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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. Approach. Combine functional analysis Computational level (Marr); Knowledge level (Newell); Rational level (Anderson)

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act r s extending act r to make big predictions

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
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
Need for 3 Systems
  • Computational Considerations
    • Some tasks need to ignore size, orientation, location
    • Some tasks need highly metric 3D part reps
need for 3 systems1
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
act r s three visiospatial systems

Visual

- object identification

Configural

- navigation

Manipulative

- grasping & tracking

ACT-R/S: Three Visiospatial Systems

Traditional “what” system

Traditional “where” system

slide6

Visual input of nearby chair

Visual Representation

Manipulative Representation

Configural Representation

allocentric vs egocentric representations
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 system
Configural System

Representation

slide11

Configural Buffer

Configural Buffer

Path

Integrator

Triangle-T1

Triangle-TN

• Vectors

• Identity-tag

• Vectors

• Identity-tag

Circle-TN

Circle-T1

+

• Vectors

• Identity-tag

• Vectors

• Identity-tag

Circ-Tri-T1

Circ-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

place cells

Single place-cell

from Muller, 1984

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

from Burgess, Jackson, Hartley & O’Keefe 2000

act r s and place cells

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

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

ACT-R/S and “Place-cells”
egocentric representation allocentric interpetation

Circ-Tri-TN

Circle-TN

• Triangle-ID

• Circle-ID

• delta-heading

• delta-pitch

• triangle-range

• circle-range

• Vectors

• Identity-tag

Triangle-TN

• Vectors

• Identity-tag

Egocentric RepresentationAllocentric Interpetation
foraging model
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)
single chunk recording
“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).

what about humans
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
Mou & McNamara (2002)

E

  • 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!

B

D

F

A

C

E

315º

View position

zero parameter egocentric prediction
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
modeling frames of reference
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
more data
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)
slide23

Training Order

Mou & McNamara (2002) Exp 2

“Allocentric”

“Egocentric”

Data

Model

r=.62

r=.85