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
ACT-R/S and “Place-cells”QuickTime™ and aTIFF (Uncompressed) decompressorare needed to see this picture.
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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
C
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
0.2
0.25
0.3
0 45 90 135 180 225 270 315
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)
0.15
0.2
0.25
0 45 90 135 180 22 270 315
Imagined Heading
P(error)
0.2
0.25
0.3
0 45 90 135 180 225 270 315
Imagined Heading
P(error)
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0 45 90 135 180 225 270 315
Imagined Heading
P(error)
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0 45 90 135 180 225 270 315
Imagined Heading
P(error)Data
Model
Training Order
“Allocentric” “Egocentric”Mou & McNamara (2002) Exp 2
r=.85 r=.62
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