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Ling 411 – 14. Categories in the Brain. Variability in functional webs. Variable ignition Variable web structure. Variability I – Variable Ignition. When ignition of a web occurs, it does not have to include the entire functional web Examples:

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variability in functional webs
Variability in functional webs
  • Variable ignition
  • Variable web structure
variability i variable ignition
Variability I – Variable Ignition
  • When ignition of a web occurs, it does not have to include the entire functional web
  • Examples:
    • It isn’t necessary to speak about everything one sees
    • Visualization is optional
      • At least to some extent
  • Application of attention can provide richer detail of ignition
    • More extensive activation of subwebs
    • For example, visualization
ignition of a word web from visual input11
Ignition of a word web from visual input

T

C

P

Art

PA

V

M

Mention is optional

speaking as a response to ignition of a web2
Speaking as a response to ignition of a web

T

C

P

Art

PA

V

M

The part of the motor structure that controls the articulation of [dog]

speaking as a response to ignition of a web3
Speaking as a response to ignition of a web

T

C

P

Art

PA

V

M

From here to the muscles that control the organs of articulation

ignition of a web from speech input
Ignition of a web from speech input

Properties:

C – Conceptual

M – Memories

PR – Phonolog. Rec.

T – Tactile

V - Visual

T

C

PR

PA

V

M

ignition of a web from speech input1
Ignition of a web from speech input

Properties:

C – Conceptual

M – Memories

PR – Phonolog. Rec.

T – Tactile

V - Visual

T

C

PR

PA

V

M

ignition of a web from speech input2
Ignition of a web from speech input

Properties:

C – Conceptual

M – Memories

P – Phonolog. Rec.

T – Tactile

V - Visual

T

C

PR

PA

V

M

ignition of a web from speech input3
Ignition of a web from speech input

Properties:

C – Conceptual

M – Memories

PR – Phonol. Rec.

T – Tactile

V - Visual

T

C

PR

PA

V

M

ignition of a web from speech input4
Ignition of a web from speech input

T

C

PR

PA

V

M

Upon hearing “cat” we can also visualize a cat

Probably a largely optional process

visualization from speech input
Visualization from speech input

T

C

PR

PA

V

M

Upon hearing “cat” we can also visualize a cat

cortex internal ignition
Cortex-internal ignition
  • “… ignition of the web after sufficiently strong stimulation by … cortical neurons outside the functional web. This … cortex-internal activation of a web can be considered the organic basis of being reminded of an object even though it is absent in the environment.”

(Pulvermüller 2002: 30)

variability ii variable web structure
Variability II – Variable web structure
  • Observation: every cat perceived or spoken about is different from others encountered previously
    • For example, different color
    • Each web is built based on experience
  • Consequence: the precise web structure for an individual is likely to differ in details for different instances of the same category
  • Inertia: some of the differences in a new exemplar are likely to be overlooked
some key concepts
Some Key Concepts
  • Functional Web
  • (Functional) Subweb
  • Cardinal node
  • Ignition
  • Reverberation
understanding semantics
Understanding semantics
  • Semantic structure is largely a matter of conceptual categories
  • Understanding how categories work is the key to unlock the mysteries of semantics
  • To understand how categories work we need to understand how the brain manages categorial information
types of conceptual categories
Types of Conceptual Categories
  • Discrete
    • Even integers
    • Counties in Texas
  • Radial
    • Birds
    • Vehicles
  • Family resemblance
    • Games
    • Furniture
  • Ill-defined
    • Thought
    • Mind
phenomena associated with categories
Phenomena associated with categories
  • 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
Phenomena associated with categories: 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
Phenomena associated with categories: 2
  • No small set of defining features (with rare exceptions)
  • Fuzzy boundaries
    • Example: VEHICLE
      • Car, truck, bus
      • Airplane?
      • Boat?
      • Toy car, model airplane?
      • Raft?
      • Roller skate?
      • Snowboard?
fuzzy categories
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
Phenomena associated with categories: 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
Prototypicality phenomena

The category BIRD

Some members are prototypical

ROBIN, SPARROW

Others are peripheral

EMU, PENGUIN

The categoryVEHICLE

Prototypical: CAR, TRUCK, BUS

Peripheral: ROLLER SKATE, HANG GLIDER

phenomena associated with categories 4
Phenomena associated with categories: 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
Phenomena associated with categories: 5
  • 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
    • 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
Phenomena associated with categories: 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: French:

bell

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
Phenomena associated with categories - 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
    • Example: Racial profiling
beyond description to explanation
Beyond description to explanation
  • How can we explain these phenomena?
  • To answer this question 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
facts and hypotheses that we can build on
Facts and hypotheses that we can build on
  • The brain is a network
    • Composed, ultimately, of neurons
    • Cortical neurons are clustered in columns
      • Columns come in different sizes
      • Each minicolumn acts as a unit
  • Therefore a person’s linguistic and conceptual system is a network
  • Every word and every concept is represented as a sub-network
    • Term: functional web(Pulvermüller 2002)
concepts and percepts cortical representation
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 (more abstract)
    • Locations, for nominal concepts:
      • Angular gyrus
      • (?)MTG
      • (?)SMG
hypotheses concerning functional webs
Hypotheses concerning functional webs
  • Hypothesis I: Functional Webs
    • A concept is represented as a functional web
  • Hypothesis II: Columnar Nodes
    • Nodes are implemented as cortical columns
  • Hypothesis III: Nodal Specificity
    • Every node in a functional web has a specific function
  • Hypothesis III(a): Adjacency
    • Nodes of related function are in adjacent locations
      • More closely related function, more closely adjacent
hypothesis iii a adjacency
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
hypotheses concerning functional webs1
Hypotheses concerning functional webs
  • Hypothesis IV: Extrapolation to Humans
    • The findings about cortical structure and function from experiments on cats, monkeys, and rats can be extrapolated to humans
    • Hypothesis IV(a): The extrapolation can be extended to linguistic and conceptual structures and functions
  • Hypothesis V:Hierarchy
    • A functionalweb is hierarchically organized
  • Hypothesis VI: Cardinal nodes
    • Every functional web has a cardinal node
    • Hypotheses VI(a):
      • Each subweb likewise has a cardinal node
part of the functional web for cat
(Part of) the functional web for CAT

The cardinal node for the entire functional web

T

C

P

A

V

M

Cardinal nodes of

the subwebs

phenomena associated with categories1

REVIEW

Phenomena associated with categories
  • 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
how to explain

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
Phenomena associated with categories: 1-3
  • No small set of defining features (with rare exceptions)
    • Example: CUP
    • More realistic alternative: radial categories
  • Fuzzy boundaries
    • Example: VEHICLE
  • Prototypical members and peripheral members
    • 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
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 properties of a category have different weights

The cardinal node

CUP

T

MADEOF GLASS

SHORT

CERAMIC

The properties are represented by nodes which are connected to lower-level nodes

HASHANDLE

nodes have activation thresholds
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
Prototypical exemplars provide stronger and more rapid activation

Activation threshold

(can be satisfied to

varying degrees)

Inhibitory connection

CUP

T

MADE OF GLASS

SHORT

CERAMIC

HAS HANDLE

Stronger connections carry more activation

explaining prototypicality
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
slide60
Activation of different sets of properties produces greater or lesser satisfaction of the activation threshold of the cardinal node

CUP

MADE OF GLASS

SHORT

CERAMIC

HAS HANDLE

More important properties have stronger connections, indicated here by thickness of lines

explaining prototypicality summary
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 41
Phenomena associated with categories: 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
How to explain? Perceptual Neuroscience
  • We have evidence on this point from the experiments described by Mountcastle
  • 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
  • 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
conceptual systems and perceptual systems

REVIEW

Conceptual systems and perceptual systems
  • 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
columns of different sizes
Columns of different sizes
  • Minicolumn
    • Basic anatomically described unit
    • 70-110 neurons (avg 75-80)
    • Diameter barely more than that of pyramidal cell body (30-50 μ)
  • Maxicolumn (term used by Mountcastle)
    • Diameter 300-500 μ
    • Bundle of about 100 continuous minicolumns
  • Hypercolumn – up to 1 mm diameter
    • Can be long and narrow rather than cylindrical
  • Functional column
    • Intermediate between minicolumn and maxicolumn
    • A contiguous group of minicolumns
functional columns
Functional Columns
  • Intermediate in size between minicolumn and maxicolumn
  • Hypothesized functional unit whose size is determined by experience/learning
  • A maxicolumn consists of multiple functional columns
  • A functional column consists of multiple minicolumns
  • Functional column may be further subdivided with learning of finer distinctions
columns of different sizes in order according to size
Columns of different sizes In order according to size

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
Hypercolums: Modules of maxicolumns

A visual area

in temporal

lobe of a

macaque

monkey

perceptual subcategories and columnar subdivisions of larger columns
Perceptual subcategories andcolumnar 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 columns1
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
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
hypercolums modules of maxicolumns1
Hypercolums: 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

Maxicolumn

Functional column

Sub-functional column

Supercategory

Category

Subcategory

Sub-subcategory

Category representations in the cortex
hypothesis applied to conceptual categories
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 51
Phenomena associated with categories: 5
  • 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
    • 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 61

REVIEW

Phenomena associated with categories: 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: French:

bell

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 71
Phenomena associated with categories - 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
    • Example: Racial profiling
these phenomena 5 7 are interrelated
These phenomena (5-7) are interrelated
  • 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
    • Example: Racial profiling
pertinent neuroanatomical findings bidirectional processing
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
Bidirectional processing and inference

These connections are bidirectional

CUP

T

MADE OF GLASS

SHORT

CERAMIC

Separate fibers for the two directions; shown as one line in the notation

HANDLE

bidirectional processing and inference1
Bidirectional processing and inference

Thought process:

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

CUP

T

SHORT

HANDLE

category structure and inference
Category Structure and Inference

Category

T

A

B

Consequence:

If A and B, then E and F

F

E

C D

Properties

examples
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
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
Mechanisms of operation
  • Entrenchment
    • Strengthening of connections through repeated activation
      • An automatic brain process
      • Important in learning
  • Reverberation of activation
    • Leads to greater levels of activation
  • Priming
  • Language as a major means of learning conceptual and perceptual distinctions
entrenchment and thinking a mechanism
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
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
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
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
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
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
examples1
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
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
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

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