11/2008aaai 20081 circuit sharing and the implementation of intelligent systems michael l. anderson...

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Circuit sharing and the implementation of intelligent systems

Michael L. Anderson

Institute for Advanced Computer StudiesProgram in Neuroscience and Cognitive Science

University of MarylandCollege Park, MD USA

Department of PsychologyFranklin & Marshall College

Lancaster, PA USA

Cognitive Architecture

What is the overall functional architecture of the brain?

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Cognitive Architecture

The classical, and still most widely accepted answer:

1.Low-level localization of function

2.High-level localization of domain

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Low-level localization of function

Penfield’s Homunculus11/2008 AAAI 2008

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High-level localization of

domain

Brodmann map showing functional domains11/2008 AAAI 2008

More abstractly

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Classical c.a. (modularity?) suggests:

• Each brain area has a fixed working

• Each function (and class of functions) is implemented in dedicated neural structures

As opposed to

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Holism (connectionist c.a.?) suggests:

• Each brain area has a flexible working

• Each function (or class of functions) is implemented in overlapping neural structures

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As opposed to

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Redeployment suggests:

• Each brain area has a fixed working

• Each function (or class of functions) is implemented in overlapping neural structures

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What’s redeployment?

Evolutionary considerations favor a “component re-use” model.

Components evolved for one cognitive function are “exapted” for later uses.

However, the original functionality is not lost—hence “redeployment” rather than exaptation.

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Evolution via redeployment

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Modularity vs. Holism vs. Redeployment

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Empirical evidence

• Database of 665 (subtraction-based) imaging experiments in 20 cognitive domains.

• “Functional connectivity” analysis of 472 experiments in 8 cognitive domains (all domains with > 30 experiments).

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Functional connectivity

1) Choose a spatial segmentation of the brain (we currently use Brodmann areas)

2) Choose an independent variable of interest (cognitive domain)

3) Determine which regions are statistically likely to be co-active, for different levels of the I.V.

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Step 3 in more detail

A. Calculate chance probability (Q) of co-activation for each BA pair

B. In each domain, determine observed probability (K) of co-activation of each BA pair

C. Where there is a significant difference between Q and K (Χ2), this is considered a “functional connection”

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Functional cooperation

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• Functional connection indicates areas that cooperate in service of cognition

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AB -AB

Domain Co-active in domain

Not co-active in domain

-Domain Co-active not in domain

Not co-active not in domain

List of domains

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Domain N

Action 56

Attention 77

Emotion 42

Language 165

Memory 88

Mental imagery 31

Reasoning 33

Visual perception 57

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Functional cooperation

We can make graphs of these cooperation links.

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ActiveArea CoActiveAreaExpected CoactProb

Observed CoactProb

ChiSquare

BA10L BA32L 0.019 0.036 8.34

BA10L BA32R 0.015 0.054 61.30

BA10L BA40L 0.029 0.054 13.77

BA10L BA40R 0.016 0.036 13.82

BA10L BA44L 0.018 0.036 10.77

BA10L BA44R 0.012 0.036 28.86

Action

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Attention

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Language

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Comparing Domain Complexes

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Can compare many things, for instance:– Node overlap

• Indicates B.A.s shared by different domain complexes

– Edge overlap• Indicates functional connectivity/cooperation

shared by different domain complexes

– Network topology• May give clues about nature of function

implementation

Node vs. Edge Overlap

Use Dice’s coefficient: 2(o1,2)/(n1+n2)

Predictions:– Modularity: e, n– Holism: E, N– Redeployment: e, N

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Modularity vs. Holism vs. Redeployment

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Nodes vs. Edges

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Nodes vs. Edges

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Nodes vs. Edges

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Nodes vs. Edges

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p << 0.001

But . . .

Maybe this result is just an artifact

• Given a small number of nodes (84)

• Large number of possible edges (3486)

• Get high node overlap and low edge overlap just by chance

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p << 0.001

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4 implications1. Give up on modularity in its classic form

2. Need to develop a domain-neutral vocabulary for cognitive science

3. Assigning computational/cognitive roles to brain areas will require cross-domain modeling

4. Should consider cross-domain uses when designing cognitive components

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4 implications1. Give up on modularity in its classic form

2. Need to develop a domain-neutral vocabulary for cognitive science

3. Assigning computational/cognitive roles to brain areas will require cross-domain modeling

4. Should consider cross-domain uses when designing cognitive components

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Divergence in implementation

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Modular architectures support functional assignment by decomposition and analysis

Convergence in implementation

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Complex system

Functional Complex 2

Functional Complex 3

Component 1

Functional Complex 1

Component 2

Component 3

Component 4

Component 5

Component 6

Sub-component 5

Sub-component 4

Sub-component 3

Sub-component 2

Sub-component 6 . . .

Sub-component 1

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Cross-domain modeling

1. Cannot determine what a sub-component should do by considering only an individual task or task category, as been the normal practice.

2. Must begin to consider at design time the use of low-level components across multiple tasks in multiple domains.

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Cross-domain modeling (2)

To do this:

1. Model each function of the system

2. Map sub-functions to a limited set of components

3. Constraint: each point of overlap must assign same (abstract) sub-function to each component

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Cross-domain modeling (3)

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• Anderson, M.L. (2007). The massive redeployment hypothesis and the functional topography of the brain. Philosophical Psychology, 21(2): 143-174.

• Anderson, M.L. (2007). Evolution of cognitive function via redeployment of brain areas. The Neuroscientist, 13(1): 13-21.

• Anderson, M. L. (2007). Massive redeployment, exaptation, and the functional integration of cognitive operations. Synthese 159(3): 329-45.

• Anderson, M.L. (2008). Circuit sharing and the implementation of intelligent systems. Connection Science, 20(4): 239-51.

http://www.agcognition.org

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