Modeling in psychology: What’s the point?

1. Modeling in psychology:What’s the point? Robert M. French L.E.A.D. – CNRS U. de Bourgogne

2. What do we mean by a “model” The reduction of a phenomenon to its essential elements as a means of explaining it.

3. A good model must: • Reproduce, at least qualitatively, the existing empirical data. • Explain this data. • Make predictions that go beyond the existing data. • Clearly indicate its context of application (i.e., its level of explanation). • Be able to be probed in order to how its mechanisms produce the phenomena being modeled and to understand the limits of the model. • Be falsifiable.

4. A probabilistic model/theory of the world Theory of the World Assume that past experience shows that an event A is as frequent as an event B. But suppose that for a period of time, we have only seen event A. We conclude that event B should become more likely until event B “catches up” to event A. For example, suppose we get the following sequence of heads and tails: ? T H H H T T H T H H T H T T T T T T H Despite our naive theory: p(H) = ½, p(T) = ½

5. A little more complicated… car goat goat

6. 1 2 3 The host of a TV game show puts a goat behind two of the doors and the new car behind the other. He picks a volunteer from the audience and explains that there are goats behind two of the doors and a new car behind the other door. He invites the volunteer to pick a door.

7. The volunteer picks Door No. 3. 1 2 3

8. 1 3 2 The television host then opens Door No. 2, behind which is a goat.

9. ? 1 3 2 He asks the volunteer if he wants to change his choice to Door 1 or keep Door 3. He may keep whatever is behind the door of his final choice.

10. ? 1 3 2 He asks the volunteer if he wants to change his choice to Door 1 or keep Door 3. He may keep whatever is behind the door of his final choice.

11. Our experience (probabilistic model of the world) tells us that when there are two options and we don’t know the outcome of either, we choose at random. In this case, this theory leads us to the wrong conclusion, because... ....WE MUST CHANGE DOORS !

12. A probabilistic model of basketball: an explanation of the « hot hand ». Thomas Gilovich, Robert Vallone, and Amos Tversky recorded every basket shot by players for the Philadelphia 76er’s during the 1980-81 season. *Gilovich et al. (1985). The Hot Hand in Basketball. On the Misinterpretation of Random Sequences. Cognitive Psychology, 17, 295-314.

13. Their model of success at scoring in basketball Statistically, if each shot is modeled as an INDEPENDENT EVENT, the performance of a player can be modeled with a coined biased so that it reflects the seasonal shooting average of the player. Heads = basket made; tails = basket not made. According to this model, there is no such thing as a « hot hand ».

14. 1 = basket made; 0 = basket missed Not great, not bad Warm up… 0 1 1 0 1 1 1 0 1 1 0 1 0 0 0 0 0 1 1 0 0 01 1 1 1 0 0 1 1 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1 1 0 0 0 0 1 0 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 0 1 0 0 0 1 0 0 0 1 0 1 1 0 0 1 1 1 1 1 0 0 0… Pass me the ball, I’m HOT! Percentages of shots made: 55% Simulated by a 55/45 biased coin and the voice of a basketball player….

15. Some reactions • There are so many variables involved in making a shot, that this study makes no sense. -- Bobby Knight, Indiana U. • “Who is this guy? He does a study, so what? I don’t give a damn what he found. -- owner of the Boston Celtics • “We once did a lot of sport. You tell ME what’s wrong here. It’s for you to explain, not for me to believe. -- my brother.

16. Consider our criteria • Reproduces, at least qualitatively, the empircal data  • Explains the data. • Makes predictions • Clearly indicates its context of application. • Can be probed to discover its limits and to understand how its underlying mechanisms produce the effects being modeled. NON • Is falsifiable. Conclusion: It’s a pretty good model of scoring in basketball.

17. Models and Prediction The Delphi Oracle predicts: An earthquake will level Athens in 8 days. Migratory birds will fly to Africa a month early this year. Sun spots will begin at the end of 2007 this year.

18. Models and Prediction The Black Box predicts: An earthquake will level Athens in 8 days. Migratory birds will fly to Africa a month early this year. Sun spots will begin at the end of 2007 this year. The Black Box has no explanatory power and is, therefore, not a model

19. A model of how the cock crows • Reproduces, at least qualitatively, the empircal data  • Explains the data. • Makes predictions • Clearly indicates its context of application. • Can be probed to discover its limits and to understand how its underlying mechanisms produce the effects being modeled. • Is falsifiable.

20. Let’s examine a little more closely the following three criteria • Clearly indicates its context of application. • Can be probed to discover its limits and to understand how its underlying mechanisms produce the effects being modeled. • Is falsifiable.

21. Clearly indicates its context of application. Without this, every model becomes false, simply by saying, « Well, it doesn’t explain this lower level. » Neuroscientists do this all the time: « Where are the GABA receptors in your model? » etc. For this reason, you must specify the level at which your model is designed to explain things.

22. Can be probed to discover its limits and to understand how its underlying mechanisms produce the effects being modeled. Without this, we cannot have the permanent and necessary interaction between the model and empirical data.

23. Is falsifiable Astrology Freudian psychoanalysis Greek/Hindu/Western mythology. Evolutionary psychology?? All unfalsifiable!

24. Unfalsifiability An interaction between a model and empirical data is necessary BUT It can lead – and too often does lead – to an unfalsifiable model.

25. Boxology: The disease of cognitive modelers

26. Original model Phonological module New Data to explain

27. New data to be explained New data explained Original Model Pre-linguistic Module Phonological module

28. Visual-gusatory module Original Model Pre-linguistic Module Phonological module

29. But this can, and frequently does, lead to “rampant boxological cancer”….

30. Original model

31. ….and, once again, the system loses its explicative power and, along the way, also becomes unfalsifiable.

32. Consider our criteria • Reproduces, at least qualitatively, the empircal data  • Explains the data. MAYBE • Makes predictions NO • Clearly indicates its context of application. NO • Can be probed to discover its limits and to understand how its underlying mechanisms produce the effects being modeled. ??? • Is falsifiable. NO

33. Evolutionary Psychology Falsifiable or not?? In its currently practiced form, the answer is (mostly) NO….

34. Connectionist models

35. What do cows drink?Connectionism provides a “bottom-up” answer: MILK COW MILK DRINK These neurons are active even the network has never heard the word MILK

36. What do cows drink? Symbolic AI gives a “top-down” answer ISA(cow, mammal) ISA(mammal, animal) Rule 1: IF animal(X) AND thirst(X) THEN lacks_water(X) IF lacks_water(X) THEN drink_water(X) Rule 2: Conclusion: Cows drink WATER.

37. A good model of human cognition must be able to give both answers, according to the context in which it is asked the question.

38. We will be looking closely at a connectionist model of categorization in young infants.

39. Connectionist model of Bilingual language learning(French, 1998; French & Jacquet, 2004) • Input to the SRN: • - Two “micro” languages, Alpha & Beta, 12 words each • An SVO grammar for each language • - Unpredictable language switching Attempted Prediction BOY LIFTS TOY MAN SEES PEN GIRL PUSHES BALL BOY PUSHES BOOK FEMME SOULEVE STYLO FILLE PREND STYLO GARÇON TOUCHE LIVRE FEMME POUSSE BALLON FILLE SOULEVE JOUET WOMAN PUSHES TOY.... (Note: absence of markers between sentences and between languages.) The network tries each time to predict the next element. We do a cluster analysis of its internal (hidden-unit) representations after having seen 20,000 sentences.

40. Summary A model is the reduction of a phenomenon (or set of phenomena) to its essential elements as a means of explaining it. It must: • Reproduce, at least qualitatively, the existing empirical data. • Explain this data. • Make predictions that go beyond the existing data. • Clearly indicate its context of application (i.e., its level of explanation). • Be able to be probed in order to how its mechanisms produce the phenomena being modeled and to understand the limits of the model. • Be falsifiable.