1. Decision-making I choosing between gambles neural basis of decision-making

2. Do we always make the best possible decisions? Normative (or prescriptive) theories: tell us how we should make rational decisions –E.g. optimize financial gain Descriptive theories: tell us how we actually make decisions, not on how we should make them. –Satisficing –Heuristics Behavior can deviates from normative account in systematic ways

3. What are rational decisions? Decisions that are internally consistent –E.g., if A>B, then B<A if A>B, B>C, then A>C (transitivity) Decisions that optimize some criterion –E.g. financial gain (expected utility theory)

4. Example What is the best choice? A).50 chance of winning $20 B).25 chance of winning $48

5. Expected Utility Model The utility of an outcome is a numerical score to measure how attractive this outcome is to the decision- maker. The expected utility is the utility of a particular outcome, weighted by the probability of that outcome’s occurring. A rational decision-maker should always choose the alternative that has the maximum expected utility. probabilityutility of receiving $x

6. Example Gamble: if you roll a 6 with a die, you get $4. Otherwise, you give me $1. Take the gamble? Expected utility = p(win)*u(win) + p(lose)*u(lose) =(1/6)*(4)+ (5/6)*(-1) =-1/6 So...do not take bet

7. Utility of money (1) Example 1: –What is the best choice? A).50 chance of winning $20 B).25 chance of winning $48 –Answer can change depending on the utility of winning $10. For somebody who is really hungry and needs a lunch, choice A might be a better bet For most people, the utility of an amount of money is not equivalent to the monetary value.

8. Utility of Money (2) Example 2: What is the best choice? (A).10 chance of winning $10 million dollars (B).99 chance of winning $1 million dollars Each additional dollar added to wealth brings less utility (“diminishing marginal utility effect”)

9. A hypothetical utility curve

10. Individual Differences Decision Maker I (risk avoider) Decision Maker II (risk taker) Monetary Value (in $1000’s) Utility -60 -40 -20 0 20 40 60 80 100 100 60 40 20 80

11. Limitations of the Expected Utility Model We can make “bad decisions”—that is, decisions that are irrational according to the expected utility model –Misestimation of likelihoods –Violations of description invariance  Framing effects –Violations of procedural invariance –Violations of transitivity

12. Example of Framing Effect Problem 1 Suppose I give you $300, but you also have to select one of these two options: (A)1.0 chance of gaining $100 (B).50 chance of gaining $200 and a.50 chance of gaining nothing Problem 2 Suppose I give you $500, but you also have to select one of these two options: (A)1.0 chance of losing $100 (B).50 chance of losing $200 and a.50 chance of losing nothing (72%) (28%) (Tversky & Kahneman, 1986) (36%) (64%)

13. Another example: mental accounting People think of money as belonging to certain categories, but it is really all the same money Problem A. Imagine that you have decided to see a play and paid the admission price of $10 per ticket. As you enter the theater, you discover that you have lost the ticket. The seat was not marked and the ticket cannot be recovered. Would you pay $10 for another ticket? _____ Problem B. Imagine that you have decided to see a play and paid the admission price of $10 per ticket. As you enter the theater, you discover that you have lost a $10 bill. Would you pay $10 for a ticket? _____ (Tversky & Kahneman, 1981)

14. Violations of Description Invariance Problem 1: –Select one of two prizes (36%) An elegant Cross pen (64%) $6 Problem 2: –Select one of three prizes (46%) An elegant Cross pen (52%) $6 (2%) An inferior pen (Shafir & Tversky 1995)

15. Violations of Transitivity Experiment included the following gambles (expected values were not shown): Result: subjects preferred: –D>E, C>D, B>C, A>B, but also E>A (Tversky, 1969)

16. General Problems of Expected Utility Hastie (2001) –Decision making in everyday life is typically much more complex than it is under laboratory conditions Some payoffs cannot be calculated Emotions play a role

17. Complex Decisions: Bounded Rationality People have limitations in memory and time Simon (1957) –Bounded rationality We produce reasonable or workable solutions to problems within limits of human processing –Satisficing We choose the first option that meets our minimum requirements

18. Neural Basis of Decision-Making

19. Neural Bases Of Expected Utility Calculations Glimcher (2003)

20. Fiorillo, Tobler, and Schultz. Science. (2003) Neural Basis of Expected Utility Reward will be delivered with probability one

21. Fiorillo, Tobler, and Schultz. Science. (2003) Reward will be delivered with probability zero Neural Basis of Expected Utility

24. Dalgleish, 2004 Functional Neuroanatomy of Emotions Nucleus Accumbens Prefrontal Cortex Dorsomedial Orbital Hypothalamus Ventral Pallidum Amygdala Anterior Cingulate

25. The Prefrontal Cortex Dorsolateral Orbitofrontal Ventromedial Davidson and Irwin, 1999

26. The Iowa Gambling Task The acts of gaining and losing are not just mental or emotional but profoundly physiological Patients with PFC lesions cannot anticipate feeling of wins or losses. Will gamble to maximize short- term gains Patients with amygdala lesions cannot experience feeling of wins or losses. Without emotional input, “rational” subjects will persist in a losing strategy

27. The Iowa Gambling Task ABCD Four decks: On each trial, the participant has to choose a card from one of the decks. Each card carries a reward, and, sometimes, a loss…

28. The Iowa Gambling Task Four decks: ABCD Each deck has a different payoff structure, which is unknown to the participant. In order to maximize overall gain, the participant has to discover which decks are advantageous and which are not. +$100 −$350

29. The Iowa Gambling Task ABCD Bad DecksGood Decks Reward per card Av. loss per card $100 $50 $125 $25

30. Behavioral Results (Bechara et al., 1999)

31. Skin Conductance Results (Bechara et al., 1999)

32. Results Healthy control participants developed: –“Hunches” about how to maximize wins. –Showed elevated SCR responses in anticipation of outcomes after poor choices. Patients with ventromedial PFC damage: –Performed poorly on task (risky/low payoff choices). Did not maximize wins and losses. –Did not show elevated SCR responses after poor choices. Somatic Marker Hypothesis (Damasio et al., 1996)