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Conference on Comparative Cognition

Melbourne, FL, March 24, 2006

The Predictability of Behavior

Russell M. Church

Brown University

T= itle

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Acknowledgments

= ; Acknowledgments

To current collaborators at Brown:&#= 13;

Paulo Guilhardi

Mika MacInnis

Linlin Yi

Marcelo Caetano =

For the empirical and theoretical substance of this talk.

National Institute of Mental Health Grant

Temporal Discrimination Learning, 1988 - 2008

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Acknowledgments

= ; Acknowledgments

To previous research collaborators at Brown:

Seth Roberts, University of California, Berkeley

Warren Meck, Duke University

Kim Kirkpatrick, University of York = ;

Jon Crystal, University of Georgia&= #13;

Rich Keen, = ; Converse College

Jason Machan, = ; Lifespan

Steve Seow, = ; Microsoft

For establishing the basis of this talk.

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Acknowledgments

To other investigators of interval timing

Ack= nowledgments

John Gibbon

Lorraine Allan

Peter Balsam

Don Blough

Randy Gallistel

Peter Killeen

Armando Machado

Ralph Miller

Bill Roberts

Ed Wasserman

And many others, who have particularly influenced my research.

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Outli= n<= span style=3D'font-size:18%;color:white;mso-color-index:0'>e

<= span style=3D'color:#A50021'>1.The Question: How well can behavior be predicted?

2. The Method: How to predict behavior =

<= span style=3D'visibility:hidden'>•

3. The Results: <= /span>Data from a timing experiment

4. The Model: <= /span>Packet theory

5. The Evaluation: A Turing test

6. The Answer: <= /span>Quite well

OUTLINE OF THE TALK

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Part = 1<= span style=3D'font-size:18%;color:white;mso-color-index:0'>:<= span style=3D'font-size:18%;color:white;mso-color-index:0'> T<= span style=3D'font-size:18%;color:white;mso-color-index:0'>h= e= Q= u= es= ti= on

Part 1: The Question

How well can behavior be predicted?

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Prediction <= span style=3D'font-size:18%;color:white;mso-color-index:0'>i= s= im= po= ss= ib= le

1.Behavior is not determined.

(Determinism is just a theory, hypoth= esis, or assumption)

2. Behavior is partially determined b= y free will.

3. Behavior is partially determined b= y random factors.

The Question: How well can behavior be predicted?

Stand= ard views: Prediction is impossible

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Prediction = i= s p= os= si= ble

My View: Prediction is possible

1.&nb= sp; Behavior is determined by biology and experience.

(Ramsey, W. (2003). Eliminative materialism. Stanford Encyclopedia of= Philosophy. http://plato.stanford.edu/entries/materialism-eliminative)

2. Free will is an illusion.

(Wegner, D. M. 2002. The illusion of conscious will, MIT Press.)

3.But, the predictability of behavior does not depend upon the truth of determinism or free will.

(Hoefer, C. 2003. Causal determinism. Stanford Encyclopedia

of Philosophy, http://plato.stanford.edu/entries/determinism-causal/)

The Question: How well can behavior be predicted?

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Prediction <= span style=3D'font-size:18%;color:white;mso-color-index:0'>i<= span style=3D'font-size:18%;color:white;mso-color-index:0'>s= difficult f= or us

1. Psychology is too young a sci= ence to predict behavior.

2. We are not smart enough to predict behavior.

The Question: How well can behavior be predicted?

Stan= dard view: Prediction is too difficult for us

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Prediction = i= s= d= if= fic= ul= t

1.Behavior is determ= ined by the brain,

which is very complex.

•

2. Behavior is determined= by the environment, which is complex. <= /div>

The Question: How well can behavior be predicted?

Stand= ard view: Prediction is difficult

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Prediction = i= s<= span style=3D'font-size:18%;color:white;mso-color-index:0'> n= ot= d= i= ff= ic= ul= t

My View: The Problem is not difficult

We can predict the behavio= r of a rat in a simple <= /span>environment quite well.

The Question: How well can behavior be predicted?

Skinner’s Problem

Given ingredients: Rat, box, and stimuli

And a recipe: = Any procedure

Predict: Respon= se of the rat

Procedure = ;Response

Light Head entry

Noise Lever press

Clicker Lick

Food Explore

Groom

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Type = o<= span style=3D'font-size:18%;color:white;mso-color-index:0'>f= “<= span style=3D'font-size:18%;color:white;mso-color-index:0'>p= r= ed= ic= t= io= n”

The Question: How well can behavior be predicted?

1.Absolute predictio= n of the precise time of each response (Impossible).

2. Prediction relative to a really bad alternative, e.g., null hypothesis (Too easy).

3. Prediction relative to the predictions of the behavior from another sample of behavio= r (Just right--A Turing test).

How = should “predictability” be defined?

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Part <= span style=3D'font-size:18%;color:white;mso-color-index:0'>2<= span style=3D'font-size:18%;color:white;mso-color-index:0'>:= The M= et= ho= d

Part 2: The Method

How to Predict Behavior

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Click Her= e to Open Video (wmv format)

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Church & Guilhardi, 2005

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Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" PowerPoint Presentation

Prima= ry Data

3868.538 17 0.000 Houselight on

3895.688 8 27.15 Head-entry response-1

3896.142 8 27.604 <= /span>Head-entry response-2

3896.496 8 27.798 <= /span>Head-entry response-3

3896.336 8 27.958 <= /span>Head-entry response-4

3896.554 8 28.016 <= /span>Head-entry response-5

3896.798 8 28.260 <= /span>Head-entry response-6

3896.820 8 28.282 <= /span>Head-entry response-7

3897.886 8 29.348 <= /span>Head-entry response-8

3898.100 8 29.562 <= /span>Head-entry response-9

3898.298 8 29.760 <= /span>Head-entry response-10

3898.720 8 30.182 <= /span>Head-entry response-11

3898.720 27 30.182 Houselight off

3898.730 19 30.192 Food delivered

Times (= s)

Guilhardi & Church, Dynamics of temporal discrimination (Learning & Behavior, 2005)

The Met= hod: How to Predict Behavior

http:/= /psychonomic.org/archive

Relative

Time

Event

Event Code

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The Method: How to predict behavior

Simulation:

Explicit solution:

Given:

A procedure

A model

Some parameters (f)

Find:

The best estimates of the parameters

Parameter Estimation: Simulation and Explicit Solution

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Part <= span style=3D'font-size:18%;color:white;mso-color-index:0'>3<= span style=3D'font-size:18%;color:white;mso-color-index:0'>:= The R= es= ult= s

Part 3: The Results

Data from a Timing Experiment

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Multip= le cued-interval procedure

The Results: Data from a timing experiment

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Patter= n on Averaged Cycles

The Results: Data from a timing experiment

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text/html; charset="us-ascii" PowerPoint Presentation

Patte= rn on Individual Cycles

The Results: Data from a timing experiment

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Response Rate

The Results: Data from a timing experiment

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Response Bouts

The Results: Data from a timing experiment

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