1. Experimental Design Tali Sharot & Christian Kaul With slides taken from presentations by: Tor Wager Christian Ruff

2. Part I Taxonomy of experimental design (Friston ’97) Aim of design Block Design Event Related Design Baseline / Control Timing

3. General Taxonomy of Experimental Design (Friston, 1997) Categorical –activation in one task as compared to that in another task –categorical designs assume that the cognitive processes can be dissected into sub-cognitive processes & that one can add or remove cognitive processes by “Pure insertion”. Factorial –Factorial designs involve combining two or more factors within a task and looking at the effect of one factor on the response to other factor Parametric –systematic changes in the brain responses according to some performance attributes of task can be investigated in parametric designs

4. Categorical experimental design –Subtraction Pure insertion: assumption that one can add or remove cognitive processes without influencing others. activation in one task as compared to that in another task considering the fact that the neural structures supporting cognitive and behavioural processes combine in a simple additive manner –Conjunction Testing multiple hypotheses several hypotheses are tested, asking whether all the activations in a series of task pairs, are jointly significant

5. Factorial design - example A – Low attentional load, motion B – Low attentional load, no motion C – High attentional load, motion D – High attentional load, no motion A B C D LOW LOAD HIGH MOTION NO MOTION Load task Rees, Frith & Lavie (1997)

6. Terminology Simple main effects Main effects Interaction terms A B C D LOW LOAD HIGH MOTION NO MOTION

7. SIMPLE MAIN EFFECTS A – B: Simple main effect of motion (vs. no motion) in the context of low load B – D: Simple main effect of low load (vs. high load) in the context of no motion D – C: ? Simple main effect of no motion (vs. motion) in the context of high load A B C D LOW LOAD HIGH MOTION NO MOTION OR The inverse simple main effect of motion (vs. no motion) in the Context of high load

8. MAIN EFFECTS (A + B) – (C + D): the main effect of low load (vs. high load) irrelevant of motion  Main effect of load (A + C) – (B + D): ? The main effect of motion (vs. no motion) irrelevant of load  Main effect of motion A B C D LOW LOAD HIGH MOTION NO MOTION

9. INTERACTION TERMS (A - B) – (C - D): the interaction effect of motion (vs. no motion) greater under low (vs. high) load (B - A) – (D - C): ? the interaction effect of no motion (vs. motion) greater under low (vs. high) load A B C D LOW LOAD HIGH MOTION NO MOTION

10. Factorial design in SPM5 A B C D LOW LOAD HIGH MOTION NO MOTION How do we enter these effects in SPM5? Simple main effect of motion in the context of low load: A vs. B or (A – B) A B C D [1 -1 0 0]

11. Factorial design in SPM5 Main effect of low load: (A + B) – (C + D) Interaction term of motion greater under low load: (A – B) – (C – D) A B C D [1 -1 -1 1] [1 1 -1 -1]

12. Factorial design in SPM5 Interaction term of motion greater under low load: (A – B) – (C – D) A B C D

13. Parametric experimental design What do we want to measure?  systematic changes in the brain responses according to some performance attributes of task can be investigated in parametric designs:

14. Part I Aim of design Block Design Event Related Design Baseline / Control Timing

15. What we want from a design Interpretability: Can I relate brain data to specific psychological events? –Memory retrieval and comparison processes associated with recognition Power: Can I detect results? Experimental (A) - Control (B)

16. Block Design Similar events are grouped ….

17. pros Avoid rapid task-switching (patients) Fast and easy to run Good signal to noise Block design - some pros & cons cons Expectation (cognitive set, attention, fatigue) Habituation (olfactory, emotional) Different trials according to subjects’ responses.

18. Event Related Design Events are mixed

19. Encode: Event Related Design Recognition Test: …….. Response: new old old new old Category: CR HIT HIT MISS FA

20. Baseline? Known: Queen! Unknown? Aunt Jenny? Different stimuli & Task Queen! Female!? Same stimuli, different task Different stimuli similar Task + Queen! i - pod! Queen! Mmm..Whats for dinner?... Similar stimuli, same task

21. “Baseline” here corresponds to session mean “Cognitive” interpretation hardly possible, but useful to define regions generally involved in the task Baseline?

22. Timing : the long and the short of it 3 s picture viewing Recognition: 250 ms “What was he in?” “I used to wear Batman PJs…”

23. Timing : the long and the short of it Autobiographical Memory Retrieval Word Recognition “Friend” Search Episodic Retrieval &Elaboration ~5sc ~14sc Response

24. Part II ROIs in early visual cortex Multivariate decoding Natural viewing Individual differences

25. ROI – Regions of interest A) anatomically defined B) functionally defined

26. What are ROIs in early visual cortex? Stimuli 3D representation Flatmesh

27. How do we get ROIs? + V1v V2v V1d V2d = right hemispheredot localiserretinotopic location of dot

28. exemplar ROI Result Extracting activity-values from ROIs for all conditions. Then compute interaction term for activity in V5/MT greater under motion (vs. no motion) under high versus low load (replication: Rees et al. ’97) contrast brain activity ROI Load task

29. Multivariate pattern analysis? What is MVPA? Methodology in which an algorithm is trained to tell two or more conditions from each other. The algorithm is then presented with a new set of data and categorises/classifies it into the conditions previously learned.

30. What questions can (& cannot) be answered with multivariate pattern analysis? When conventional analysis is not feasible, multivariate analysis might be an option  but what are we actually measuring? Assumption: Feature sensitive information is present in BOLD signal Haynes & Rees (2006) Haynes & Rees (2005) Mean signal LDA

31. What questions can (& cannot) be answered with multivariate pattern analysis? Feature sensitive information is present in BOLD signal (biased competition) Multivariate decoding extracts this info Thus feature selective processing promises new insights (i.e. towards a better understanding of neuronal population coding contained in the BOLD signal) Haynes & Rees (2006) Haynes & Rees (2005) Mean signal LDA

32. Multivariate pattern analysis – how to design an experiment Other then with conventional analysis we are asking a different question: Does the pattern of activity contain meaningful information we can extract?  Not the level of brain activity is addressed, but the pattern of information within the activity.

33. Experiment 1Question: Does feature selective information (left vs. right tilted orientation as measured by decoding from BOLD signal) for the irrelevant annulus change between the two central load conditions? Prediction (by load theory): –Feature selective information will be reduced in high load condition

34. Multivariate Decoding example Result: Number of voxels % correct decoded 501001 Low High N voxels Accuracy Result: Feature selective info present and decoded expected actual

35. The reverse-correlation method Hasson et al., (2004)

36. Individual Differences

37. Post – Scanning Questionnaires/ Tests … Select subjects that vary on a specific dimension

38. Thank you…

39. Between- Subject Correlation Hasson et al., (2004)

41. Kahn et al., (2004)

42. Example: Subsequent Memory Kahn et al., (2004)