Right Hemisphere in Language Processing Coarse and Fine Coding

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Right Hemisphere in Language ProcessingCoarse
and Fine Coding
Ling 411 15
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Major nodes of a hypothesized functional word web
for a manipulable object
T
M
C
PP
PR
PA
V
Ignition from speech input
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Ignition from visual input
T
3
M
3
C
4
2
PP
3
4
PR
PA
V
1
1
4
Ignition from tactile input
1
T
3
M
C
4
2
PP
3
4
PR
3
PA
V
1
5
Ignition from conceptual input
T
2
M
2
1
C
3
PP
2
3
PR
2
PA
V
1
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RH Linguistic Functions

• Inference, Metaphor
• Coarse coding
• Music

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Some findings w.r.t. RH speech perception

• Vowel qualities
• Intonation
• Tones in tone languages

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Possible bases for RH/LH difference

• Higher ratio of white to gray matter in RH
• Therefore, higher degree of connectivity in RH
• Difference in dendritic branching
• Different density of interneurons
• Evoked potentials (EEG) are more diffuse over the
  RH than over LH

Beeman 257
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Anatomical differences between LH and RH

• Geschwind Levitsky (1968)
• 100 brain specimens examined
• Planum temporale
• Larger in LH 65
• Larger in RH 11
• About the same, both sides 24
• Correlates with shape of Sylvian fissure
• Shorter horizontal extent in RH

Goodglass 199360
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Experiments (described by Beeman)

• Words presented to rvf-LH or lvf-RH
• RH more active than LH
• Synonyms
• Co-members of a category table, bed
• Polysemy FOOT1 FOOT2
• Metaphorically related connotations
• Sustains multiple interpretations
• LH about same as RH
• Other associations baby-cradle
• LH more active than RH
• Choose verb associated with noun

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Patients with brain-damage

• Some patients with LH damage
• Cant name fruits but can say that they are
  fruits
• Patients with RH damage
• Impaired comprehension of metaphorical statements
• More difficulty producing words from a particular
  semantic category than producing words beginning
  with a particular letter (258)

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Imaging studies

• When listening to spoken discourse, cerebral
  blood flow increases in
• Wernickes area
• Brocas area
• RH homologues of Wernickes and Brocas areas
• More cerebral blood flow in RH when subjects read
  sentences containing metaphors than literal
  sentences

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Experiments on speech perception

• Dichotic listening normal subjects
• Right ear (i.e. LH) advantage for distinctions of
• Voicing
• Place of articulation
• Left hear (RH) advantage for
• Emotional tone of short sentences
• Sentences presented in which only intonation
  could be heard
• RH advantage for identifying sentence type
  declarative, question , or command

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Experiments on speech perception

• Split brain patients
• They hear a consonant
• Then written representations are presented
• Point to the one you heard
• rvf-LH exhibited strong advantage

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Patients with right-brain damage

• Posterior RH lesions result in deficits in
  interpreting emotional tone
• Anterior RH lesions abolish the ability to
  control the production of speech intonation

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Split-brain studies

• Isolated RH has ability to read single words
• But not as fast nor as accurate as LH
• Ability declines with increasing word length
• Lexical context does not assist letter
  identification
• In Japanese subjects
• RH is better at reading kanji than kana
• Kanji from Chinese characters
• Kana syllabic writing system
• LH is better at reading kana

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Musical abilities and the hemispheres

• Pitch, melody, intensity, harmony, etc. in RH
• Rhythm in LH
• Absolute pitch (if present) in LH temporal plane
• Musicians ability to analyze chord structures in
  LH
• Appreciation of chord harmony in RH
• Discrimination of local melody cues more in LH
• Timbre discrimination in anterior right temporal
  lobe
• Melody recognition in anterior right temporal
  lobe

Evidence from results of brain lesions/surgery,
from dichotic listening experiments, from Wada
test experiments, and from imaging
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An MSI study from Max Planck Institute
Levelt, Praamstra, Meyer, Helenius Salmelin,
J.Cog.Neuroscience 1998
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Right hemisphere in speech perception

• The primary substrate for speech perception is
  the left pSTP
• pSTP Heschls gyrus plus planum temporale
• Yet another type of conduction aphasia
• Some patients with damage to left pSTP show
  symptoms of conduction aphasia (Hickock 2000)
• Apparent paradox
• In conduction aphasia, comprehension is preserved
  
• Explanation
• Speech perception is subserved by pSTP in both
  hemispheres

(Hickock 2000 90)
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RH involvement in speech perceptionIsolated RH

• Evidence from tests of isolated RH
• Split-brain studies
• Wada test
• Sodium amytol, sodium barbitol
• Discrimination of speech sounds
• Comprehension of syntactically simple speech

(Hickok 2000 92)
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Caution Split-Brain Studies

• These patients are generally epileptics
• Usually the onset of seizures is several to many
  years before the surgery
• Often the onset of seizures was during childhood
• Therefore the brain has had time to adapt
  perhaps reorganize some linguistic functions

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RH involvement in speech perceptionIntra-operativ
e recording

• Evidence from intraoperative recording
• Sites found in STG of both hemispheres for
• Phoneme clusters
• Distinguishing speech from backwards speech
• Distinguishing mono- from polysyllabic words

(Hickok 2000 92-3)
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RH involvement in speech perceptionImaging

• Evidence from imaging
• PET
• fMRI
• MEG
• Subjects passively listen to speech
• Both hemispheres show activity
• More activity in LH
• Some evidence for differential contributions of
  the two hemispheres (Hickok Poeppel, another
  publication)

(Hickok 2000 93)
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Coarse and fine coding

• Coarsely coded node
• Responds to a relatively large range of values
• Finely coded node
• Responds to a narrow range
• Needed for sharp contrasts
• Examples
• Phonology
• Morphology
• Mathematics

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Receptive fields of nodes

• Every perceptual node has a receptive field
• Can be called its value
• The node is activated by tokens of that field
• Its function is to recognize input of that field
• Coarse coding receptive field is broad
• Fine coding receptive field is narrow

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Uses of coarse and fine coding

• Fine coding for
• Sharp contrasts
• Voiced vs. voiceless stops
• Edges in vision
• Coarse coding for
• Meanings with broad range of semantic properties
• General visual impressions

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Coarse and fine codingLow-level nodes

• Low-level near bottom of hierarchy
• Lowest level primary areas
• Lowest level nodes are coarse-coded
• At other low levels, coarse and fine coding
• Colors (visual cortex)
• Fine coding for fine color discrimination
• Coarse coding for range of color
• Frequencies (auditory cortex)
• Fine coding for fine pitch discrimination
• Coarse coding for range of pitches

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Inhibitory connections Based on Mountcastle
(1998)

• Columnar specificity is maintained by
  pericolumnar inhibition (190)
• Activity in one column can suppress that in its
  immediate neighbors (191)
• Inhibitory cells can also inhibit other
  inhibitory cells (193)
• Inhibitory cells can connect to axons of other
  cells (axoaxonal connections)
• Large basket cells send myelinated projections as
  far as 1-2 mm horizontally (193)

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The anatomy of lateral inhibition

• Inhibitory connections
• Extend horizontally to other columns in the
  vicinity
• These columns are natural competitors
• Enhances contrast

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Coarse coding at low levels

• Typical situation for sensory neurons
• Neurons fire..
• Occasionally at random even when not receiving
  activation
• More strongly when receiving activation
• More strongly yet when receiving a lot of
  activation
• Hence, low level nodes have broad receptive
  fields
• Locally, they are coarsely coded

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Typical Low-level Node Coarsely Coded
Responds to a range of inputs
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How to get fine coding

• Neurons (hence also columns, presumably) are
  inherently, locally, coarse-coded
• For linguistic processing we often need much
  greater precision fine coding
• Problem How to get finely coded nodes if neurons
  are inherently coarsely coded?

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Response curve of a coarsely coded node
Responds to a wide range of inputs
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Response curve of node A (coarsely coded)
Node A is coarsely coded for
Range of colors
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Response curve of node B (coarsely coded)
Node B is coarsely coded for
(Node A is coarsely coded for )
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Overlapping receptive fields
each individual representation (e.g. receptive
field) is inexact, or coarse, but the overall
system of overlapping representations can provide
precise interpretations. Mark Beeman (1998),
256
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Overlapping receptive fields
Node A
Node B
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Higher-level node C
C
Response curve of C Response curve of
B Response curve of A
A
B
Node C is more finely coded
A
B
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Enhance fine-coding with inhibition
Node C can be yet more finely coded by
receiving inhibitory inputs from nodes for
and
C
A
B
A
B
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Further enhancement by raising threshold
C
A
B
Threshold
A
B
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Coarse coding at higher levels

• A node with a large number of incoming
  connections and a relatively low threshold
• This arrangement allows it to respond to any of a
  broad range of situations
• Coarse coding is the usual situation at the
  conceptual level
• A concept node generally represents a category,
  not just a single thing
• Different members of the category, with differing
  features, activate the category node

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Coarse and fine codingHigh-level nodes

• High-level nodes concepts, meanings
• Coarse coding
• More coarse in RH
• Broad range of semantic properties
• In RH, not necessarily logical
• Fine coding
• Mainly in LH
• Narrow range of semantic properties

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A coarsely-coded category
The head node
CUP
T
MADE OF GLASS
CERAMIC
SHORT
HAS HANDLE
Therefore, the CUP node is activated by varying
combin-ations of a large range of properties
Properties
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Coarse coding and RH

• Coarse coding is particularly prominent in RH
• Beeman diffuse activation in RH (as opposed to
  focused activation in LH)

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Coarsely coded concept nodes

• Cups
• A great variety of cups activate the CUP node
• To different degrees
• Properties of prototypical cups activate the node
  more strongly
• Your grandmother
• A specific person, but a coarsely coded node
• Top of a hierarchical functional web
• Why coarsely coded?
• Wearing different clothes
• Doing different things
• Seen live or in a picture
• At different ages
• Etc.

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Summary Coarse and fine coding

• Low-level nodes (as in primary areas)
• Tend to be coarsely coded
• Upper-level nodes
• For course coding
• Large number of incoming links
• Low activation threshold
• For fine coding
• Threshold high in relation to number of incoming
  links
• Lateral inhibition

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