ACT-R/S: Extending ACT-R to

make big predictions Christian Schunn, Tony Harrison,

Xioahui Kong, Lelyn Saner,Melanie Shoup, Mike Knepp, …

University of Pittsburgh

Approach

Combine functional analysis– Computational level (Marr); Knowledge

level (Newell); Rational level (Anderson)

with neuroscience understanding– most elaborated about gross structure

to build a spatial cognitive architecture for problem solving

Need for 3 Systems• Computational Considerations

– Some tasks need to ignore size, orientation, location

– Some tasks need highly metric 3D part reps

• Computational Considerations– Some tasks need to ignore size, orientation,

location – Some tasks need highly metric 3D part reps– Some tasks need relative 3D locations of

blob objects

Need for 3 Systems

ACT-R/S: Three Visiospatial Systems

- object identification

Visual

- navigationConfigural

- grasping & trackingManipulative

Traditional “what” system

Traditional “where” system

Visual input of nearby chair Visual Representation

Manipulative Representation Configural Representation

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Allocentric vs. egocentric representations

• All ACT-R/S representations are inherently egocentric representations=> Allocentric view points must be inferred

(computed)

• Q:– What about data suggestive of allocentric

representations?

Configural SystemRepresentationRepresentation

Configural System

Buffer

PathIntegrator

AttendLandmark

SelfLocomotion

Configural-A-T0

• Vectors • Identity-token A

Configural-A-T1

• Vectors • Identity-tag A

Configural-A-T0

• Vectors • Identity-tagA

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Configural-B-T0

• Vectors • Identity-tagB

A-B-T0

• Identity-tagA • Identity-tagB • angle

Configural BufferConfigural BufferConfigural BufferConfigural Buffer

Triangle-T1Triangle-T1• Vectors• Identity-tag• Vectors• Identity-tag

Circle-T1Circle-T1• Vectors• Identity-tag• Vectors• Identity-tag

Circle-TNCircle-TN• Vectors• Identity-tag• Vectors• Identity-tag

Triangle-TNTriangle-TN• Vectors• Identity-tag• Vectors• Identity-tag

Circ-Tri-T1Circ-Tri-T1• Triangle-ID• Circle-ID• delta-heading• delta-pitch• triangle-range• circle-range

• Triangle-ID• Circle-ID• delta-heading• delta-pitch• triangle-range• circle-range

Circ-Tri-TNCirc-Tri-TN• Triangle-ID• Circle-ID• delta-heading• delta-pitch• triangle-range• circle-range

• Triangle-ID• Circle-ID• delta-heading• delta-pitch• triangle-range• circle-range

++

PathIntegrator

PathIntegrator

• Pyramidal cells in rodent hippocampus (CA1/CA3)

• Fires maximally w/r rodent’s location - regardless of orientation

• Span many modalities (aural, olfactory, visual, haptic & vestibular)

• Stable across time• Plot cell-firing rate across space

“Place-cells”QuickTime™ and aGraphics decompressorare needed to see this picture.

from Muller, 1984from Muller, 1984

Single place-cellSingle place-cell

• Cell firing within a rat is also correlated with:– Goal (Shapiro & Eichenbaum, 1999)

– Direction of travel (O’Keefe, 1999)

– Duration in the environment (Ludvig, 1999)

– Relative configuration of landmarks (Tanila, Shapiro & Eichenbaum, 1997; Fenton, Csizmadia, & Muller, 2000)

“Place-cells”(the not-so pretty picture)

from Burgess, Jackson, Hartley & O’Keefe 2000

from Burgess, Jackson, Hartley & O’Keefe 2000

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• Configural representation (vectors) supports lowest level navigation - but defines an infinite set of locations

• Configural relationship (between two) establishes a unique location in space

Egocentric RepresentationAllocentric Interpetation

Time

Circ-Tri-TNCirc-Tri-TN• Triangle-ID• Circle-ID• delta-heading• delta-pitch• triangle-range• circle-range

• Triangle-ID• Circle-ID• delta-heading• delta-pitch• triangle-range• circle-range

Circle-TNCircle-TN• Vectors• Identity-tag• Vectors• Identity-tag

Triangle-TNTriangle-TN• Vectors• Identity-tag• Vectors• Identity-tag

• Virtual rat searching for food• Square environment with each wall as a landmark

(obstacle free)• When no food is available, rat free roams or returns to

previously successful location• Food is placed semi-randomly to force rat to cover the

entire environment multiple times• Record activation across time and space for preselected

configural-relationships• (Add Guasssian noise)

Foraging ModelQuickTime™ and a

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“Single-Chunk” Recording

• Multiple passes throughsame region will reactivateconfigural relation chunk.

• Stable fields are a functionof regularities in the learned attending pattern.

• Multi-modal peaks likewiseinfluenced by goal (same

landmarks, different order).

What about humans?

• Small scale orientation and navigation data typically reports egocentric representations– Diwadkar & McNamara, 1997; Roskos-Ewoldsen,

McNamara, Shelton, & Carr, 1998; Shelton & McNamara, 1997

• One famous counter-example– Mou & McNamara, 2002

Mou & McNamara (2002)• Subjects study a view of objects

from 315 deg.• Study it as if from intrinsic axis (0

deg)– A-B– C-D-E– F-G

• Testing asks subjects to imagine:– Standing at X– Look at Y– Point to Z

• Plot pointing error as function of imagined heading (X-Y)

• 0, 90, 180, 270 much lower error!

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0º315ºView position

A

B

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D

E

F

E

Zero parameter egocentric prediction

1. The hierarchical task analysis of training and testing– But extra boost from encoding configuration chunks

(egocentric vectors as in ACT-R/S)

2. Count number of times any specific chunk will be accessed

3. Compute probability of successful retrieval of chunks (location, facing, pointing), using basic ACT-R chunk learning and retrieval functions, default parameters, delay of 10 minutes

Modeling Frames of Reference

• Data (Exp 1)

• Zero parameter prediction• Playing with noise

parameter(s) and retrieval threshold () improve absolute fit (RMSE)

• All (reasonable) parameter values produce similar qualitative fit

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Imagined Heading

P(error)

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More data

• Having mats on the floor which emphasize allocentric frame of reference– No effect (as predicted)

• Square vs. round room– No effect (as predicted)

• Training order from ego vs. allocentric orientation– Big effect (as predicted)

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P(error)

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P(error)Data

Model

Training Order

“Allocentric” “Egocentric”Mou & McNamara (2002) Exp 2

r=.85 r=.62