Right Hemisphere in Language Processing

Coarse and Fine Coding

Ling 411 – 15

Major nodes of a hypothesized functional word web for a manipulable object:

V

MC

T

PRPA

PP

Ignition from speech input

Ignition from visual input

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MC

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PRPA

PP

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4

43

3 3

1

2

Ignition from tactile input

V

MC

T

PRPA

PP

1

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43

3

3

1

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Ignition from conceptual input

V

MC

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PRPA

PP

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32

2

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2

RH Linguistic Functions

Inference, Metaphor Coarse coding Music

Some findings w.r.t. RH speech perception

Vowel qualities Intonation Tones in tone languages

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 LHBeeman 257

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 1993:60

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

Patients with brain-damage

Some patients with LH damage•Can’t 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)

Imaging studies

When listening to spoken discourse, cerebral blood flow increases in•Wernicke’s area

•Broca’s area

•RH homologues of Wernicke’s and Broca’s areas

More cerebral blood flow in RH when subjects read sentences containing metaphors than literal sentences

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

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

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

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

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

An MSI study from Max Planck Institute

Levelt, Praamstra, Meyer, Helenius & Salmelin, J.Cog.Neuroscience 1998

Right hemisphere in speech perception

The primary substrate for speech perception is the left pSTP• pSTP – Heschl’s 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)

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)

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

RH involvement in speech perceptionIntra-operative 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)

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)

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

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

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

Coarse and fine coding:Low-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

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)

The anatomy of lateral inhibition

Inhibitory connections Extend horizontally to other columns

in the vicinity•These columns are natural competitors

Enhances contrast

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

Typical Low-level Node: Coarsely Coded

Responds to a range of inputs

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?

Response curve of a coarsely coded node

Responds to a wide range of inputs

Response curve of node A (coarsely coded)

Range of colors

Node A is coarsely coded for

Response curve of node B (coarsely coded)

Node B is coarsely coded for(Node A is coarsely coded for )

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

Overlapping receptive fields

Node A

Node B

Higher-level node C

A B

C

A

B

Response curve of C

Response curve of B

Response curve of A Node C is more finely coded

Enhance fine-coding with inhibition

Node C can be yet more finely coded by receiving inhibitory inputs from nodes for

and

A B

C

A

B

Further enhancement by raising threshold

A B

C

A

B

Threshold

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

Coarse and fine coding:High-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

A coarsely-coded category

T

CUP

MADE OF GLASS

CERAMICSHORT

HAS HANDLE

Properties

The head node

Therefore, the CUP node is activated by varying combin-ations of a large range of properties

Coarse coding and RH

Coarse coding is particularly prominent in RH

Beeman: “diffuse activation” in RH (as opposed to “focused activation” in LH)

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.

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

end