housefire apple pear fruit roses flowers tulips blue orangestreet

82. Semantic Networks Fire engine truckbusvehiclecar red housefire apple pear fruit roses flowers tulips blue orangestreet

83. Semantic Networks Fire engine truckbusvehiclecar red housefire apple pear fruit roses flowers tulips blue orangestreet

84. Semantic Networks Fire engine truckbusvehiclecar red housefire apple pear fruit roses flowers tulips blue orangestreet

85. Semantic Networks Fire engine truckbusvehiclecar red housefire apple pear fruit roses flowers tulips blue orangestreet

86. Semantic Networks Advantages of Collins and Loftus model Recognizes diversity of information in a semantic network Captures complexity of our semantic representation Consistent with results from priming studies

87. Bock and Levelt (1994) SHEEPGOAT SheepGoat /gout/ / ip/ ipgout N woolmilkanimal Concepts with semantic features Lemmas grammtical features Lexemes morphemes and sounds Phonemes growth gives Is an category

88. Lexical access How do we retrieve the linguistic information from Long-term memory? What factors are involved in retrieving information from the lexicon? Models of lexical retrieval

89. Recognizing a word cat dog cap wolf tree yarn cat claw fur hat Search for a match cat Input

90. Recognizing a word cat dog cap wolf tree yarn cat claw fur hat Search for a match cat Input

91. Recognizing a word cat dog cap wolf tree yarn cat claw fur hat Search for a match Select word cat Retrieve lexical information Cat noun Animal, pet, Meows, furry, Purrs, etc. cat Input

92. Lexical access Factors affecting lexical access Frequency Semantic priming Role of prior context Phonological structure Morphological structure Lexical ambiguity

93. Word frequency Gambastya Revery Voitle Chard Wefe Cratily Decoy Puldow Raflot Mulvow Governor Bless Tuglety Gare Relief Ruftily History Pindle Lexical Decision Task: Oriole Vuluble Chalt Awry Signet Trave Crock Cryptic Ewe Develop Gardot Busy Effort Garvola Match Sard Pleasant Coin

94. Word frequency Gambastya Revery Voitle Chard Wefe Cratily Decoy Puldow Raflot Mulvow Governor Bless Tuglety Gare Relief Ruftily History Pindle Lexical Decision Task: Lexical Decision is dependent on word frequency Oriole Vuluble Chalt Awry Signet Trave Crock Cryptic Ewe Develop Gardot Busy Effort Garvola Match Sard Pleasant Coin Low frequencyHigh(er) frequency

95. Word frequency The kite fell on the dog Eyemovement studies:

96. Word frequency The kite fell on the dog Eyemovement studies:

97. Word frequency The kite fell on the dog Eyemovement studies:

98. Word frequency The kite fell on the dog Eyemovement studies: Subjects spend about 80 msecs longer fixating on low- frequency words than high- frequency words

99. Semantic priming Meyer & Schvaneveldt (1971) Lexical Decision Task PrimeTargetTime Nurse Butter940 msecs BreadButter855 msecs Evidence that associative relations influence lexical access

100. Role of prior context Listen to short paragraph. At some point during the Paragraph a string of letters will appear on the screen. Decide if it is an English word or not. Say ‘yes’ or ‘no’ as quickly as you can.

101. Role of prior context ant

102. Role of prior context Swinney (1979) Hear: “Rumor had it that, for years, the government building has been plagued with problems. The man was not surprised when he found several spiders, roaches and other bugs in the corner of his room.” Lexical Decision task Context related:ant Context inappropriate:spy Context unrelatedsew Results and conclusions Within 400 msecs of hearing "bugs", both ant and spy are primed After 700 msecs, only ant is primed

103. Lexical ambiguity Hogaboam and Pefetti (1975) Words can have multiple interpretations The role of frequency of meaning Task, is the last word ambiguous? The jealous husband read the letter (dominant meaning) The antique typewriter was missing a letter (subordinate meaning) Participants are faster on the second sentence.

104. Morphological structure Snodgrass and Jarvell (1972) Do we strip off the prefixes and suffixes of a word for lexical access? Lexical Decision Task: Response times greater for affixed words than words without affixes Evidence suggests that there is a stage where prefixes are stripped.

105. Models of lexical access Serial comparison models Search model (Forster, 1976, 1979, 1987, 1989) Parallel comparison models Logogen model (Morton, 1969) Cohort model (Marslen-Wilson, 1987, 1990)

106. Logogen model (Morton 1969) Auditory stimuli Visual stimuli Auditory analysis Visual analysis Logogen system Output buffer Context system Responses Available Responses Semantic Attributes

107. Logogen model The lexical entry for each word comes with a logogen The lexical entry only becomes available once the logogen ‘fires’ When does a logogen fire? When you read/hear the word

108. Think of a logogen as being like a ‘strength-o-meter’ at a fairground When the bell rings, the logogen has ‘fired’

109. ‘cat’ [kæt] What makes the logogen fire? – seeing/hearing the word What happens once the logogen has fired? – access to lexical entry!

110. – High frequency words have a lower threshold for firing –e.g., cat vs. cot ‘cat’ [kæt] So how does this help us to explain the frequency effect? ‘cot’ [kot] Low freq takes longer

111. Spreading activation from doctor lowers the threshold for nurse to fire – So nurse take less time to fire ‘nurse’ [n ə :s] ‘doctor’ [dokt ə ] nurse doctor Spreading activation network doctornurse

112. Search model Entries in order of Decreasing frequency Visual input cat Auditory input /kat/ Access codes Pointers matcatmouse Mental lexicon

113. Cohort model Specifically for auditory word recognition Speakers can recognize a word very rapidly Usually within 200-250 msec Recognition point (uniqueness point) - point at which a word is unambiguously different from other words and can be recognized Three stages of word recognition 1) activate a set of possible candidates 2) narrow the search to one candidate 3) integrate single candidate into semantic and syntactic context

114. Cohort model Prior context: “I took the car for a …” /s//sp//spi//spin/ … soap spinach psychologist spin spit sun spank … spinach spin spit spank … spinach spin spit … spin time

115. Summarize similarities and diffs: Parallel vs serial Role of context Note: There are other models out there (TRACE, FLMP, various connectionist models, and more)