1. Expertise, Millisecond by Millisecond Tim Curran, University of Colorado Boulder 1

2. Expertise, Millisecond by Millisecond 1.Behavioral/Computational Time-Course Studies –Palmeri Lab (Vanderbilt) 2.Human EEG Studies –Tanaka (Victoria) & Curran (Colorado) Labs 3.Monkey Electrophysiology –Sheinberg Lab (Brown) 2

3. Expertise, Millisecond by Millisecond 1.Behavioral/Computational Time-Course Studies –Palmeri Lab (Vanderbilt) 2.Human EEG Studies –Tanaka (Victoria) & Curran (Colorado) Labs 3.Monkey Electrophysiology –Sheinberg Lab (Brown) 3

4. Basic-Level Advantage and Subordinate-Level Shift with Expertise 4

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6. Exemplar Theories of Categorization Specific exemplars/instances of category members are stored. New things are categorized by comparison with all stored instances. 6 Memory

7. BirdsDogs Indigo Bunting Blue Bird Golden Retriever Yellow Lab Novice Exemplar Representations 7

8. BirdsDogs Indigo Bunting Blue Bird Golden Retriever Yellow Lab Novice Exemplar Representations 8 easy fast

9. BirdsDogs Indigo Bunting Blue Bird Golden Retriever Yellow Lab Novice Exemplar Representations 9 hard slow

10. BirdsDogs Indigo Bunting Blue Bird Golden Retriever Yellow Lab Novice Exemplar Representations Indigo Bunting Blue Bird Golden Retriever Yellow Lab Expert Exemplar Representations 10 High Memory Sensitivity Low Memory Sensitivity

11. Exemplars 11

12. Palmeri ModelCottrell Model 12 Same processes for both levels of categorization.

13. Separate Basic and Subordinate Level Processes 13

14. Separate Basic and Subordinate Level Processes 14

15. Category Verification 15

16. Speeded Verification (Response Signal Method) Accuracy Chance 16

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23. 23 Novice Results

24. Expertise, Millisecond by Millisecond 1.Behavioral/Computational Time-Course Studies –Palmeri Lab (Vanderbilt) RT differences between basic and subordinate categorization may reflect differences in memory sensitivity rather than different stages of processing. Subordinate-level shifts seen with expertise similarly can be explained as an increase in memory sensitivity rather than as bypassing a basic-level processing stage. 24

25. Expertise, Millisecond by Millisecond 1.Behavioral/Computational Time-Course Studies –Palmeri Lab (Vanderbilt) 2.Human EEG Studies –Tanaka (Victoria) & Curran (Colorado) Labs 3.Monkey Electrophysiology –Sheinberg Lab (Brown) 25

26. Neurons Produce Tiny Electrical Fields 26

27. Neurons Aligned within the Cortex Produce Summed Electrical Fields = Scalp EEG 27

28. EEG can be measured with Scalp Electrodes + _ 28

29. Event-related potentials (ERPs) 29

30. Scalp ERPs Excellent Temporal Resolution –Milliseconds Poor Spatial Resolution - Anatomical sources difficult to localize. 30

31. The N170 is larger for faces compared to other objects categories (e.g. Bentin et al., 1996; Botzel et al., 1995; Eimer, 2000; Rossion et al., 2000) N170 31

32. What’s Special about Faces? Special face processing module(s)? (Kanwisher, Bentin) Greater identification experience with faces than other objects? –“perceptual expertise hypothesis” (Gauthier, Tarr, Tanaka) 32

33. Question Is N170 amplitude sensitive to differences in experience/expertise? 33

34. (Tanaka & Curran, 2001) Expertise Effects on the N170 34

35. Perceptual Car Expertise Same/Different Judgments Same Trials are not physically identical. Cars: Same Make/Model (different years, color, perspective) Birds: Same Species (different exemplars) Gauthier, Curran, Curby & Collins (2003) Car Expertise Index: ∆d ’ = d ’ cars - d ’ birds 35

36. N170 Correlates with Degree of Expertise Gauthier, Curran, Curby & Collins (2003) (both p <.05) ExpertsNovices N170 Amplitude (µV) to Cars Self-reported Novices Self-reported Experts 36

37. Expertise, Millisecond by Millisecond 2.Human EEG Studies –Tanaka (Victoria) & Curran (Colorado) Labs –The N170 is sensitive to visual expertise, and does not just reflect a face-specific mechanism. –Changes in N170 amplitude with expertise are consistent with changes in the underlying representations whereas the fastest means first hypothesis might predict N170 timing differences between expert and novice conditions that were not observed. 37

38. Expertise, Millisecond by Millisecond 1.Behavioral Time-Course Studies –Palmeri Lab (Vanderbilt) 2.Human EEG Studies –Tanaka (Victoria) & Curran (Colorado) Labs 3.Monkey Electrophysiology –Sheinberg Lab (Brown) 38

39. How does long term experience with complex objects affect the brain’s response to these stimuli? Highly familiar (Learned over months of training) Novel 39

40. Chronic skull based EEG recordings from monkeys viewing objects over the course of many days reveal general enhanced evoked responses. (Peissig et al., 2007, Cerebral Cortex) 40

41. EEG familiarity effects for complex pictures, measured between 120ms and 250ms after stimulus onset, gradually dissipate over many days. (Number of Repetitions of Novel Objects) 41

42. 100ms Post-synaptic Field Potentials (0.3Hz-300Hz) Spiking Activity (100Hz-6000Hz) 42

43. Familiar Stimuli Novel Stimuli Learned in match to sample task and seen many times in viewing only conditions (over 4-6 months) Pulled from same database as familiars but introduced for the first time in each session 43 Woloszyn & Sheinberg, 2012

44. First example cell 44 Woloszyn & Sheinberg, 2012

45. Second example cell.... 75 more pairs 45 Woloszyn & Sheinberg, 2012

46. Second example cell.... 75 more pairs 46 Woloszyn & Sheinberg, 2012

47. Third example cell.... 75 more pairs 47 Woloszyn & Sheinberg, 2012

48. Not all cells are alike Woloszyn & Sheinberg, 2012 48

49. Putative inhibitory cell.... 75 more pairs 49 Woloszyn & Sheinberg, 2012

50. Effects of familiarity depend on cell type and timing 50 Woloszyn & Sheinberg, 2012 (only top 3 stimuli for each cell)

51. Effects of familiarity depend on cell type and timing 51 Woloszyn & Sheinberg, 2012 (only top 3 stimuli for each cell)

52. Expertise, Millisecond by Millisecond 3.Monkey Electrophysiology –Sheinberg Lab (Brown) –Scalp ERPs, action potentials, (and local field potentials, not shown) all show differences between familiar and novel stimuli around the same time as the human N170 ERP. –Recordings from individual IT neurons indicate that excitatory neurons prefer familiar stimuli whereas inhibitory neurons prefer novel stimuli. These two cell types differ in the timing of their action potentials as well as in the timing of their responses to familiarity. 52

53. Expertise, Millisecond by Millisecond 1.Behavioral/Computational Time-Course Studies –Palmeri Lab (Vanderbilt) 2.Human EEG Studies –Tanaka (Victoria) & Curran (Colorado) Labs 3.Monkey Electrophysiology –Sheinberg Lab (Brown) 53

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55. Extras 55

56. 56 Woloszyn & Sheinberg, 2012