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?
Other organizations of the dictionary: (1) Filed by last letter of the word instead of first letter, (2) filed first by word length and then letter, etc.

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
SNARL +
ASK
RASK
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 Task Semantic Effect
Semantically Related Word Pairs butter milk
doctor nurse hand finger speak talk sound volume book volume

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

12. Lexical Decision Task Semantic 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 = CAPTAIN target = BOAT
Time (lags vary)
CAPTAIN
####
(sometimes introduce masking or other words introduced in between)
BOAT

15. Variants of Priming Cross-modal Priming “captain” BOAT
Sometimes hear sentences as opposed to words in isolation

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

17. Lexical Decision Task Phonology 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”?
KAPITEIN
KAPITAAL

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

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

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

22. Cross-Modal Priming (Marslen-Wilson & Zwitserlood)
Prediction of RT if KAPITEIN activates KAPITAAL?
KAPITEIN
PAMFLET
(captain)
(pamphlet)
BOOT
BOOT
FAST
SLOW
(boat)
<
(boat) =
=<
GELD
GELD
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 Models Forster’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 Models Evaluation
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 Model Morton’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 Model Morton’s Logogen Model
Threshold
Analogy of logogen unit ‘firing’.
INPUT /kaet/
cat
skip
cot
camp

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

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

35. Spreading Activation cradle baby bed hospital nurse animal dentist
“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. Parallel Search Models Evaluation
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

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

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

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

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. or Alignment Problem ThesKyisfalling! The sky is falling!
This guy is 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?

47. 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]

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

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

50. Computer records scene plus eye fixation
Eye camera
Scene camera
“Pick up the candle”

51. Eye-tracking Technology
Same Command: “Pick up the candle.”
Two Different Scenes
Competitor ABSENT
Competitor PRESENT
COMPETITOR
of “candle”
(phonologically) + +
TARGET

52. Results Latency from onset of spoken target
It takes 200ms to program an eye movement.
So processing time here is about 200ms to 250ms for word comprehension.
FAST!!!

53. Digression: Typical Eyetracking Data Presentation

54. Speech Recognition How do we achieve speed? But…
Parallel search of words
I.e. Activation of potential candidates in parallel
But…
Parallel search leads to many likely candidates to consider at the same time.
If there are many possible likely candidates, how do we QUICKLY select the right candidate among the choices?

55. Speech Recognition CONTEXTUAL EFFECTS? Suppose you hear:
Popeye loves to eat…
Have you already guessed what the next word is going to be?

56. Shadowing Task Message entering participant’s ears.
Participant repeat (speak) what s/he hears while listening – SHADOWING!
The participant’s speech is recorded for analysis.
Record Sound

57. Shadowing Task (Marslen-Wilson 1973)
Message played into ears (160 words/min):
Susan and Jill wanted…
Tone synchronized with start of every word
of message sent to recorder.
Shadowed Speech
Measure onset of each word shadowed relative to the tone.

58. Shadowing Task (Marslen-Wilson 1973) Susan and Jill wanted…
Time unfolding…
Message Played:
Susan and Jill wanted…
tone
Susan and Jill wanted…
Message Shadowed:
onset
time
Measure onset of each word shadowed relative to the tone.

59. Shadowing Task (Marslen-Wilson 1973) Finding:
About 10% of the population can shadow with 250ms delay.
Most people shadow between 800ms-1000ms.
Record Shadowed Message

60. Shadowing Task (Marslen-Wilson 1973)
You might ask: Are they really retrieving words and processing what they heard that quickly?
(1) Post-reading, participants were good at answering content questions.
(2) Errors found were structurally appropriate:
e.g. insertion of “that”
From:
It was beginning to be light enough so I could see…
To:
It was beginning to be light enough so that I could see…
Record Shadowed Message

61. In-Class Shadowing Demo
2 Volunteers
One to read a passage
One to shadow a passage