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. 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

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

6. Visual input of nearby chairVisual Representation Manipulative RepresentationConfigural 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 Triangle-T1 Vectors Identity-tag Vectors Identity-tag Circle-T1 Vectors Identity-tag Vectors Identity-tag Circle-TN Vectors Identity-tag Vectors Identity-tag Triangle-TN Vectors Identity-tag Vectors Identity-tag Circ-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-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 + + Path Integrator Path Integrator

12. 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” from Muller, 1984 Single place-cell

13. 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

14. 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

15. Egocentric Representation Allocentric Interpetation 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 Circle-TN Vectors Identity-tag Vectors Identity-tag Triangle-TN Vectors Identity-tag Vectors Identity-tag

16. 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 Model

17. “Single-Chunk” Recording Multiple passes through same region will reactivate configural relation chunk. Stable fields are a function of regularities in the learned attending pattern. 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) 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! 0º 315º View position A B C D E F E

20. 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

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