Act r s extending act r to make big predictions
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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

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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


Act r s extending act r to make big predictions

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


Act r s extending act r to make big predictions

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)


Act r s extending act r to make big predictions

Training Order

Mou & McNamara (2002) Exp 2

“Allocentric”

“Egocentric”

Data

Model

r=.62

r=.85


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