1. Modeling and Neuroscience (or ACT-R and fMRI) Jon M. Fincham Carnegie Mellon University, Pittsburgh, PA fincham@cmu.edu

2. Jon M. Fincham ACT-R PGSS 2001 Overview Motivation Motivation Task Specifics Task Specifics Modeling Specifics Modeling Specifics Experiment Results Experiment Results Implications Implications

3. Jon M. Fincham ACT-R PGSS 2001 “Neuroscience” issues Where does x take place? Where does x take place? What does circuit x do? What does circuit x do? How is x computed? How is x computed? “Modeling” issues How is x computed? How is x computed? Where does x take place? Where does x take place? What circuit participates in x? What circuit participates in x?

4. Jon M. Fincham ACT-R PGSS 2001 Modeling & fMRI Issues Computational cognitive modeling provides rich predictions of behavior over time. Can we use the richness of a cognitive model to drive fMRI data analysis and if so how do we do it? Computational cognitive modeling provides rich predictions of behavior over time. Can we use the richness of a cognitive model to drive fMRI data analysis and if so how do we do it? How can we use fMRI results to guide development of specific cognitive models and ACT-R theory in general How can we use fMRI results to guide development of specific cognitive models and ACT-R theory in general

5. Jon M. Fincham ACT-R PGSS 2001 The Task: Tower of Hanoi (of course) The 5-disk Tower of Hanoi (TOH) task is behaviorally rich planning task The subgoaling strategy involves varying numbers of planning steps at each move while progressing toward the goal state ACT-R cognitive model nicely captures behavioral data

6. Jon M. Fincham ACT-R PGSS 2001 Task Summary: Pre-scan practice 21 pseudo-random problems, classic interface, explicit subgoal posting, mousing 21 pseudo-random problems, grid interface, explicit subgoal posting, mousing 7 problems, grid interface, secondary task, no subgoal posting, 3 button response Memorize single goal state, 10 simple practice problems

7. Jon M. Fincham ACT-R PGSS 2001 TOH Classic Interface

8. Jon M. Fincham ACT-R PGSS 2001 TOH Grid Interface

9. Jon M. Fincham ACT-R PGSS 2001 The Subgoaling Strategy 1. Select largest out of place disk in current context and destination peg. 2. If direct move, do it and goto step 1. Otherwise, set subgoal to make move 3. If next largest disk blocks destination, select it and other peg & go to step 2. 4. If next largest disk blocks source, select it and other peg & go to step 2.

10. Jon M. Fincham ACT-R PGSS 2001 TOH 3-tower example move sequence Plan 3 move sequence (3-C, 2-B, 1-C) Plan 1 move sequence (2-B) Plan 1 move sequence (1-B) Plan 2 move sequence (2-C, 1-A) Plan 1 move sequence (2-C) Plan 1 move sequence (1-C) Plan 1 move sequence (3-C)Goal State

11. Jon M. Fincham ACT-R PGSS 2001 The Task: TOH in the magnet One full volume (25 slices) every 4 seconds One full volume (25 slices) every 4 seconds 16 seconds per move = 4 scans per move 16 seconds per move = 4 scans per move 12 20-23 move problems, about 6 minutes each 12 20-23 move problems, about 6 minutes each

12. Jon M. Fincham ACT-R PGSS 2001 Behavioral Results

13. Jon M. Fincham ACT-R PGSS 2001 Behavioral Results

14. Jon M. Fincham ACT-R PGSS 2001 What do we want to see? How does the brain handle goal processing? How does the brain handle goal processing? Which brain areas are differentially responsive to goal setting operations? Which brain areas are differentially responsive to goal setting operations? Are there identifiable circuits that collectively implement manipulation of goals? Are there identifiable circuits that collectively implement manipulation of goals?

15. Jon M. Fincham ACT-R PGSS 2001 Terminology BOLD - Blood Oxygenation Level Dependent response (aka hemodynamic response) BOLD - Blood Oxygenation Level Dependent response (aka hemodynamic response) MR - magnetic resonance, signal measured in the magnet MR - magnetic resonance, signal measured in the magnet Voxel - approximately cube “point” within the brain Voxel - approximately cube “point” within the brain

16. Jon M. Fincham ACT-R PGSS 2001 Where do we begin? Run model over problem set, collecting goal setting event timestamps Run model over problem set, collecting goal setting event timestamps Use goal setting timestamps to generate an ideal BOLD-like timeseries Use goal setting timestamps to generate an ideal BOLD-like timeseries

17. Jon M. Fincham ACT-R PGSS 2001 ACTR(t) Events and Time Series

18. Jon M. Fincham ACT-R PGSS 2001 BOLD Response Characteristics

19. Jon M. Fincham ACT-R PGSS 2001 Identifying a responsive voxel Model MR signal as a function of the ACT-R generated time series Model MR signal as a function of the ACT-R generated time series  MR(t) = B 0 + B 1 *trial(t) + B 2 *ACTR(t) +  (t)  Ignore error trials and immediate successors  Run regression for every one of the 25x64x64 voxels  Result is a beta map for each regressor

20. Jon M. Fincham ACT-R PGSS 2001 Group Analysis Morph each brain into a reference brain Morph each brain into a reference brain Voxel-wise 2-tailed t-test of H 0 : B 2 = 0 across subjects Voxel-wise 2-tailed t-test of H 0 : B 2 = 0 across subjects

21. Jon M. Fincham ACT-R PGSS 2001 Analysis Summary Within subject voxel-wise regression of MR signal against ACT-R generated time series Within subject voxel-wise regression of MR signal against ACT-R generated time series  MR(t) = B 0 + B 1 *trial(t) + B 2 *ACTR(t) +  (t)  Ignore error trials and immediate successors Voxel-wise 2-tailed t-test of H 0 : B 2 = 0 across subjects Voxel-wise 2-tailed t-test of H 0 : B 2 = 0 across subjects Threshold at p<0.0005 and contiguity of 8 voxels Threshold at p<0.0005 and contiguity of 8 voxels

22. Jon M. Fincham ACT-R PGSS 2001 TOH Activation Map (p < 0.0005, contiguity = 8) R L

23. Jon M. Fincham ACT-R PGSS 2001 Premotor & Parietal activity increase parametrically with number of planning steps

24. Jon M. Fincham ACT-R PGSS 2001 Premotor & Parietal activity increase parametrically with number of planning steps

25. Jon M. Fincham ACT-R PGSS 2001 Premotor & Parietal activity increase parametrically with number of planning steps

26. Jon M. Fincham ACT-R PGSS 2001 Prefrontal - Basal Ganglia - Thalamic Circuit

27. Jon M. Fincham ACT-R PGSS 2001 Prefrontal - Basal Ganglia - Thalamic Circuit

28. Jon M. Fincham ACT-R PGSS 2001 Prefrontal - Basal Ganglia - Thalamic Circuit

29. Jon M. Fincham ACT-R PGSS 2001 Prefrontal - Basal Ganglia - Thalamic Circuit

30. Jon M. Fincham ACT-R PGSS 2001 PFC -Basal Ganglia -Thalamus Striatum = Pattern Matching & conflict resolution? Result gates thalamus to update buffers?

31. Jon M. Fincham ACT-R PGSS 2001 Summary of findings so far... Move planning activity in parietal and premotor areas varies parametrically with number of planning steps Move planning activity in parietal and premotor areas varies parametrically with number of planning steps PFC-Basal Ganglia-Thalamic circuit does not vary parametrically with number of planning steps but shows significant BOLD response during high planning moves only PFC-Basal Ganglia-Thalamic circuit does not vary parametrically with number of planning steps but shows significant BOLD response during high planning moves only Suggests PFC becomes engaged when sequencing of multiple moves is required Suggests PFC becomes engaged when sequencing of multiple moves is required

32. Jon M. Fincham ACT-R PGSS 2001 What can we conclude about the model? Subjects are bypassing subgoaling procedure for 2-tower subproblems Subjects are bypassing subgoaling procedure for 2-tower subproblems Setting a goal “move disk 1 to opposite of where disk 2 goes” Setting a goal “move disk 1 to opposite of where disk 2 goes” Now we can use GLM model comparison techniques to confirm best fitting models... Now we can use GLM model comparison techniques to confirm best fitting models...

33. Jon M. Fincham ACT-R PGSS 2001 What can we conclude about ACT-R? Nothing…….yet. Nothing…….yet. Goal manipulation does seem to predict brain activity in the “right” places, but Goal manipulation does seem to predict brain activity in the “right” places, but Need to run other studies in different domains (and different models) to gain confidence in our label of “goal processing” circuitry Need to run other studies in different domains (and different models) to gain confidence in our label of “goal processing” circuitry

34. Jon M. Fincham ACT-R PGSS 2001 What have we learned so far? Applying cognitive modeling to the neuroimaging domain is feasible: models can inform analysis Applying cognitive modeling to the neuroimaging domain is feasible: models can inform analysis fMRI data can inform models fMRI data can inform models fMRI data can inform architecture fMRI data can inform architecture Symbiotic relationship exists between modeling and fMRI Symbiotic relationship exists between modeling and fMRI What else? What else?

35. Jon M. Fincham ACT-R PGSS 2001 What else can we examine? +goal>, +retrieval>, +visual>, +aural>, +manual>, +goal>, +retrieval>, +visual>, +aural>, +manual>, Number of elements in goal Number of elements in goal Number of full buffers Number of full buffers

36. Jon M. Fincham ACT-R PGSS 2001 Thank you!