Experimental Design

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

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

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

8. MOTION NO MOTION MAIN EFFECTS A B C D LOW LOAD HIGH • (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

9. MOTION NO MOTION INTERACTION TERMS A B C D LOW LOAD HIGH • (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

10. MOTION NO MOTION Factorial design in SPM5 A B C D LOW LOAD HIGH • 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 A B C D • Main effect of low load: • (A + B) – (C + D) • Interaction term of motion greater under low load: • (A – B) – (C – D) [1 1 -1 -1] A B C D [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. Experimental (A) - Control (B) What we want from a design • Power: Can I detect results? • Interpretability: Can I relate brain data to specific psychological events? • Memory retrieval and comparison processes associated with recognition

16. Block Design • Similar events are grouped ….

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

18. Event Related Design • Events are mixed

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

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

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

22. 3 s picture viewing Timing : the long and the short of it 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 “Friend” Word Recognition Search Episodic Retrieval &Elaboration ~14sc ~5sc 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 representationFlatmesh

27. V2v V1v V1d V2d How do we get ROIs? + = right hemisphere dot localiser retinotopic location of dot

28. Load task 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

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 decodingextracts 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 1 Question: • 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 Decodingexample Result: actual expected Low Accuracy High 1 50 100 % correct decoded N voxels Result: Feature selective info present and decoded Number of voxels

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

36. Individual Differences

37. Individual Differences 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)