Computational models of cognitive control (II)

1. Computational models of cognitive control (II) Matthew Botvinick Princeton Neuroscience Institute and Department of Psychology, Princeton University

2. Banishing the homunculus

3. Banishing the homunculus Decision-making in control:

4. Banishing the homunculus Decision-making in control: Not only, “How does control shape decision-making?”

5. Banishing the homunculus Decision-making in control: Not only, “How does control shape decision-making?” But also, “How are ‘control states’ selected?”

6. Banishing the homunculus Decision-making in control: Not only, “How does control shape decision-making?” But also, “How are ‘control states’ selected?” And, “How are they updated over time?”

8. 1. Routine sequential action Botvinick & Plaut, Psychological Review, 2004 Botvinick, Proceedings of the Royal Society, B, 2007. Botvinick, TICS, 2008

9. ‘Routine sequential action’ • Action on familiar objects • Well-defined sequential structure • Concrete goals • Highly routine • Everyday tasks

10. ?! Computational models of cognitive control (II) Matthew Botvinick Princeton Neuroscience Institute and Department of Psychology, Princeton University

11. ADD GROUNDS ADD CREAM ADD SUGAR ADD SUGAR FROM SUGARPACK ADD SUGAR FROM SUGARBOWL PICK-UP PUT-DOWN POUR STIR TEAR SCOOP Hierarchical structure MAKE INSTANT COFFEE

12. MAKE INSTANT COFFEE ADD GROUNDS ADD CREAM ADD SUGAR ADD SUGAR FROM SUGARBOWL / PACKET PICK-UP PUT-DOWN POUR STIR TEAR SCOOP Hierarchical models of action (e.g.,Cooper & Shallice, 2000; Estes, 1972; Houghton, 1990; MacKay, 1987, Rumelhart & Norman, 1982) • Schemas as primitive elements • Hierarchical structure of task built directly into architecture

13. at at+2 at+1 st st+2 st+1 pt pt+2 pt+1 An alternative approach

14. at at+2 at+1 st st+2 st+1 pt pt+2 pt+1 • p, s, a = patterns of activation over simple processing units • Weighted, excitatory/inhibitory connections • Weights adjusted through gradient-descent learning in target task domains

15. Recurrent neural networks • Feedback as well as feedforward connections • Allow preservation of information over time • Demonstrated capacity to learn sequential behaviors (e.g., Cleermans, 1993; Elman, 1990)

16. environment The model action internal representation perceptual input

17. Fixate(Blue) Fixate(Green) Fixate(Top) PickUp Fixate(Table) PutDown Fixate(Green) PickUp Ballard, Hayhoe, Pook & Rao, (1996). BBS.

18. environment Model architecture manipulative perceptual action perceptual input viewed object held object

19. Routine sequential action: Task domain • Hierarchically structured • Actions/subtasks may appear in multiple contexts • Environmental cues alone sometimes insufficient to guide action selection • Subtasks that may be executed in variable order • Subtask disjunctions

20. grounds cream cream Start drink End ` steep tea drink End

21. Manipulative actions sugar-packet Perceptual actions Representations

22. Input Target/ output

23. Input Target/ output

24. Input Target/ output

25. Input Target/ output

26. Input Target/ output

27. Input Target/ output

28. Input Target/ output

29. Model behavior

30. grounds cream cream Start drink End 15% 18% grounds cream cream Start drink End 12% 10% grounds grounds cream cream cream cream Start Start drink drink End End 20% 25% Start Start drink steep tea drink steep tea End End

31. Slips of action (after Reason) • Occur at decision (or fork) points • Sequence errors involve subtask omissions, repetitions, and lapses • Lapses show effect of relative task frequency

32. manipulative perceptual action perceptual input viewed object held object environment

33. Sample of behavior: • pick-up coffee-pack • pull-open coffee-pack • pour coffee-pack into cup • put-down coffee-pack • pick-up spoon • stir cup • put-down spoon • pick-up sugar-pack • tear-open sugar-pack • pour sugar-pack into cup • put-down sugar-pack • pick-up spoon • stir cup • put-down spoon • pick-up cup* • sip cup • sip cup • say-done grounds sugar (pack) cream omitted drink

34. subtask 1 subtask 2 subtask 3 subtask 4 100 Percentage of trials error-free 0 Step in coffee sequence

35. Omissions / anticipations Repetitions / perseverations Intrusions / lapses 80 60 Percentage of trials 40 20 0 0.02 0.1 0.2 0.3 Noise level (variance)

36. 0.16 0.14 0.12 0.1 Odds of lapse into coffee-making 0.08 0.06 0.04 0.02 0 5:1 1:1 1:5 Tea : coffee steep tea sugar cream * grounds cream cream Start drink End steep tea drink End

37. Action disorganization syndrome (after Schwartz and colleagues) • Fragmentation of sequential structure (independent actions) • Specific error types • Omission effect

38. environment manipulative perceptual action perceptual input viewed object held object

39. Sample of behavior: • pick-up coffee-pack • pull-open coffee-pack • put-down coffee-pack* • pick-up coffee-pack • pour coffee-pack into cup • put-down coffee-pack • pick-up spoon • stir cup • put-down spoon • pick-up sugar-pack • tear-open sugar-pack • pour sugar-pack into cup • put-down sugar-pack • pick-up cup* • put-down cup • pull-off sugarbowl lid* • put-down lid • pick-up spoon • scoop sugarbowl with • spoon • put-down spoon* • pick-up cup* • sip cup • sip cup • say-done disrupted subtask subtask fragment sugar repeated subtask fragment cream omitted

41. 0.7 0.6 0.5 0.4 Proportion Independents 0.3 0.2 0.1 0 0.5 0.4 0.3 0.2 0.1 0 Noise (variance) Empirical data: Schwartz, et al. Neuropsychology, 1991

42. 70 Sequence errors 60 Omission errors 50 40 Errors (per opportunity) 30 20 10 0 0.3 0.2 0.1 0.04 Noise (variance) From: Schwartz, et al. Neuropsychology, 1998.

43. Internal representations

44. 1.9 1.4 0.9 0.4 -0.1 -0.6 -1.1 -1.6 -1.2 -0.2 0.8

45. 1.9 1.4 0.9 0.4 -0.1 -0.6 -1.1 -1.6 -1.2 -0.2 0.8

46. 1.9 1.4 0.9 0.4 -0.1 -0.6 -1.1 -1.6 -1.2 -0.2 0.8

47. 1.9 1.4 0.9 0.4 -0.1 -0.6 -1.1 -1.6 -1.2 -0.2 0.8

48. 1.9 1.4 0.9 0.4 -0.1 -0.6 -1.1 -1.6 -1.2 -0.2 0.8

49. grounds cream cream drink drink steep tea

50. grounds cream cream drink drink steep tea