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

1. 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

2. 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

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

4. 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

5. Visual - object identification Configural - navigation Manipulative - grasping & tracking ACT-R/S: Three Visiospatial Systems Traditional “what” system Traditional “where” system

6. Visual input of nearby chair Visual Representation Manipulative Representation Configural Representation

9. 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?

10. Configural System Representation

11. 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

12. 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

13. “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

14. • 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”

15. 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

16. 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)

17. “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).

18. 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

19. 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 0º

20. 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

21. 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

22. 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)

23. Training Order Mou & McNamara (2002) Exp 2 “Allocentric” “Egocentric” Data Model r=.62 r=.85