ACT-R/S:%20Extending%20ACT-R%20to%20make%20big%20predictions

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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

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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

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Need for 3 Systems

• Computational Considerations
• Some tasks need to ignore size, orientation,
  location
• Some tasks need highly metric 3D part reps

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Need for 3 Systems

• 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

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ACT-R/S Three Visiospatial Systems
Traditional what system
Traditional where system
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Allocentric vs. egocentric representations

• All ACT-R/S representations are inherently
  egocentric representations
• gt Allocentric view points must be inferred
  (computed)
• Q
• What about data suggestive of allocentric
  representations?

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Configural System
Representation
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Place-cells

• Pyramidal cells in rodent hippocampus (CA1/CA3)
• Fires maximally w/r rodents location -
  regardless of orientation
• Span many modalities (aural, olfactory, visual,
  haptic vestibular)
• Stable across time
• Plot cell-firing rate across space

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Place-cells(the not-so pretty picture)

• Cell firing within a rat is also correlated with
• Goal (Shapiro Eichenbaum, 1999)
• Direction of travel (OKeefe, 1999)
• Duration in the environment (Ludvig, 1999)
• Relative configuration of landmarks (Tanila,
  Shapiro Eichenbaum, 1997 Fenton, Csizmadia,
  Muller, 2000)

from Burgess, Jackson, Hartley OKeefe 2000
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ACT-R/S and Place-cells
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Egocentric RepresentationAllocentric
Interpetation
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Foraging Model

• 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)

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Single-Chunk Recording
Stable fields are a function of regularities
in the learned attending pattern.
Multiple passes through same region will
reactivate configural relation chunk.
Multi-modal peaks likewise influenced by goal
(same landmarks, different order).
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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

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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!

E
B
D
F
A
C
E
0º
315º View position
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Zero parameter egocentric prediction

• The hierarchical task analysis of training and
  testing
• But extra boost from encoding configuration
  chunks (egocentric vectors as in ACT-R/S)
• Count number of times any specific chunk will be
  accessed
• 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

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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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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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Training Order
Mou McNamara (2002) Exp 2
Allocentric
Egocentric
Data
Model
r.62
r.85