1. 11/2008AAAI 20081 Circuit sharing and the implementation of intelligent systems Michael L. Anderson Institute for Advanced Computer Studies Program in Neuroscience and Cognitive Science University of Maryland College Park, MD USA Department of Psychology Franklin & Marshall College Lancaster, PA USA

2. Cognitive Architecture What is the overall functional architecture of the brain? 11/2008AAAI 20082

3. Cognitive Architecture The classical, and still most widely accepted answer: 1.Low-level localization of function 2.High-level localization of domain 11/2008AAAI 20083

4. 4 Low-level localization of function Penfield’s Homunculus 11/2008AAAI 2008

5. 5 High-level localization of domain Brodmann map showing functional domains 11/2008AAAI 2008

6. More abstractly 11/2008AAAI 20086 1 3 5 2 4 6 Classical c.a. (modularity?) suggests: Each brain area has a fixed working Each function (and class of functions) is implemented in dedicated neural structures

7. As opposed to 11/2008AAAI 20087 Holism (connectionist c.a.?) suggests: Each brain area has a flexible working Each function (or class of functions) is implemented in overlapping neural structures 1 3 5 2 4 6

8. As opposed to 11/2008AAAI 20088 Redeployment suggests: Each brain area has a fixed working Each function (or class of functions) is implemented in overlapping neural structures 1 3 5 2 4 6

9. 9 What’s redeployment? Evolutionary considerations favor a “component re-use” model. Components evolved for one cognitive function are “exapted” for later uses. However, the original functionality is not lost—hence “redeployment” rather than exaptation. 11/2008AAAI 2008

10. Evolution via redeployment 11/2008AAAI 200810

11. 11/2008AAAI 200811 Modularity vs. Holism vs. Redeployment 1 3 5 2 4 6 1 3 5 2 4 6 1 3 5 2 4 6

12. Empirical evidence Database of 665 (subtraction-based) imaging experiments in 20 cognitive domains. “Functional connectivity” analysis of 472 experiments in 8 cognitive domains (all domains with > 30 experiments). 1211/2008AAAI 2008

13. Functional connectivity 1)Choose a spatial segmentation of the brain (we currently use Brodmann areas) 2)Choose an independent variable of interest (cognitive domain) 3)Determine which regions are statistically likely to be co-active, for different levels of the I.V. 1311/2008AAAI 2008

14. Step 3 in more detail A.Calculate chance probability (Q) of co- activation for each BA pair B.In each domain, determine observed probability (K) of co-activation of each BA pair C.Where there is a significant difference between Q and K (Χ 2 ), this is considered a “functional connection” 1411/2008AAAI 2008

15. Functional cooperation 15 Functional connection indicates areas that cooperate in service of cognition 11/2008AAAI 2008 AB-AB DomainCo-active in domain Not co-active in domain -DomainCo-active not in domain Not co-active not in domain

16. List of domains 16 DomainN Action56 Attention77 Emotion42 Language165 Memory88 Mental imagery31 Reasoning33 Visual perception57 11/2008AAAI 2008

17. Functional cooperation We can make graphs of these cooperation links. 1711/2008AAAI 2008 ActiveAreaCoActiveArea Expected CoactProb Observed CoactProb ChiSquare BA10LBA32L0.0190.0368.34 BA10LBA32R0.0150.05461.30 BA10LBA40L0.0290.05413.77 BA10LBA40R0.0160.03613.82 BA10LBA44L0.0180.03610.77 BA10LBA44R0.0120.03628.86

18. Action 18

19. Attention 19

20. Language 20

21. Comparing Domain Complexes 2111/2008AAAI 2008 Can compare many things, for instance: –Node overlap Indicates B.A.s shared by different domain complexes –Edge overlap Indicates functional connectivity/cooperation shared by different domain complexes –Network topology May give clues about nature of function implementation

22. Node vs. Edge Overlap Use Dice’s coefficient: 2(o 1,2 )/(n 1 +n 2 ) Predictions: –Modularity: e, n –Holism: E, N –Redeployment: e, N 2211/2008AAAI 2008

23. 11/2008AAAI 200823 Modularity vs. Holism vs. Redeployment 1 3 5 2 4 6 1 3 5 2 4 6 1 3 5 2 4 6

24. Nodes vs. Edges 11/2008AAAI 200824

25. Nodes vs. Edges 11/2008AAAI 200825

26. Nodes vs. Edges 11/2008AAAI 200826

27. Nodes vs. Edges 11/2008AAAI 200827 p << 0.001

28. But... Maybe this result is just an artifact Given a small number of nodes (84) Large number of possible edges (3486) Get high node overlap and low edge overlap just by chance 11/2008AAAI 200828

29. 11/2008AAAI 200829 p << 0.001

30. 30 4 implications 1.Give up on modularity in its classic form 2.Need to develop a domain-neutral vocabulary for cognitive science 3.Assigning computational/cognitive roles to brain areas will require cross-domain modeling 4.Should consider cross-domain uses when designing cognitive components 11/2008AAAI 2008

31. 31 4 implications 1.Give up on modularity in its classic form 2.Need to develop a domain-neutral vocabulary for cognitive science 3.Assigning computational/cognitive roles to brain areas will require cross-domain modeling 4.Should consider cross-domain uses when designing cognitive components 11/2008AAAI 2008

32. Divergence in implementation 11/2008AAAI 200832 Complex system Module 1 Component 1 Sub- component 1... Component 2 Module 2 Component 3Component 4 Module 3 Component 5Component 6 Modular architectures support functional assignment by decomposition and analysis

33. Convergence in implementation 11/2008AAAI 200833 Complex system Functional Complex 2 Functional Complex 3 Component 1 Functional Complex 1 Component 2 Component 3 Component 4 Component 5 Component 6 Sub- component 5 Sub- component 4 Sub- component 3 Sub- component 2 Sub-component 6... Sub- component 1

34. 34 Cross-domain modeling 1.Cannot determine what a sub-component should do by considering only an individual task or task category, as been the normal practice. 2.Must begin to consider at design time the use of low-level components across multiple tasks in multiple domains. 11/2008AAAI 2008

35. 35 Cross-domain modeling (2) To do this: 1.Model each function of the system 2.Map sub-functions to a limited set of components 3.Constraint: each point of overlap must assign same (abstract) sub-function to each component 11/2008AAAI 2008

36. 36 Cross-domain modeling (3) 11/2008AAAI 2008

37. Anderson, M.L. (2007). The massive redeployment hypothesis and the functional topography of the brain. Philosophical Psychology, 21(2): 143-174. Anderson, M.L. (2007). Evolution of cognitive function via redeployment of brain areas. The Neuroscientist, 13(1): 13-21. Anderson, M. L. (2007). Massive redeployment, exaptation, and the functional integration of cognitive operations. Synthese 159(3): 329-45. Anderson, M.L. (2008). Circuit sharing and the implementation of intelligent systems. Connection Science, 20(4): 239-51. http://www.agcognition.org 11/2008AAAI 200837