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Psy1302 Psychology of Language

Psy1302 Psychology of Language. Lecture 8 & 9 Mental Dictionary – The Lexicon Spoken Word Recognition. Agenda of the day. Two related questions How are words organized and stored in our brain? How do we retrieve, look up words from memory? . The Lexicon.

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Psy1302 Psychology of Language

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  1. Psy1302 Psychology of Language Lecture 8 & 9 Mental Dictionary – The Lexicon Spoken Word Recognition

  2. Agenda of the day • Two related questions • How are words organized and stored in our brain? • How do we retrieve, look up words from memory?

  3. The Lexicon A lexicon is usually a list of words together with additional word-specific information, i.e., a dictionary.

  4. Question 1 • How are words organized and stored in our brain?

  5. Subtext • How might psychology experiments • inform us of our mental processes • help us create models of our mental representations and of how our mind process information?

  6. Lexical Decision Task A Classical Reaction Time (RT) Paradigm • Task: Respond to “Is it a word?” • Measure: RT for deciding “yes” or “no” • “blicket” is not a word. Respond: NO • “lick” is a word. Respond: YES • RT (i.e., speed of look-up) informs us of the organization of the mental lexicon

  7. Lexical Decision Task CLAM NUMBER BLUTY PLUP + ASK RASK SNARL PURCE

  8. Lexical Decision Task Frequency Effect - Results (Embick et al., 2001)

  9. Lexical Decision Task Frequency Effect - Summary • In a lexical decision task, responses are faster for more frequent words • Implies lexicon organized by frequency

  10. Lexical Decision TaskSemantic Effect • Semantically Related Word Pairsbutter milk doctor nursehand finger speak talksound volumebook volume

  11. YES Top: Word Bottom: Associate YES Top: Word Bottom: Non-associate NO Top: Word Bottom: Nonword NO Top: Nonword Bottom: Word NO Top: Nonword Bottom: Nonword Lexical Decision TaskSemantic Effect - Design BREAD BUTTER NURSE BUTTER WINE PLAME PLAME WINE PABLE REAB Meyer, D. E., & Schvaneveldt, R. W. (1971)

  12. Lexical Decision TaskSemantic Effect - Results • RT Associated words < RT of ALL other conditions • Of the NO responses of First word is a Nonword < RT First word is a word Meyer, D. E., & Schvaneveldt, R. W. (1971)

  13. Lexical Decision Task Semantics Effect - Summary • In a lexical decision task, responses are faster for semantically associated words [Terminology: • when a target (e.g., BUTTER) is preceded by a stimulus (e.g., BREAD) and the target is processed faster, this is called “PRIMING” • BREAD primes BUTTER • BREAD is the “prime”.] • Implies lexicon organized by meaning

  14. Variants of Priming • Present the words serially • prime = CAPTAINtarget = BOAT Time (lags vary) CAPTAIN #### (sometimes introduce masking or other words introduced in between) BOAT http://opl.apa.org/Experiments/Start.aspx?EID=8

  15. “captain” BOAT Variants of Priming • Cross-modal Priming

  16. BOAT BOAT Cross-Modal Priming(Marslen-Wilson & Zwitserlood) • Prediction of RT? CAPTAIN PAMPHLET FAST SLOW < • Things to control about the primes (CAPTAIN & PAMPHLET): • frequency, syllable duration, and # of letters are comparable.

  17. Lexical Decision TaskPhonology effects Design your own experiment • How might you test whether the lexicon is organized by phonology? For Example: • When you are hearing “spinach” do you access words like “spinster” or “spinach”?

  18. Cross-modal Priming(Marslen-Wilson & Zwitserlood) • Hear word through earphone. • E.g. “Captain” • Judge whether string presented on monitor is a word or not a word. • Does hearing “captain” which sounds like “capital” PRIME “money”? KAPITAAL KAPITEIN

  19. Cross-modal Priming(Marslen-Wilson & Zwitserlood) • Vocabulary in Dutch • KAPITEIN: captain • BOOT: boat • KAPITAAL: capital • GELD: money • PAMFLET: pamphlet

  20. GELD Cross-Modal Priming(Marslen-Wilson & Zwitserlood) “KAPITEIN” (captain) Hear Prime: KAPITAAL-GELD Lexical Decision: (capital-money) • KAPITEIN & KAPITAAL controlled for frequency • (captain) (capital)

  21. GELD Cross-Modal Priming(Marslen-Wilson & Zwitserlood) “PAMFLET” (pamphlet) Hear Prime: PAMFLET-GELD Lexical Decision: (pamphlet-money) • KAPITEIN & PAMFLET controlled for frequency • (captain) (pamphlet)

  22. BOOT GELD BOOT GELD Cross-Modal Priming(Marslen-Wilson & Zwitserlood) • Prediction of RT if KAPITEIN activates KAPITAAL? PAMFLET KAPITEIN (captain) (pamphlet) FAST SLOW (boat) < (boat) = =< < MEDIUM SLOW (money) (money)

  23. Summary of Experiments • The 3 Lexical Decision & Cross-modal Priming Tasks show: • lexicon organized by frequency • lexicon organized by semantic relatedness • lexicon organized by phonological relatedness

  24. Question 2 • How do we retrieve, look up words from memory?

  25. Two Historical Models of Organization and Retrieval • Serial Models (Forster) • Parallel Models (Morton)

  26. Serial Search Models • Forster 1989 • Serially search to match word • Frequency ordered phonological list • Explains more frequent words are accessed first • Semantically associate list • Priming & Context plays a role and makes less frequent words be accessed faster

  27. Serial Search ModelsForster’s Model

  28. Serial Search Models • Forster 1989 • Serially search to match word • Frequency ordered phonological list • Explains more frequent words are accessed first • Semantically associate list • Priming & Context plays a role and makes less frequent words be accessed faster

  29. Serial Search ModelsEvaluation • Physiological unrealistic • Neuron transmission slow • 100 time steps can be computed in ½ second (Feldman & Ballard, 1982) • But retrieval of a word is FAST • If we know ~75K words, searching serially is time consuming.

  30. Parallel Search Models • Morton’s Logogen model (1964, 1979). • (father of other models to come) Next few slides adapted from J. Simner.

  31. Parallel Search ModelMorton’s Logogen Model • The lexical entry for each word comes with a logogen unit • Input such as [k] of “cat” activate the relevant units in parallel. • The lexical entry becomes available once the logogen ‘fires’ • (i.e., reaches threshold) INPUT [k] cat skip cot camp

  32. Parallel Search ModelMorton’s Logogen Model Threshold Analogy of logogen unit ‘firing’. INPUT /kaet/ cat skip cot camp

  33. ‘cat’ [kæt] ‘cot’ [kot] Parallel Search ModelMorton’s Logogen Model High frequency words have a lower threshold for firing, so it takes less to activate. lower threshold = shorter RT

  34. cradle baby bed hospital nurse animal dentist doctor mammal bird canary rain fever heat delirium sun ostrich green grass yellow Network connection of Logogens

  35. Spreading Activation cradle “doctor” spread activation to “nurse” so “nurse” becomes semi-activated. NURSE now requires less to fire. baby bed hospital nurse animal dentist doctor mammal bird canary rain fever heat delirium sun ostrich green grass yellow

  36. Accounting for Data High frequency words have lower threshold Multiple Networks: Words related by… meaning are linked in a semantic network sound are linked in a phonological network How to deal with ability to judge something as a non-word? Impose a timer. After a certain time period, decide not a word Appeal - Efficiency Lexical access is in parallel Parallel Search ModelsEvaluation

  37. In-Class Discussion Time (Discussion of HW assignment) • Design a program to recognize words in speech when given the phonetic transcription. • Simple Case: One word • Hard Case: string of multiple words (word segmentation problem)

  38. Recognizing a single word • The Simplest Idea • Take the string of phonemes/syllables in order and match the string to find a word in our mental lexicon • (In the same way we look up words in a dictionary – alphabet by alphabet.)

  39. S song story sparrow saunter slow secret sentry Cohort of Candidates ... ... (i.e., words beginning w/ the sound heard so far) Slides adapted from P. Collins.

  40. SP spice spoke spare spin splendid spelling spread Cohort of Candidates ... (i.e., words beginning w/ the sound heard so far)

  41. SPI spit spigot spill spiffy spinaker spirit spin Cohort of Candidates ...

  42. Segmenting a string of words (hw Q1B) • THEREDONATEAKETTLEOFTENCHIPS • THE RED ON A TEA KETTLE OFTEN CHIPS • THERE, DON ATE A KETTLE OF TEN CHIPS • PROCEDURE?

  43. Alignment Problem ThesKyisfalling! The skyis falling! or Thisguyis falling!

  44. Segmenting a string of words • THEREDONATEAKETTLEOFTENCHIPS • THE REDONATEAKETTLEOFTENCHIPS • THE RED ONATEAKETTLEOFTENCHIPS • THE RED ON ATEAKETTLEOFTENCHIPS • THE RED ON A TEAKETTLEOFTENCHIPS • … • THE RED ON ATE AKETTLEOFTENCHIPS • … • THE REDO NATEAKETTLEOFTENCHIPS • … • THE REDONATE AKETTLEOFTENCHIPS • … • THERE DONATEAKETTLEOFTENCHIPS • ... (so on and so forth… MANY ALTERNATIVES TO GO THROUGH!!!) (Is this how our brains segments speech?)

  45. Some Design Issues • Depth first vs. Breadth first Search • Pruning?

  46. Outline for rest of today and next lecture • We are fast at speech recognition. • How do we achieve speed? • Contextual Effects • Terminologies and Ideas • Two Classic Models • Cohort Model • TRACE Model [and many experimental paradigms and findings]

  47. Speech Recognition is FAST • Intuitively immediate. • Comprehension happens around 200-300ms after hearing the word. • Eye-tracking Studies • Shadowing Studies • Gating Studies • Word Monitoring Studies

  48. Eye-tracking Technology “Pick up the square”

  49. Computer records scene plus eye fixation Eye camera Scene camera “Pick up the candle” http://www.psych.upenn.edu/trueswell_videos/adult1u.mov

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