Reductive and Representational Explanation in Synthetic NeuroethologyPete MandikAssistant Professor of PhilosophyCoordinator, Cognitive Science LaboratoryWilliam Paterson University, New Jersey

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Collaborators

Michael Collins, City University of New York Graduate Center

Alex Vereschagin, William Paterson University

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My Thesis

Even for the simplest cases of intelligent behavior, the best explanations are both reductive and representational

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Overview

■ Mental representation in folk-psychological explanation

■ Mental representation in non-humans

■ The problem of chemotaxis■ Modeling the neural control of

chemotaxis■ What the representations are

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Mental reps in folk-psychGeorge is opening the fridge because:George desires that he drinks some beerGeorge sees that the fridge is in front of himGeorge remembers that he put some beer in the fridge

■George’s psychological states cause his behavior■George’s psychological states have representational content

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Mental reps in non-human animalsRats and maze learningAfter finding the platform the first time, rats remember its location and can swim straight to it on subsequent trial from novel starting positions.Rats not only represent the location, but compute the shortest path.

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Mental reps in non-human animalsDucks’ representation of rate of return

Every day two naturalists go out to a pond where some ducks are overwintering and station themselves about 30 yards apart. Each carries a sack of bread chunks. Each day a randomly chosen one of the naturalists throws a chunk every 5 seconds; the other throws every 10 seconds. After a few days experience with this drill, the ducks divide themselves in proportion to the throwing rates; within 1 minute after the onset of throwing, there are twice as many ducks in front of the naturalist that throws at twice the rate of the other. One day, however, the slower thrower throws chunks twice as big. At first the ducks distribute themselves two to one in favor of the faster thrower, but within 5 minutes they are divided fifty-fifty between the two “foraging patches.” … Ducks and other foraging animals can represent rates of return, the number of items per unit time multiplied by the average size of an item.

(Gallistel 1990; emphasis mine)

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Positive Chemotaxis

Movement toward the source of a chemical stimulus

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2-D food finding

Sensors

Brain

Steering Muscles

2-Sensor Chemophile:

■ Steering muscles orient creature toward stimulus

■ Perception of stimulus being to the right fully determined by differential sensor activity

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1-D food finding

Sensor

Brain

Steering Muscles

1- Sensor “Lost” Creature

■ left/right stimulus location underdetermined by sensor activity

■ only proximity perceived

■ Adding memory can help

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Things to Note:

Note that single-sensor gradient navigation is a “representation hungry” problem

Note the folk-psychological explanation of how a human would solve the problem

Note, in what follows, the resemblance to the explanation of the worm’s solution

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C. Elegans

Caenorhabditis Elegans

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C. Elegans

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C. Elegans

Feree and Lockery (1999). “Computational Rules for Chemotaxis in the Nematode C. Elegans.” Journal of Computational Neuroscience 6, 263-277

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C. Elegans

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C. Elegans

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C. Elegans

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The Extracted Rule:

,)(2

2

210 dt

Cdz

dt

dCztCz

dt

dbias

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Zeroth Order

The simulations were run keeping only the terms up to the zeroth order:

This rule failed to produce chemotaxis for any initial position.

)(0 tCzdt

dbias

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First Order

Next the simulations were run keeping all terms up to the first order:

This rule accurately reproduced the successful chemotaxis performed by the network model.

dt

dCztCz

dt

dbias 10 )(

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Problems

Remains open. . .How the network controllers are

workingWhat the networks themselves are

representing and computingWhether the networks are utilizing

memory

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Framsticks

3-D Artificial Life simulator

By Maciej Komosinski

and Szymon Ulatowski Poznan University of Technology, Poland

http://www.frams.poznan.pl/

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Framsticks

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Framsticks nematodes

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Memory in Chemotaxis

■ Experimental Set Up◆ 3 orientation networks: Feed-

forward, Recurrent, and Blind◆ five runs each, for 240 million steps◆ mutations allowed only for neural

weights◆ fitness defined as lifetime distance◆ Initial weights: Evolved CPGs with

un-evolved (zero weights) orienting networks

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0

200

400

600

800

1000

1200

1 2 3 4 5 6 7 8 9 10 11 12

Series1

Series2

Series3

Results

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What the representations are

States of neural activation isomorphic to and causally correlated with environmental states

■ Sensory states■ Memory states■ Motor-command states

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Representation and IsomorphismIsomorphism

One to one mapping between structures structure = set of

elements plus set of relations on those elements

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Representation and IsomorphismRepresentation

Primarily: a relation between isomorphic structures

Secondarily: a relation between elements and/or relations in one structure and those in another

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Isomorphisms between multiple structuresWhich of the many structures a given

structure is isomorphic to, does a given structure represent?

The range of choices will be narrowed by the causal networks the structure is embedded in

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For further investigation

■ States of desire/motivation◆ Clearer in models of action selection,

not intrinsic to the stimulus orientation networks

■ Modeling representational error and falsity◆ Error and falsity are distinct, but this

is clearer in non assertoric attitudes

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Summing up

Single-sensor chemotaxis is a “representation hungry” problem

Even explanations of adaptive behaviors as simple as chemotaxis benefit from psychological state ascriptions

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Summing up

The psychological states in question are identical to neural states

The neural states in question are causally explanatory of intelligent behavior in virtue of isomorphisms between structures of neural activations and structures of environmental features

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Summing up

Therefore…

Even for the simplest cases of intelligent behavior, the best explanations are both reductive and representational

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THE END