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Slides for keynote lecture at OHBM 2017.

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- 1. GENERALIZABILITY IN FMRI, FAST AND SLOW TalYarkoni
- 2. GENERALIZATION MATTERS • Induction (reasoning from the speciﬁc to the general) is central to the scientiﬁc method • Most ﬁndings in neuroimaging are only interesting because we expect them to generalize broadly
- 3. happy pandas
- 4. sad pandas
- 5. neutral pandas
- 6. LOGIC OF SUBTRACTION - Experimental manipulation Emotion-related neural changes =
- 7. BRAINS ON PANDAS
- 8. WHAT SHOULD WE CONCLUDE? • We studied a small number of subjects responding to a small number of panda images at one research site with one experimenter • What universe of observations are we are entitled to generalize these conclusions to? • Can we just choose our generalization strategy? • Consider two different approaches: fast vs. slow
- 9. FAST GENERALIZATION • Dream big! • Paper title:“Increased amygdala activation in response to affective mammalian expressions relative to neutral mammalian expressions” • Seems pretty interesting! • But how many inductive leaps did we just take?
- 10. SLOW GENERALIZATION • Careful now… • Paper title:“Greater amygdala activation in 22 UT- Austin undergraduates when viewing images angsty_tree_panda.jpg and panda_delight.png than image panda_goes_meh.jpg” • Boooooooooring • Which of these titles do you prefer? Fast or slow?
- 11. NOT A MATTER OF PREFERENCE • We typically prefer our conclusions to hold for stimuli like the ones we sampled, not just those exact ones • Unfortunately, our intentions and preferences have very little to do with it • The statistical model must support the desired inference • If we want to generalize to new stimuli, our model must account for the variance introduced by the stimulus sampling process
- 12. Tom NicholsJake Westfall THANKSTO…
- 13. RANDOMVS. FIXED EFFECTS • Many deﬁnitions in the literature (Gelman, 2005) • For our purposes: effects are ﬁxed if the observed levels are of intrinsic interest, and random if they are only interesting as exemplars of an underlying population • E.g., subjects are almost always treated as a random factor • We rare don’t care about these particular subjects • In an N-back task, load is considered ﬁxed • We don’t sample 2-back and 3-back from a population of N’s
- 14. WHY BOTHER WITH RANDOM EFFECTS? • Suppose we have 100 trials for each of 20 subjects —50 per experimental condition • The appropriate one-sample t-test is conducted at the level of subjects, not trials.Why? • Answer: because we intend to generalize to new subjects, and trials are correlated within subjects
- 15. WHY STOP AT SUBJECTS? • In fMRI, subjects are almost always models as random effects • Stimuli are virtually never modeled as random (but see Donnet, Lavielle, & Poline, 2006; Bedny,Aguirre, &Thompson-Schill, 2007) • Yet the logic is (or should be) exactly the same for stimuli (Coleman, 1964; Clark, 1975)
- 16. Westfall, Nichols, &Yarkoni (2017)
- 17. Westfall, Nichols, &Yarkoni (2017)
- 18. SIMULATION • What happens if we don’t include random stimulus effects? • Assume: • No true difference between sad & neutral pandas (d = 0) • Roughly equal subject and stimulus variances ( ) • We sample 100 subjects and 16 stimuli (8 per condition)
- 19. Westfall, Nichols, &Yarkoni (2017)
- 20. Judd,Westfall, & Kenny (2012)
- 21. Westfall, Nichols, &Yarkoni (2017)
- 22. Westfall, Nichols, &Yarkoni (2017) HEY, WHA’ HAPPENED?
- 23. Westfall, Nichols, &Yarkoni (2017)
- 24. Westfall, Nichols, &Yarkoni (2017)
- 25. THE BAD NEWS • This problem is pervasive in the fMRI literature • We estimate that it affects at least 50% of published studies • The magnitude of the problem is often severe • In most of our test datasets, we see effect sizes drop by 50 - 90% • Under realistic assumptions, the FPR in the literature is likely to be high • No support for RSMs in any major analysis packages
- 26. THE GOOD NEWS • The behavior is (relatively) predictable • When stimulus sample is large, RSM and standard models converge • Moral of the story: use large stimulus samples! • It’s helpful to present different stimuli to different subjects
- 27. Westfall, Nichols, & Yarkoni (2017)
- 28. GENERALIZINGTHE GENERALIZATION PROBLEM • Subjects and stimuli are not the only thing we care to generalize over • Same logic applies to many other random factors • Scanner, research site, experimenter, font face, etc. • Most of these factors do not vary in a given experiment • The actual false positive rate has to be even higher • Unless you explicitly specify that you’re not interested in generalizing to new levels of the non-varying factors
- 29. INFERENCEVS. PREDICTION • What goes for inference also holds for prediction • Instead of random effects, we can talk about cross- validation schemes • In context of stimuli, we can train and test on different subsets of stimuli • And similarly for other random factors
- 30. Woo, Chang, Lindquist, & Wager (2017)
- 31. Huth et al. (2016)
- 32. WHICH POPULATION? • Modeling a factor as random allows us to generalized to a population • This doesn’t mean it’s the intended population! • In general, we don’t actually know what the population is • E.g., random subject effects allow us to generalize to new subjects from the same population • But those subjects can still be WEIRD! (Henrich et al., 2010)
- 33. GENERALIZATION PATTERNS • Given a design factor (e.g., stimuli, subjects, sites, etc.), what does it take to support generalization? • Conceptually similar approaches required for inference and prediction/classiﬁcation
- 34. Generalization target Example Inference Prediction/ classiﬁcation No population Speciﬁc photos of Amy and Ramesh Ignore F Ignore F A population The population of Ekman faces Model F as a random factor Train and test on different subsets of F Intended population The natural distribution of photos of facial expressions Sample widely; include more complex random effects; use post- stratiﬁcation methods Sample widely; train and test on different sets of F; use sophisticated cross-validation schemes
- 35. CONCLUSIONS • Generalization is hard • We almost universally overgeneralize our conclusions in fMRI • This has serious consequences for false positive rates, predictive performance estimates, etc. • There are means of addressing the problem via both modeling (add random effects or cross-validate) and design (sample more levels) • We should probably all make an effort to generalize more slowly
- 36. A mathematician, a physicist, and an astronomer were traveling north by train.They had just crossed the border into Scotland, when the astronomer looked out of the window and saw a single black sheep in the middle of a ﬁeld. “Scottish sheep are black,” he remarked. “No, my friend,” said the physicist.“Some Scottish sheep are black.”At which point the mathematician looked up from his paper and said: “In Scotland, there exists at least one ﬁeld, in which there exists at least one sheep, at least one side of which is black.”

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