Shanghai International Studies University
5 November 2010
Categories in the Brain
Prototypicality, Subcategorization, Thinking
Sydney Lamb
Rice University
lamb@rice.edu
“to know is to categorize”
Jeffrey Ellis
Topics in this presentation
•
•
•
•
Phenomena associated with categories
Information in the brain
Six Hypotheses
Explaining the phenomena associated with categories
Topics
•
•
•
•
Phenomena associated with categories
Information in the brain
Six Hypotheses
Explaining the phenomena associated with categories
Phenomena associated with categories
1.
2.
3.
4.
5.
6.
7.
No small set of defining features (with rare exceptions)
Fuzzy boundaries
Prototypical members and peripheral members
Subcategories, and sub-subcategories, in hierarchical chains
Categories are in the mind, not in the real world
Categories and their memberships vary from one
language/culture system to another
Categories influence thinking, in both appropriate and
inappropriate ways
Phenomena associated with categories: 1
1.
No small set of defining features (with rare exceptions)
–
The feature-attribute model fails
• Works for some mathematical objects, but
doesn’t apply to the way people’s cognitive
systems apprehend most things
• Example: CUP
Phenomena associated with categories: 2
1.
No small set of defining features (with rare exceptions)
2.
Fuzzy boundaries
–
Example: VEHICLE
• Car, truck, bus
• Airplane?
• Boat?
• Toy car, model airplane?
• Raft?
• Roller skate?
• Snowboard?
Fuzzy Categories
• No fixed boundaries
• Membership comes in degrees
– Prototypical
– Less prototypical
– Peripheral
– Metaphorical
• The property of fuzziness relates closely
to the phenomenon of prototypicality
Phenomena associated with categories: 3
1.
2.
3.
No small set of defining features (with rare exceptions)
Fuzzy boundaries
Prototypical members and peripheral members
–
–
Prototypical
• CAR, TRUCK, BUS
Peripheral:
•
–
AIRPLANE, TOY CAR, RAFT, ROLLER SKATE, etc.
Varying degrees of peripherality
Prototypicality phenomena
• The category BIRD
– Some members are prototypical
• ROBIN, SPARROW
– Others are peripheral
• EMU, PENGUIN
• The category VEHICLE
– Prototypical: CAR, TRUCK, BUS
– Peripheral: ROLLER SKATE, HANG GLIDER
Phenomena associated with categories: 4
1.
2.
3.
4.
No small set of defining features (with rare exceptions)
Fuzzy boundaries
Prototypical members and peripheral members
Subcategories, and sub-subcategories, in hierarchical
chains
–
ANIMAL – MAMMAL – CARNIVORE – CANINE – DOG –TERRIER
– JACK RUSSELL TERRIER – EDDIE
–
Each subcategory has the properties of the category plus
additional properties
Smallest subcategory has the most properties
–
Phenomena associated with categories: 5
1.
2.
3.
4.
No small set of defining features (with rare exceptions)
Fuzzy boundaries
Prototypical members and peripheral members
Subcategories, and sub-subcategories, in hierarchical chains
5.
Categories are in the mind, not in the real world
–
–
In the world, everything
• is unique
• lacks clear boundaries
• changes from day to day (even moment to moment)
Whorf: “kaleidoscopic flux”
Phenomena
associated with categories: 6
1.
No small set of defining features (with rare exceptions)
2.
3.
4.
5.
6.
Fuzzy boundaries
Prototypical members and peripheral members
Subcategories, and sub-subcategories, in hierarchical chains
Categories are in the mind, not in the real world
Categories and their memberships vary from one
language/culture system to another
English:
bell
French:
cloche
(of a church)
clochette
(on a cow)
sonnette
(of a door)
grelot
(of a sleigh)
timbre (on a desk)
glas
(to announce a death)
Phenomena associated with categories - 7
1.
2.
3.
4.
5.
6.
7.
No small set of defining features (with rare exceptions)
Fuzzy boundaries
Prototypical members and peripheral members
Subcategories, and sub-subcategories, in hierarchical chains
Categories are in the mind, not in the real world
Categories and their memberships vary from one language/culture system
to another
Categories influence thinking, in both appropriate and
inappropriate ways (B.L. Whorf)
Why?
1.
2.
3.
4.
5.
6.
7.
No small set of defining features (with rare exceptions)
Fuzzy boundaries
Prototypical members and peripheral members
Subcategories, and sub-subcategories, in hierarchical chains
Categories are in the mind, not in the real world
Categories and their memberships vary from one
language/culture system to another
Categories influence thinking, in both appropriate and
inappropriate ways
Why?
Answer: Because of the structure of the brain
1.
2.
3.
4.
5.
6.
7.
No small set of defining features (with rare exceptions)
Fuzzy boundaries
Prototypical members and peripheral members
Subcategories, and sub-subcategories, in hierarchical chains
Categories are in the mind, not in the real world
Categories and their memberships vary from one
language/culture system to another
Categories influence thinking, in both appropriate and
inappropriate ways
Topics
•
•
•
•
Phenomena associated with categories
Information in the brain
Six Hypotheses
Explaining the phenomena associated with categories
How to explain?
• We have to examine how our information about
categories is represented in the brain
• The brain is where our linguistic and cultural knowledge
is represented
• This recommendation is in line with a suggestion first
made to linguists by Norman Geschwind in 1964
– Geschwind: a great neurologist
– Said that linguists should consider brain struc\ture
Sources of information about the brain
• Aphasiology
– Research findings during a century-and-a-half
• Brain imaging
• Neuroanatomy
• Other research in neuroscience
– E.g., Mountcastle, Perceptual Neuroscience (1998)
Some things that are now well established
• The brain is a network
– Composed, ultimately, of neurons
• Neurons are interconnected
– Axons (with branches)
– Dendrites (with branches)
• Activity travels along neural pathways
– Cortical neurons are clustered in columns
• Columns come in different sizes
– The smallest: minicolumn – 70-110 neurons
• Each minicolumn acts as a unit
– When it becomes active all its neurons are active
• Locations of various kinds of “information”
– Visual, auditory, tactile, motor, …
Deductions from known facts
• Everything represented in the brain has the form of a
network
– (the “human information system”)
• Therefore a person’s linguistic and conceptual system is a
network
– (part of the information system)
• Every lexeme and every concept is a sub-network
– Term: functional web (Pulvermüller 2002)
Concepts and percepts:
Cortical representation
•
•
Percept: one sensory modality
– Locations are known
• Auditory: temporal lobe
• Visual: occipital lobe
• Somatosensory: parietal lobe
Concept: more than one sensory modality
– Higher level
– Angular gyrus, (?)temporal lobe, (?)SMG
Example: The concept DOG
• We know what a dog looks like
– Visual information, in occipital lobe
• We know what its bark sounds like
– Auditory information, in temporal lobe
• We know what its fur feels like
– Somatosensory information, in parietal lobe
• All of the above..
– constitute perceptual information
– are subwebs with many nodes each
– have to be interconnected into a larger web
– along with further web structure for conceptual information
Topics
•
•
•
•
Phenomena associated with categories
Information in the brain
Six Hypotheses
Explaining the phenomena associated with categories
Topic 3
• Six Hypotheses
– Functional webs
– Cortical Columns
– Nodal specificity
• Adjacency
– Extrapolation to humans
• And to linguistic and conceptual structure
– Hierarchy in functional webs
– Cardinal nodes
Hypothesis I: Functional Webs
•
•
A concept is represented as a functional web
Spread over a wide area of cortex
– Includes perceptual information
– As well as specifically conceptual information
• For nominal concepts, mainly in
– Angular gyrus
– (?) For some, middle temporal gyrus
– (?) For some, supramarginal gyrus
Building a model of a functional web:
First steps
Each node in this diagram
represents the cardinal node* of a
subweb of properties
T
C
For example
M
V
C – Conceptual
M – Motor
T – Tactile
V – Visual
*to be defined in a moment!
Add phonological recognition
For example, FORK
C
T
M
P
V
These are all
cardinal nodes –
each is supported
by a subweb
Labels for Properties:
C – Conceptual
M – Motor
P – Phonological image
T – Tactile
V – Visual
The phonological image
of the spoken form [fork]
(in Wernicke’s area)
Add node in primary auditory area
For example, FORK
C
T
M
P
PA
V
Labels for Properties:
C – Conceptual
M – Motor
P – Phonological image
PA – Primary Auditory
T – Tactile
V – Visual
Primary Auditory: the cortical structures in the primary
auditory cortex that are activated when the ears receive
the vibrations of the spoken form [fork]
Add node for phonological production
For example, FORK
C
T
M
P
PP
PA
Arcuate fasciculus
V
Labels for Properties:
C – Conceptual
M – Motor
P – Phonological image
PA – Primary Auditory
PP – Phonological Production
T – Tactile
V – Visual
Part of the functional web for FORK
(showing cardinal nodes only)
Each node
shown
here is the
cardinal
node of a
subweb
T
M
PP
C
P
PA
V
For example,
the cardinal
node of the
visual subweb
An activated functional web
(with two subwebs partly shown)
T
C
PP
PR
PA
M
C – Cardinal concept node
M – Memories
PA – Primary auditory
PP – Phonological production
PR – Phonological recognition
T – Tactile
V – Visual
V
Visual features
Ignition of a functional web from visual input
T
C
PR
Art
PA
M
V
Ignition of a functional web from visual input
T
C
PR
Art
PA
M
V
Ignition of a functional web from visual input
T
C
PR
Art
PA
M
V
Ignition of a functional web from visual input
T
C
PR
Art
PA
M
V
Ignition of a functional web from visual input
T
C
PR
Art
PA
M
V
Ignition of a functional web from visual input
T
C
PR
Art
PA
M
V
Ignition of a functional web from visual input
T
C
PR
Art
PA
M
V
Ignition of a functional web from visual input
T
C
PR
Art
PA
M
V
Ignition of a functional web from visual input
T
C
PR
Art
PA
M
V
Ignition of a functional web from visual input
T
C
PR
Art
PA
M
V
Ignition of a functional web from visual input
T
C
PR
Art
PA
M
V
Ignition of a functional web from visual input
T
C
PR
Art
PA
M
V
Ignition of a functional web from visual input
T
C
PR
Art
PA
M
V
Ignition of a functional web from visual input
T
C
PR
Art
PA
M
V
Speaking as a response to ignition of a web
T
C
PR
Art
PA
M
V
Speaking as a response to ignition of a web
T
C
PR
Art
PA
M
V
Speaking as a response to ignition of a web
T
C
PR
Art
PA
M
From here (via subcortical
structures) to the muscles that
control the organs of articulation
V
An MEG study from Max Planck Institute
Hypothesis II:
Nodes as Cortical Columns
• Information is represented in the cortex in the form of functional
webs (Hypothesis I)
– A functional web is a network within the cortical network as a
whole
• consisting of nodes and their interconnections
– connections represented in graphs as lines
• Nodes are implemented as cortical columns
• The interconnections are represented by inter-columnar neural
connections and synapses
– Axonal fibers
– Dendritic fibers
The node as a cortical column
• The properties of the cortical column are approximately
those described by Vernon Mountcastle
– Mountcastle, Perceptual Neuroscience, 1998
• Additional properties of columns and functional webs
can be derived from Mountcastle’s treatment together
with neurolinguistic findings
– Method: “connecting the dots”
• Hypothesis IV: (Coming Soon!)
“[T]he effective unit of operation…is not the single
neuron and its axon, but bundles or groups of cells and
their axons with similar functional properties and
anatomical connections.”
Vernon Mountcastle, Perceptual
Neuroscience (1998), p. 192
The Cerebral Cortex – coronal section
 Grey matter
• Columns of neurons
 White matter
• Inter-column
connections
Evidence for columns
• Experiments on living cats, monkeys, rats
• Microelectrode penetrations in cortex
• If perpendicular to cortical surface
– Neurons all of same response properties
• If not perpendicular
– Neurons of different response properties
• Conclusion: All neurons of a single column respond to stimuli
– alike
– and differently from those of adjacent columns
Microelectrode penetrations in the
paw area of a cat’s cortex
Columns for orientation of lines (visual cortex)
Microelectrode
penetrations
K. Obermayer & G.G. Blasdell, 1993
The (Mini)Column
• Width is about (or just larger than) the diameter of a single
pyramidal cell
– About 30–50 m in diameter
• Extends thru the six cortical layers
– Three to six mm in length
– The entire thickness of the cortex is accounted for by the
columns
• Roughly cylindrical in shape
• If expanded by a factor of 100, the dimensions would correspond
to a tube with diameter of 1/8 inch and length of one foot
Cortical Column Structure
• Minicolumn 30-50 microns diameter
• Recurrent axon collaterals of pyramidal neurons
activate other neurons in same column
• Inhibitory neurons can inhibit neurons of neighboring
columns
– Function: contrast
• Excitatory connections can activate neighboring
columns
– In this case we get a bundle of contiguous
columns acting as a unit
Cortical minicolumns: Quantities
•
•
•
•
•
•
Diameter of minicolumn: 30 microns
Neurons per minicolumn: 70-110 (avg. 75-80)
Minicolumns/mm2 of cortical surface: 1460
Minicolumns/cm2 of cortical surface: 146,000
Neurons under 1 sq mm of cortical surface: 110,000
Approximate number of minicolumns in Wernicke’s
area: 2,920,000 (at 20 sq cm for Wernicke’s area)
Adapted from Mountcastle 1998: 96
Topological essence of cortical structure
(known facts from neuroanatomy)
• The thickness of the cortex is entirely accounted for by
the columns
• Hence, the cortex is an array of nodes
– A two-dimensional structure of interconnected nodes
(columns)
• Third dimension for
– Internal structure of the nodes (columns)
– Cortico-cortical connections (white matter)
Nodal interconnections
(known facts from neuroanatomy)
• Nodes (columns) are connected to
– Nearby nodes
– Distant nodes
• Connections to nearby nodes are either excitatory or inhibitory
– Via horizontal axons (through gray matter)
• Connections to distant nodes are excitatory only
– Via long (myelinated) axons of pyramidal neurons
Simplified model of minicolumn I:
Activation of neurons in a column
Other
cortical
locations
Cell Types
II
III
Pyramidal
Spiny
Stellate
Thalamus
IV
Inhibitory
Connections to
neighboring
columns not
shown
V
VI
Subcortical
locations
Simplified model of minicolumn II:
Inhibition of competitors
Other
cortical
locations
Cell Types
II
III
Pyramidal
Spiny
Stellate
Thalamus
IV
Inhibitory
V
VI
Cells in
neighboring
columns
Local and distal connections
excitatory
inhibitory
Findings relating to columns
(Mountcastle, Perceptual Neuroscience, 1998)
• The column is the fundamental module of perceptual systems
– probably also of motor systems
• This columnar structure is found in all mammals that have
been investigated
• The theory is confirmed by detailed studies of visual, auditory,
and somatosensory perception in living cat and monkey brains
Hypothesis III: Nodal Specificity in
functional webs
• Every node in a functional web has a specific function
• The nodes in each area of a functional web
– Constitute a subweb
– Their function fits the portion of cortex in which
they are located
• For example,
– Phonological recognition in Wernicke’s area
– Visual subweb in occipital and lower temporal lobe
– Tactile subweb in parietal lobe
– Each node of a subweb also has a specific function
within that of the subweb
Support for Nodal Specificity: the paw
area of a cat’s cortex
Column (node) represents
specific location on paw
Support for Nodal Specificity:
Columns for orientation of lines (visual cortex)
Microelectrode
penetrations
K. Obermayer & G.G. Blasdell, 1993
Hypothesis III(a): Adjacency
• Nodes of related function are in adjacent locations
– More closely related function, more closely adjacent
• Examples:
– Adjacent locations on cat’s paw represented by
adjacent cortical locations
– Similar line orientations represented by adjacent
cortical locations
Support for Nodal adjacency: the paw
area of a cat’s cortex
Adjacent column in cortex
for adjacent location on paw
Extrapolation to Language?
• Our knowledge of cortical columns comes mostly from
studies of perception in cats, monkeys, and rats
• Such studies haven’t been done for language
– Cats and monkeys don’t have language
– That kind of neurosurgical experiment isn’t done on
human beings
• Are they relevant to language anyway?
– Relevant if language uses similar cortical structures
– Relevant if linguistic functions are like perceptual
functions
Hypothesis IV: Extrapolation to Humans
• Hypothesis: The findings about cortical structure and
function from experiments on cats, monkeys, and rats can
be extrapolated to human cortical structure and function
• In fact, this hypothesis is simply assumed to be valid by
neuroscientists
• Why? We know from neuroanatomy that, locally,
– Cortical structure is relatively uniform across mammals
– Cortical function is relatively uniform across mammals
Hypothesis IV(a):
Linguistic and conceptual structure
• Hypothesis IV(a): The extrapolation can be extended to
linguistic and conceptual structures and functions
• Why?
– Local uniformity of cortical structure and function across
all human cortical areas except for primary areas
• Primary visual and primary auditory are known to
have specialized structures, across mammals
• Higher level areas are – locally – highly uniform
Objection
• Cats and monkeys don’t have language
• Therefore language must have unique properties of its
structural representation in the cortex
• Answer: Yes, language is different, but
– The differences are a consequence not of different
(local) structure but differences of connectivity
– The network does not have different kinds of
structure for different kinds of information
• Rather, different connectivities
Hypothesis V:
Hierarchy in functional webs
• A functional web is hierarchically organized
– Bottom levels in primary areas
– Lower levels closer to primary areas
– Higher (more abstract) levels in
• Associative areas – e.g., angular gyrus
• Executive areas – prefrontal
• These higher areas are much larger in
humans than in other mammals
• Hypothesis V(a): Each subweb is likewise
hierarchically organized
Properties of Hierachy
• Each level has fewer nodes than lower
levels, more than higher levels
– Compare the organization of
management of a corporation
• Top level has just one node
– Compare the “CEO”
Hypothesis VI:
Cardinal nodes
• Every functional web has a cardinal node
– At the top of the entire functional web
– Unique to that concept
– For example, C/cat/ at “top” of the web for CAT
• Hypothesis VI(a):
– Each subweb likewise has a cardinal node
• At the top level of the subweb
• Unique to that subweb
• For example, V/cat/
– At the top of the visual subweb
Cardinal nodes of a functional web
Some of the cortical structure relating to fork
Each
node
shown
here is
the
cardinal
node of
a
subweb
Cardinal
node of the
whole web
T
M
PP
C
P
PA
V
Cardinal
node of
the visual
subweb
(Part of) the functional web for CAT
The cardinal node for the
entire functional web
T
C
P
A
M
V
Cardinal nodes of
the subwebs
Support for the cardinal node hypothesis - 1
It follows from the hypotheses of nodal specificity and
hierarchy
– A hierarchy must have a highest level
– The node at this level must have a specific function
2. It is needed for ignition of the whole web from activation
of part of it
– For example, to activate the phonological
representation from the visual
3. It is automatically recruited in learning anyway,
according to the Hebbian learning hypothesis
1.
More support for cardinal nodes
Example: FORK
• The web as a whole represents the concept
– For example, the concept FORK
• The whole can evidently be activated by any part of
•
the network
– From seeing a fork
– From eating with a fork
– Etc.
The cardinal node provides the coordinated
organization that makes such reactivation possible
Reactivating the functional web
• When the cardinal node (the integrating node) is
activated, it can activate the whole (distributed)
functional web
– Without it, how would that be possible?
– E.g., activating conceptual and perceptual
properties of cat upon hearing the word cat
– From phonological recognition to concepts
– From visual image to phonological representation
Cardinal nodes and the linguistic sign
•
•
Connection of conceptual to phonological representation
Consider two possibilities
1. A cardinal node for the concept connected to a
cardinal node for the phonological image
2. No cardinal nodes: multiple connections between
concept representation and phonological image
• supported by Pulvermüller (2002)
Implications of possibility 2
•
•
•
•
No cardinal nodes: multiple connections between
concept representation and phonological image
I.e., different parts of meaning connected to
different parts of phonological image
Consider fork
– Maybe /f-/ connects to the shape?
– Maybe /-or-/ connects to the feeling of holding
a fork in the hand?
– Maybe /-k/ connects to the knowledge that
fork is related to knife?
Conclusion: Possibility 2 must be rejected
Topics
•
•
•
•
Phenomena associated with categories
Information in the brain
Six Hypotheses
Explaining the phenomena associated with categories
REVIEW
Phenomena associated with categories
1.
2.
3.
4.
5.
6.
7.
No small set of defining features (with rare exceptions)
Fuzzy boundaries
Prototypical members and peripheral members
Subcategories, and sub-subcategories, in hierarchical chains
Categories are in the mind, not in the real world
Categories and their memberships vary from one
language/culture system to another
Categories influence thinking, in both appropriate and
inappropriate ways
REVIEW
How to explain?
• Description is fine, but its only a start
• Next step: Explanation
• How to explain?
– By answering the question of how categories
are represented in the brain
Phenomena associated with categories: 1-3
No small set of defining features (with rare exceptions)
– Example: CUP
– More realistic alternative: radial categories
2. Fuzzy boundaries
– Example: VEHICLE
3. Prototypical members and peripheral members
1.
–
VEHICLE
•
Prototypical:
–
•
•
CAR, TRUCK, BUS
Peripheral:
–
AIRPLANE, TOY CAR, RAFT, ROLLER SKATE, etc.
–
Varying degrees of peripherality
These three phenomena are interdependent
How do radial categories work?
• Different connections have different strengths (weights)
• More important properties have greater strengths
• For CUP,
– Important (but not necessary!) properties:
• Short (as compared with a glass)
• Ceramic
• Having a handle
• Cups with these properties are more prototypical
The properties of a category
have different weights
The cardinal node
CUP
T
MADE OF GLASS
SHORT
CERAMIC
The properties
are represented
by nodes which
are connected to
lower-level nodes
HAS HANDLE
Nodes have activation thresholds
• The node will be activated by any of many different
combinations of properties
• The key word is enough – it takes enough activation
from enough properties to satisfy the threshold
• The node will be activated to different degrees by
different combinations of properties
– When strongly activated, it transmits stronger
activation to its downstream nodes.
Prototypical exemplars provide
stronger and more rapid activation
The cardinal node
Activation threshold
(can be satisfied to
varying degrees)
CUP
T
MADE OF GLASS
SHORT
CERAMIC
Stronger
connections carry
more activation
HAS HANDLE
Explaining Prototypicality
• Cardinal category nodes get more activation from the
prototypical exemplars
– More heavily weighted property nodes
• E.g., FLYING is strongly connected to BIRD
– Property nodes more strongly activated
• Peripheral items (e.g. EMU) provide only weak activation,
weakly satisfying the threshold (emus can’t fly)
• Borderline items may or may not produce enough
activation to satisfy threshold
Activation of different sets of properties produces
greater or lesser satisfaction of the activation threshold
of the cardinal node
CUP
Inhibitory
connection
MADE OF GLASS
SHORT
CERAMIC
HAS HANDLE
More important properties have stronger
connections, indicated here by thickness of lines
Explaining prototypicality: Summary
•
•
•
•
Variation in strength of connections
Many connecting properties of varying strength
Varying degrees of activation
Prototypical members receive stronger activation from
more associated properties
• BIRD is strongly connected to the property FLYING
– Emus and ostriches don’t fly
– But they have some properties connected with BIRD
– Sparrows and robins do fly
• And as commonly occurring birds they have been
experienced often, leading to entrenchment –
stronger connections
Phenomena associated with categories: 4
1.
2.
3.
4.
No small set of defining features (with rare
exceptions)
Fuzzy boundaries
Prototypical members and peripheral members
Subcategories, and sub-subcategories, in
hierarchical chains
–
ANIMAL – MAMMAL – CARNIVORE – CANINE – DOG –
TERRIER – JACK RUSSELL TERRIER – EDDIE
–
Each subcategory has the properties of the
category plus additional properties
Smallest subcategory has the most properties
–
How to explain?
Perceptual Neuroscience
• Hypothesis IV: Extrapolation
• Hypothesis IV(a): Extrapolation can be extended to
linguistic and conceptual structures
• Why? Cortical structure, viewed locally, is
– Uniform across mammalian species
– Uniform across different cortical regions
• Exceptions in primary visual and primary auditory
areas
• Different cortical regions have different functions
– because of differences in connectivity
– not because of differences in structure
REVIEW
In particular..
• Cortical structure and function, locally, are
essentially the same in humans as in cats and
monkeys and rats
• Moreover, in humans,
– The regions that support language have the
same structure locally as other cortical regions
Uniformity of cortical function
• Claims:
– Locally, all cortical processing is the same
– The apparent differences of function are consequences
of differences in larger-scale connectivity
• Conclusion (if the claim is supported):
– Understanding language, even at higher levels, is
basically a perceptual process
Local uniformity follows from
the basic connectionist claim
•
•
•
Lines and nodes (i.e., columns) are
approximately the same all over
Uniformity of cortical structure
– Same kinds of columnar structure
– Same kinds of neurons
– Same kinds of connections
Conclusion: Different areas have different
functions because of what they are connected to
Cortical columns cannot store symbols
• They only
– Receive activation
– Maintain activation
– Inhibit competitors
– Transmit activation
• Important consequence:
– We have linguistic information represented
in the cortex without the use of symbols
– It’s all in the connectivity
• Challenge:
– How?
Columnar Functions:
Integration and Broadcasting
• Integration: A column is activated if it receives
enough activation from
– Other columns
– Thalamus
• Can be activated to varying degrees
• Can keep activation alive for a period of time
• Broadcasting: An activated column transmits
activation to other columns
– Exitatory
– Inhibitory
• Learning: adjustment of connection strengths
and thresholds
Integration and Broadcasting
 Broadcasting
• To multiple locations
• In parallel
 Integration
Integration and Broadcasting
Broadcasting
Integration
Now I’ll tell my friends!
Wow, I got activated!
REVIEW
Conceptual systems and perceptual systems
• Likewise, conceptual systems in humans evidently use
the same structures as perceptual systems
• Therefore it is not too great a stretch to suppose that
experimental findings on the structure of perceptual
systems in monkeys can be applied to an understanding
of the structure of conceptual systems of human beings
• In particular to the structures of conceptual categories
Findings of Mountcastle:
Columns of different sizes for
categories and subcategories
• Minicolumn
– The smallest unit
– 70-110 neurons
• Functional column
– Variable size – depends on experience
– Intermediate between minicolumn and
maxicolumn
• Maxicolumn (a.k.a. column)
– 100 to a few hundred minicolumns
• Hypercolumn
– Several contiguous maxicolumns
Hypercolums: Modules of maxicolumns
A visual area
in temporal
lobe of a
macaque
monkey
Perceptual subcategories and
columnar subdivisions of larger columns
• Nodal specificity applies for maxicolumns as well
as for minicolumns
• The adjacency hypothesis likewise applies to
larger categories and columns
– Adjacency applies for adjacent maxicolumns
• Subcategories of a category have similar function
– Therefore their cardinal nodes should be in
adjacent locations
Functional columns
• The minicolumns within a maxicolumn respond to a
common set of features
• Functional columns are intermediate in size between
minicolumns and maxicolumns
• Different functional columns within a maxicolumn are
distinct because of non-shared additional features
– Shared within the functional column
– Not shared with the rest of the maxicolumn
Mountcastle: “The neurons of a [maxi]column have
certain sets of static and dynamic properties in common,
upon which others that may differ are superimposed.”
Similarly..
• Neurons of a hypercolumn may have similar response
features, upon which others that differ may be superimposed
• Result is maxicolumns in the hypercolumn sharing certain
basic features while differing with respect to others
• Such maxicolumns may be further subdivided into functional
columns on the basis of additional features
• That is, columnar structure directly maps categories and
subcategories
(!)
Hypercolumns: Modules of maxicolumns
A visual area
in the
temporal lobe
of a macaque
monkey
Category
(hypercolumn)
Subcategory
(can be further
subdivided)
Category representations in the cortex
• Hypercolumn
• Supercategory
• Maxicolumn
• Category
• Functional column
• Subcategory
• Sub-functional column
• Sub-subcategory
Hypothesis applied to conceptual categories
• A whole maxicolumn gets activated for a category
– Example: BEAR
• Different functional columns within the maxicolumn
for subcategories
– BROWN BEAR, GRIZZLY, POLAR BEAR, etc.
• Adjacent maxicolumns for categories related to BEAR
(sharing various features)
– I.e. , other carnivores
• Similarly, CUP has a column surrounded by columns
for other drinking vessels
Phenomena associated with categories: 5
1.
2.
3.
4.
No small set of defining features (with rare exceptions)
Fuzzy boundaries
Prototypical members and peripheral members
Subcategories, and sub-subcategories, in hierarchical chains
5.
Categories are in the mind, not in the real world
–
–
In the world, everything
• is unique
• lacks clear boundaries
• changes from day to day (even moment to moment)
Whorf: “kaleidoscopic flux”
REVIEW
Phenomena associated with categories: 6
1.
2.
3.
4.
5.
6.
No small set of defining features (with rare exceptions)
Fuzzy boundaries
Prototypical members and peripheral members
Subcategories, and sub-subcategories, in hierarchical chains
Categories are in the mind, not in the real world
Categories and their memberships vary from one
language/culture system to another
English:
bell
French:
cloche
clochette
sonnette
grelot
timbre
glas
(of a church)
(on a cow)
(of a door)
(of a sleigh)
(on a desk)
(to announce a death)
Phenomena associated with categories - 7
1.
2.
3.
4.
5.
6.
7.
No small set of defining features (with rare exceptions)
Fuzzy boundaries
Prototypical members and peripheral members
Subcategories, and sub-subcategories, in hierarchical chains
Categories are in the mind, not in the real world
Categories and their memberships vary from one language/culture system
to another
Categories influence thinking, in both appropriate and
inappropriate ways
–
B.L. Whorf
These phenomena (5-7) are interrelated
Categories are in the mind, not in the real world
6. Categories and their memberships vary from
one language/culture system to another
7. Categories influence thinking, in both
appropriate and inappropriate ways
– B.L. Whorf
5.
Pertinent neuroanatomical findings:
Bidirectional Processing
• An established fact of neuroanatomy:
– A connection from point A to point B in the
cortex is generally accompanied by a
connection from point B to point A
• Separate fibers (axons): (1) A to B, (2) B to A
• In short, cortico-cortical connections are
generally bidirectional
Bidirectional processing and inference
These connections
are bidirectional
CUP
T
MADE OF GLASS
SHORT
CERAMIC
HANDLE
Separate fibers for the
two directions; shown as
one line in the notation
Bidirectional processing and inference
Thought process:
CUP
T
SHORT
HANDLE
1. The cardinal
concept node is
activated by a subset
of its property nodes
2. Feed-backward
processing activates
other property nodes
Consequence:
We “apprehend”
properties that are not
actually perceived
Category Structure and Inference
Category
T
Consequence:
A
If A and B,
then E and F
B
C
Properties
F
D
E
Examples
• Looks like a duck
– Probably quacks
• Ceramic, cup-shaped, handle
– Probably holds coffee (without breaking)
• Dark clouds, thunder
– It’s going to rain
• ATM
– Probably has money
Another hypothesis of Whorf
• Grammatical categories of a language influence the
thinking of people who speak the language
• Can we explain this too in terms of brain structure?
Mechanisms of operation
Entrenchment
– Strengthening of connections through
repeated activation
• An automatic brain process
• Important in learning
2. Reverberation of activation
3. Priming
4. Language as a major means of learning
conceptual and perceptual distinctions
1.
Entrenchment and thinking: a mechanism
• Connections become stronger with use
– (entrenchment)
• Grammatical categories make speakers
constantly heed selected phenomena
• Connections for phenomena which speakers
must constantly heed..
– Will be repeatedly traversed
– Therefore will get progressively stronger
Thinking: Reverberating Activation
 Speaking and thinking in English:
• Reverberating activation among categories
and images of English
 Thinking in German or Spanish or Yucatec
• Reverberating activation among categories
and images of German or Spanish or Yucatec
“When I speak Indian, I think differently”
Wallace Chafe’s Oneida informant
Example: Grammatical gender
• Does talking about inanimate objects as if they were
masculine or feminine actually lead people to think of
inanimate objects as having a gender?
• Could the grammatical genders assigned to objects
by a language influence people’s mental
representation of objects?
Boroditsky (2003)
Plausibility of the possibility
• Children learning to speak a language with
grammatical gender may suppose that
gender indicates a meaningful distinction
between types of objects
• Other grammatical distinctions do reflect
actual perceptual differences: singular:plural
Children learning a language with gender
• “For all they know, the grammatical
genders assigned by their language are
the true universal genders of objects.”
Boroditsky et al, 2003
Experiment: Gender and Associations
(Boroditsky et al. 2002)
• Subjects: speakers of Spanish or German
– All were fluent also in English
– English used as language of experiment
• Task: Write down the 1st 3 adjectives that come to
mind to describe each object
– All the (24) objects have opposite gender
in German and Spanish
• Raters of adjectives: Native English speakers
Examples:
• Key (masc in German, fem in Spanish)
– Adjectives used by German speakers:
• Hard, heavy, jagged, metal, serrated, useful
– Adjectives used by Spanish speakers:
• Golden, intricate, little, lovely, shiny, tiny
• Bridge (fem in German, masc in spanish)
– Adjectives used by German speakers:
• Beautiful, elegant, fragile, peaceful, pretty
– Adjectives used by Spanish speakers:
• Big, dangerous, long, strong, sturdy, towering
Results of the Experiment
(Boroditsky et al. 2002)
• Raters of adjectives were native English speakers
• Result: Adjectives were rated as masculine or
feminine in agreement with the gender in subject’s
native language
In conclusion..
All of these phenomena associated with categories
(briefly reviewed in this presentation) can be
explained as inevitable consequences of the
structure and function of the human brain
Thank you for your attention!
References
Boroditsky, Lera, Schmidt, Phillips. 2003. Sex,
syntax, and semantics. Language in Mind (eds. Dedre
Gentner & Susan Goldin-Meadow), MIT Press, 2003.
Geschwind, Norman. 1964. The development of the
brain and the evolution of language. Georgetown
Round Table on Languages and Linguistics 17.155-169.
Lamb, Sydney, 1999. Pathways of the Brain: The
Neurocognitive Basis of Language. John Benjamins.
Mountcastle, Vernon, 1998. Perceptual Neuroscience:
The Cerebral Cortex. Harvard University Press.
Pulvermüller, Friedemann, 2002. The Neuroscience of
Language. Cambridge University Press
Whorf, Benjamin Lee. 1956. Language, Thought, and
Reality (ed. John B. Carroll). MIT Press.
For further information . .
www.rice.edu/langbrain