Mental Lexicon

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Mental Lexicon

• All of your knowledge about words
• and you know a lot of words!
• Average college-educated adult
• Speaking vocabulary 75,000 - 100,000 words
• Recognition vocabulary is substantially larger
• You're not equally likely to use all of those
  words
• The 50 most common words make up
• 60 of the words we speak
• 45 of the words we write
• On average, you only say 10-15 words before
  repeating one

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10 Most Frequent English Words(counts out of
1,000,000 words)
Written Written Spoken Spoken
the 70,000 I 65,000
be 40,000 and 38,000
of the
and to (?)
a that
in you
he (she) 20,000 (6000) it
to (infin.) of
have a
to (prep) 11,000 know 15,000
Notice that most are function words rather than
content words
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Bottom-up Top-down Processing in Visual Word
Recognition
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Writing Systems

• Two basic types of writing systems
• Ones that indicate pronunciation
• Ones that do so less
• Systems that do represent pronunciation
• Alphabets - One character supposed to represent
  one sound
• ALL modern alphabets are derived from Phoenician
• Syllabaries - One character represents a whole
  syllable
• In most, 2 syllables that share a sound dont
  look anything alike
• Japanese hiragana ? /ka/ vs ? /ki/ vs ?
  /ku/
• But, Korean hangul has C V characters within
  syllable ?? /suzn/?
• Systems that represent pronunciation less
  directly
• Ideograms (pictograms) - One character represents
  a meaning
• Words with similar meanings usually share
  characters, even if pronunciation is completely
  different
• But often words that share a syllable that has
  different meanings in each word also share a
  character

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Word Boundaries

• Most (all?) languages using Roman alphabet put
  spaces between words
• But some other writing systems do not (e.g.
  Chinese, Japanese)
• - Sometimes ambiguous where word boundaries are
  (just as in speech)

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Bottom-up and Top-down Processingin Reading

• Some examples
• Detecting particular letters is less accurate in
  highly familiar words
• Proofreading is harder the more familiar the text

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Bottom-up and Top-down Processingin Reading

• Some examples
• Detecting particular letters is less accurate in
  highly familiar words
• Proofreading is harder the more familiar the text
• - ...
• Race Models of word recognition
• Top-down and bottom-up processes go on in
  parallel
• Racing with each other
• Whichever "finishes" first wins the race
• i.e. determines how you identify the word
• If bottom-up processing is hard because input is
  noisy, top-down wins
• If little help from context, bottom-up wins
• Decision Criterion Finish line in race
• How sure must you be that the input is a word
  before saying so?

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Disorders of Reading

• Patterns of Acquired Dyslexia have influenced
  theories and models of normal reading
• More than observations of any other kind of
  language deficit have influenced models of other
  aspects of normal language processing

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Surface Dyslexia(tends to occur in fluent
aphasics with posterior brain damage)

• Read regularly spelled words aloud ok
• Read nonwords aloud ok
• Tend to mispronounce irregularly spelled words
• They regularize them
• island gt /Izl?nd/
• pint gt /pInt/
• So, they seem to
• construct pronunciations via direct
  letter-to-sound mappings
• without retrieving knowledge about particular
  words' pronunciations

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Phonological Dyslexia(often no other
aphasia)(most similar of the acquired dyslexias
to developmental dyslexias)

• Read highly familiar words aloud ok, regardless
  of spelling regularity
• Trouble reading both less familiar words and
  non-words aloud
• Tend to pronounce them as similar-looking
  familiar words
• i.e., they lexicalize them
• forb gt fork
• moth gt mother
• border gt bread
• If the word they come up with happens to have an
  irregular spelling for its pronunciation, they
  pronounce it in the correct irregular way
• So, they seem to
• get into the neighborhood of words that look like
  what they see
• retrieve the pronunciation of one of the more
  familiar words in that neighborhood

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Deep Dyslexia(tends to occur in non-fluent
aphasics with anterior brain damage)

• Read content words aloud a lot better than
  function words
• Within content words, better on concrete,
  imageable ones
• Often can't read non-words at all, or may
  lexicalize them
• Errors sometimes semantically related, with no
  sound or spelling similarity
• ape gt monkey
• forest gt trees
• Errors sometimes visually related instead, or
  mixed visual and semantic
• scandal gt sandals
• orchestra gt sympathy
• So, they seem to (sometimes)
• get into the neighborhood of words with meanings
  like what they see
• then retrieve the pronunciation of another word
  in that neighborhood

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Modular vs Interactive Processing Systems

• Its obvious that both bottom-up and top-down
  processes contribute to the recognition of
  letters sounds words
• But how does top-down processing work?
• Interactive account
• Context knowledge guide actual perception of
  input
• vs
• Modular account ( post-perceptual, autonomous)
• Context knowledge influence choices among
  alternative candidates proposed by perceptual
  processes

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Localist
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Localist
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Localist
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Modular Account of Phoneme Restoration

• The connectionist account is interactive
• In contrast, a modular account says
• No top-down feedback from words to sounds
• Instead, system guesses there must have been s
  because that's what would make sense
• An unconscious decision process

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• Each of the next 3 slides has a list of letter
  strings
• Your task is to read through them as quickly as
  you can and count how many of them are words
• Raise your hand as soon as youre done

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• zyndc
• cnccl
• apple
• frgtd
• wrpts
• brat
• nxprd
• must
• lbdry
• other
• nrgln
• sfbdl
• war
• cloth
• dtrnp
• library
• stwsn
• mplfs

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• bant
• anger
• fold
• bagin
• pretser
• mash
• kalt
• magic
• lomp
• sinos
• arid
• hink
• radle
• track
• rean
• supper
• weth
• amol

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• brane
• leev
• want
• damp
• stane
• mair
• quick
• lowd
• heeter
• power
• wim
• pryse
• muther
• prefer
• koller
• heaven
• much
• prufe

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• Why were you slower on the second list than on
  the first, and even slower on the third list,
  even though there were 7 words in each list?
• Because the nonwords (NWs) grew progressively
  more word-like across the lists
• In list 1, the NWs were not even pronounceable
  and had illegal sequences of letters
• In list 2, the NWs were pronounceable and
  followed legal English spelling patterns
• In list 3, NWs all had the same pronunciation as
  a real word
• pseudohomophones

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Tasks Strategies

• You adopted different decision criteria in the 3
  different lists
• about how fully to process the letter strings
  before moving on to the next one
• So, sometimes the "distractors/fillers" in an
  experiment can matter a lot!
• Influence task-specific response strategies
  people can adopt
• Crucial to think very carefully about how
  participants could be doing the tasks we give
  them
• But also important to realize peoples intuitions
  about how theyre doing something are often not
  reliable

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• The rest of the slides here are ones I didnt get
  to in class

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• When you encounter a new word you dont know, you
  can tell a lot about it from
• its position in the sentence relative to other
  words you do know
• Syntax
• its prefixes and suffixes
• Morphology

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The JabberwockyLewis Carroll
One two! One two! And through and through The
vorpal blade went snicker-snack! He left it dead,
and with its head He went galumphing back. "And
hast thou slain the Jabberwock? Come to my arms,
my beamish boy! Oh frabjous day! Callooh!
Callay!" He chortled in his joy. 'Twas brillig,
and the slithy toves Did gyre and gimble in the
wabe All mimsy were the borogoves, And the mome
raths outgrabe. Adjectives, Nouns, Verbs
Parts of speech Syntactic categories

• 'Twas brillig, and the slithy toves
• Did gyre and gimble in the wabe
• All mimsy were the borogoves,
• And the mome raths outgrabe.
• "Beware the Jabberwock, my son!
• The jaws that bite, the claws that catch!
• Beware the Jubjub bird, and shun
• The frumious Bandersnatch!
• He took his vorpal sword in hand
• Long time the manxome foe he sought --
• So rested he by the Tumtum tree,
• And stood a while in thought.
• And, as in uffish thought he stood,
• The Jabberwock, with eyes of flame,
• Came whiffling through the tulgey wood,
• And burbled as it came!

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Morphology

• Many words have internal structure
• friend gt friendly gt unfriendly gt unfriendliest
• Morpheme smallest meaningful unit in language
• friend 1 morpheme
• unfriendliest 4 morphemes (end is not a
  morpheme in friend)
• Affixes prefixes, suffixes, infixes
• Free morphemes (friend, the) vs
• Bound morphemes (un-, -ly, -est)
• Lexical ( Content) morphemes (friend) vs
• Grammatical ( Function) morphemes (the, un-,
  -ly, -est)
• Allomorphs different versions of same morpheme
• (Remember allophones?)
• English plural /s/, /z/, /Iz/, /In/, ...
• English indefinite article a, an

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• Languages vary from having
• Many short simple words and using more of them
• (e.g. Chinese isolating)
• To having mostly long complex words and using few
  of them
• (e.g. Turkish, Hungarian agglutinative)
• English is somewhere in between
• Term Morphosyntax reflects the fact that the same
  kinds of relationships are coded morphologically
  in some languages syntactically in others

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Does Morphological StructureAffect Reading?

• People do seem to decompose complex words during
  reading
• Priming from regularly inflected morphological
  relatives can be equivalent to repetition priming
• e.g., believes primes believe just as much as
  believe primes itself
• But priming from irregularly inflected or
  derivational relatives is smaller
• e.g., believer doesnt prime believe as much as
  believe primes itself, nor does went prime go as
  much as go primes itself or as much as repeated
  primes repeat
• Even when what looks like a morpheme really isnt
• e.g., beak- and -er in beaker
• Get effects of frequencies of apparent
  subcomponents
• The frequency of the word beak (meaning a birds
  beak) influences response time to beaker even
  though its meaning is not a component of the
  meaning of beaker

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Eye Movements

• Two types of eye movements
• Smooth pursuit
• Long smooth movements
• Can only do this if eyes are following something
• Saccades
• Short jumps
• Most eye movements
• When eye is not moving fixations
• Reading consists of saccades and fixations
• Backward saccades regressions

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Eyetracking (Dual Purkinje Tracker)

• - Dim infrared light shines on eye
• - Reflections bounce back from different layers
  in eye
• Relative positions of different reflections show
  where eye is pointing
• Some other kinds of eyetrackers work in
  different ways

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Span of Fixation

• How much can you see during a single fixation?
• -
• It depends on
• your visual acuity
• your reading skill level
• how hard what youre reading is, overall
• how familiar the current and preceding and next
  words are
• how predictable the current and preceding and
  next words are
• ...
• how much of current word you could see on
  previous fixation

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What techniques could be used to answer this
question?

• Eyetracking
• Contingent display changes (McConkie Rayner)
• Moving window 1 special type
• Works because youre functionally blind during
  saccades
• so you dont see the change itself happen
• Need eyetracker computer fast enough to
  complete display changes before saccade ends
• Average saccade only lasts 10-20 msec

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The person who is fixating where the is does
not see the xxxs
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• So, how far ahead does the eye see?
• Get different answers when
• Ask people what they consciously notice
• vs
• See what affects their eye movement patterns

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Contingent Display Change

• Idea
• If something peripheral changes just before the
  eye reaches it
• during a saccade, so dont see change itself
  happening
• If the eye stays on the changed thing longer than
  when thats what was there all along
• what was originally there must have been "seen"
  peripherally
• Answer to the question
• You can sometimes get wordshape and initial
  letter info as far ahead as 10-14 characters
• But you have to get as close as 6 characters
  before you detect the "wordness" of the next unit

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Semantic Priming(Phenomenon Tool)

• ... ...
• arm arm
• kitchen kitchen
• tree tree
• Related prime gtdoctor actor lt Unrelated prime
• nurse ltTargetgt nurse
• floor floor
• ... ...
• In a priming experiment
• Some people see nurse immediately after doctor in
  a list of words
• Related condition
• Others see nurse after an unrelated word like
  actor
• Unrelated condition
• - Notice that target word is identical across
  conditions, so important word properties like
  frequency length are perfectly controlled
• People respond faster, on average, in Related
  condition
• Priming ( facilitation)