Integrating Motivation and Emotion into Decision Making

1. Integrating Motivation and Emotion into Decision Making Jerome R. Busemeyer Ryan K. Jessup Indiana University jbusemey@indiana.edu http://mypage.iu.edu/~jbusemey/ Modeling Integrated Cognitive Systems Systems, Saratoga Springs NY

2. What systems are we trying to integrate? • Problem Solving • Generate plans to accomplish goals • Plans are action- event sequences := courses of action • Judgment • Estimate likelihood of events that occur in a plan • Evaluate importance of consequences produced along the paths of a plan • Decision Making • Select a course of action that has uncertain but important consequences • E.g. Decide whether or not to pass a truck on a dangerous two lane highway • Motivation • Persistentneeds that arouse and energize long term goals • Hunger, Sex, Curiosity, Security, Power, ect. • Emotions • Temporary states reflecting current changes in motivation • Joy (e.g. gain of power) vs. Anger (e.g. loss of power) • Hope (anticipated gain) vs. Fear (anticipated loss) • Affect • State evaluation in terms of positive versus negative feelings • Anger  negative feeling, Joy  positive feeling • Mood • Lingeringaffect that moderates cognitive processing (beliefs) • E.g. good mood  optimism vs. bad mood  pessimism

3. What are the Bases of Motivational and Emotional Experiences (E.g., Fear)? • Neuro activation • Brain Activation (Fear–increase, Sadness–decrease) • Neurotransmitter release (GABA–inhibition, Dopamine– reward) • Hormonal response of the Endocrine system • Adrenaline (epinephrine) tension – anxiety flight • Noradrenaline (norepinephrine) aggression fight • Physiological reaction of autonomic nervous system • Pupil size, heart rate, respiratory rate • Galvanic skin conductance (perspiration), skin temperature • Behavioral Preparation • Facial expressions (Tomkins, Izard, Ekman) body posture • Programmed reactions and coping responses (flight or flight) • Cognitive Interpretation (more on next slide) • Appraisal and interpretation of above reactions • James-Lange theory (Schacter & Singer, 1962; Lazarus, 1991; Weiner, 1986)

5. Two System View of Motivation and Emotion(Buck,1984; Gray, 1994; Ledoux, 1996; Levenson,1994; Sherer, 1994; Panksepp,1994; Zajonc, 1980) • Subcortical Direct Route • Fast, spontaneous, unconscious, physiological, involuntary reaction • Thalamus  Amygdala  motor cortex, limbic circuit) • Neocortical Indirect Route: • Slower, conscious, appraisal, coping response (indirect path through Thalamus  Sensory Cortex  Prefrontal Cortex  Amygdala  motor cortex, neocortical circuit) • Integration of emotion and cognition • Orbital (ventral medial) prefrontal cortex center for integration emotion and cognition (Damasio, 1994)

6. Two System View of Decision Making(Epstein, 1994; Kahneman & Frederick, 2002; Loewenstein & O’Donoghue, 2004;Metcalf & Mischell, 1999; Slovic & Peters, 2000; Sloman, 1996,) • Heart: • Emotional, Intuitive, Affective, based system • Implicit, unconscious, automatic, associative, fast, parallel, non-compensatory, experiential, contextual • Little demands on working memory • Mind: • Rational, Analytic, Reasoning based system • Explicit, conscious, controlled and deliberative, slow, serial, compensatory, comprehensive, abstract • Large demands on working memory • Heart is corrected by Mind at a cost of working memory (willpower).

7. Do we need to change decision theory for emotional consequences? • Regreteffects(Zeelenberg & Beattie, 1997, OBHDP) • Preferences among gambles change depending on whether or not outcome feedback is given following choice (which provides an opportunity for regret). • Decision weights(Rottenstreich & Hsee. 2001, Psych Sci) • Function is more inverse S-shaped (flat across the intermediate ranges of probabilities) for emotional outcomes. • Discount Rates(Loewenstein & Lerner, 2003) • Higher discount rates are obtained using emotional consequences (e.g., cocaine vs. money for cocaine abusers) • Decision Strategies(Luce, Bettman, Payne, 1997 JEP:LMC) • Switch to non-compensatory strategies to avoid making difficult negative emotional tradeoffs.

8. Can emotions distort our decision processes? • Emotional carry over effects(Goldberg, Lerner, & Tetlock, 1999, European JSP; Lerner, Small & Loewenstein, 2004, Psych Sci) • Anger from watching a murder movie spills over and influences judgments of punishments for unrelated crimes. • Emotional films affects subsequent prices for gambles • Emotions overwhelm reasons (Shiv and Fedorikhim (1999, JCR) • When given a choice between a healthy and unhealthy snack, participants generally choose the health snack • But with hungeraroused (tested before lunch) and healthy thoughts suppressed (by a working memory task), then the Unhealthy snack was preferred.

9. Does reasoning always improve decision making? • Over-emphasis on Reasons (Wilson & Schooler (1993, PSPB) • Participants were asked to choose a poster to take home • One group gave a list reasons prior to the choice • Second simply used their intuitive feelings • Six weeks later the group who focused on reasons were less pleased with their choice compared to the intuitive group.

10. Can we predict the effect of motivation on our decisions? • Hot-Cold Empathy Gaps(Loewenstein & Lerner, 2003, Read & Van Leeuwen, 1998, OBHDP) • When in a cold state, (not hungry), people under predict how they will feel in a hot state (hungry) • When in a hot state (sexually aroused) people under predict how they will later feel when in a cold state (morning after effect) • A person in a cold state (no pain) cannot predict how a person in a hot state (in pain) will react

11. Does mood bias information processing? • Negative moods (as compared to positive moods) narrow the focus of attention and make people more vigilant and systematic in information processing (Isen, 1999; Schwarz, 1990) • Pleasant moods enhance helping behavior (Baron, 1997) • Positive mood affects risk aversion. (Isen, Nygren, & Ashby, 1998) • Fearful moods generate pessimistic risk assements while anger produces less pessimistic risk assessments (Lerner & Keltner, 2000)

12. Models of the Two System View • Mind: • The decision maker retrieves weights and values from some fixed table (like reading a consumer report magazine). • Utility is computed as the weights times values summed across outcomes • Choose the action producing maximum utility • Heart: • Collection of heuristic rules of thumb • E.g. Lexicographic rule

13. Decision Field Theory: A dynamic and stochastic computational model of decision making • Overview and Summary • Busemeyer, J. R. & Johnson, J. (2004) Computational models of decision making. D. Koehler & N. Harvey (Eds.) Handbook of Judgment and Decision Making, Oxford: UK: Blackwell Publishing Co. Ch. 7, Pp 133-154. • Decision Making Under Uncertainty • Busemeyer, J., & Townsend, J. T. (1993). Decision Field Theory: A dynamic cognitive approach to decision making. Psychological Review, 100, 432-459. • Multi Alternative Preferential Choice • Roe, R. M., Busemeyer, J. R. & Townsend, J. T. (2001) Multi-alternative decision field theory: A dynamic artificial neural network model of decision-making. Psychological Review, 108, 370-392. • Price and Choice Preference Reversals • Johnson, J. J. & Busemeyer, J. R. (2004) A dynamic, stochastic, computational model of preference reversal phenomena. Revision under review for Psychological Review. • Motivational basis of utility • Busemeyer, J. R., Townsend, J. T., & Stout, J. C. (2003) Motivational Underpinnings of Utility in Decision Making: Decision Field Theory Analysis of Deprivation and Satiation. In S. Moore (Ed.) Emotional Cognition. Amsterdam: John Benjamin

14. Example Dynamic Decision • Walter is riding his motorcycle behind a truck on a dangerous two lane highway. The truck is loaded with old tires. Suddenly, the truck hits a bump and a tire bounces down, landing flat on the road directly in Walter’s path. • What course of action should Walter choose? • Screech to a stop to avoid the tire • Swerve to the side and avoid the tire • Speed up and ride straight over the top of the tire

15. Choice Process for Subject Controlled Stopping Time: Random Walk / Diffusion Process Threshold bound controls speed accuracy tradeoffs

16. W M1 C VA PA M2 S VB M3 PB M4 VC PC M5 Evolution of Preference Connectionist Framework M = Motivational values W = attention Weights V = input Valences V(t) = C M(t) W(t) P = Preference state P(t+h) = SP(t) + V(t+h) E[ V ] = MW an Expected Utility P(t) estimates this over time

17. M1 C VA PA M2 S VB M3 PB M4 VC PC M5 Evolution of Preference time t, W(t) V = input Valences V(t) = C MW(t)

18. M1 C VA PA M2 S VB M3 PB M4 VC PC M5 Evolution of Preference time t+h, W(t+h) V = input Valences V(t+h) = C MW(t+h)

19. M1 C VA PA M2 S VB M3 PB M4 VC PC M5 Evolution of Preference time t+2h, W(t+2h) V = input Valences V(t+2h)=C MW(t+2h)

20. Evolution of Preference P = Preference state P(t+h) = SP(t) + V(t+h) M1 C VA PA M2 S VB M3 PB M4 VC PC M5

21. BMW Saturn Multi-Alternative choice paradigm • Binary choices • Add a New Brand • Compare Conditions quality New Brand Quality Choice Probability Economy economics BMW Saturn

22. Similarity Effect (Tversky, 1972, Psychological Review) Pr[X | X,Y]  Pr[Y|X,Y] Pr[X|X,Y,S] < Pr[ Y|X,Y,S] Preference Reversal Violation of Independence from Irrelevant Alternatives Rules out Simple Scalable Class of Models (e.g. Luce’s,1959) ratio of strength model) Explained by Tversky’s (1972) Elimination by Aspects model Y s X

23. Compromise Effect (Simonson, 1989, Journal of Consumer Research) Pr[ C | Y,C] < Pr[ Y | Y,C] Pr[ C | X,Y,C] > Pr[ Y | X,Y,C] Preference Reversal Violation of Independence of Irrelevant Alternatives Cannot be explained by Tversky’s (1972) Elimination by Aspects Model Explained by Tversky & Simonson’s (1992) Loss Aversion Model Y C X

24. Reference Point Effects (Tversky & Kahneman, 1991, Quarterly Journal of Economics) Pr[ X | X,Y,Ry] < Pr[ Y | X,Y,Ry] Pr[ X | X,Y,Rx] > Pr[ Y | X,Y,Rx] Violation of Independence from Irrelevant Alternatives Not explained by Tversky’s (1972) Elimination by Aspects Model Explained by Tversky & Simonson’s (1992) Loss Aversion Model Y Ry Rx X

25. Attraction Effect (Huber, Payne, & Puto, 1982, Journal of Consumer Research) Pr[ X | X,Y] < Pr[ X | X,Y,D] Violation of Regularity Rules out Random Utility Models (e.g. McFadden’s (1982) generalized extreme value model Explained by Tversky & Simonson’s (1992) Loss Aversion Model Y D X

26. Y RY RX X Summary of Findings • Similarity Pr(X|X,Y,S)<Pr(Y|X,Y,S) • Attraction Pr (X|X,Y,D) >Pr (Y|X,Y,D) • Reference Point Pr(X|X,Y, RX)>Pr(Y|X,Y, RX) Pr(X|X,Y, RY)<Pr(Y|X,Y, RY) • Compromise Pr (C|X,Y,C) >Pr (X|X,Y,C) C S D

27. Y C S D X RY Rx Decision field theory predictions X Pr (X) O Pr (Y) + Pr (C) X Pr (X) O Pr (Y) + Pr (Rx) X Pr (X) O Pr (Y) + Pr (Ry) X Pr(X) Pr O (Y) + Pr (S) X Pr (X) O Pr (Y) + Pr (D)

28. Theoretical Requirements for a theory of motivation and decision making • Dynamic Model of Decision Making • Describe the evolution of preferences over time • Integrates traditional decision concepts • Probabilities • Multi-attribute Values • Integrates traditional motivational concepts • Need Stimulation and Attenuation • Satiation – Deprivation

29. Example: Allocating time between work and recreation • Five Conflicting Motives • Career Achievement • Financial Security • Rest and Relaxation • Fun and Enjoyment • Family Relations

30. Q W N V M A G B P Motivational Values: Dynamic Control Problem G = Goal stimulation Q = attribute Quantities A = Achievements on attributes A(t+h) = FA(t) + Q'B(t) M(t) = Q  Diag[N(t)] V(t) = C [Q  Diag[N(t)]W(t)] N = attribute Needs N(t+h)=LN(t)+[G(t+h)-A(t+h)] P(t+h) = S P(t) + V(t) B(t) = f( P(t) )

31. Q W N V M A G B P Motivational Values Clark Hull’s Drive X Incentive N := drive Q := incentive Toate’s Feedback Control System: N := state variable G := control signal (N – A) := the error B := feedback controller Simon (1967) Motivational control over attention

32. Model Example: Recreational versus Work Related Needs Over Time Person A remains under control Person B loses control Stress Intervention at Time = 50

33. Return to Effects of Emotion on Decision making • Decision weights (Rottenstreich & Hsee. 2001, Psych Sci) • Function is more inverse S-shaped (flat across the intermediate ranges of probabilities) for emotional outcomes.

34. Start z1 z3 q11 q22 q33 q12 q23 12 90 98 q21 q31 q13 q24 q32 Exit Process for Generating weights G: .10 .05 .85 $12 $90 $98 Transitions: q13 = .10 q11 = (1- q13)  q12 = (1- q13) (1-) q24 = .05 q22 = (1- q24) q23 = (1- q24) (1-)/2 q21 = (1- q24) (1-)/2 q32 = .85 q33 = (1- q32)  q31 = (1- q32) (1-) [1 0 0] [0 0 1] [1/3 1/3 1/3] [.10 .05 .85] Z := start distribution Z = [ z1 z2 z3 ] Two Free Parameters , z

35. General Solution for Weights P := input vector of objective probabilities for each outcome W := output vector of decision wgts for each outcome Z := Initial state vector Q := State transition matrix W = Z’(I – Q)-1P

36. Example: = .2andZ =[1 0 0] P= [ .10 .05 .85 ]  W = [ .22 .10 .68 ]. If  = 1 and Z= P then W = P In this way, the model can still recover the original probabilities

37. Solution for binary outcomes‘Win X with p otherwise Y with q=(1-p), X>Y

38. Fit of Process model to CPT Wgts

39. Effect of Emotional Outcomes on Decision Weights Predicted by Increasing the Dwell time for emotional consequences • Decision weights(Rottenstreich & Hsee. 2001, Psych Sci) • Function is more inverse S-shaped (flat across the intermediate ranges of probabilities) for emotional outcomes.

40. Conclusions • Motivation and emotion have an important and complex influences on decision making processes. • Many decision theorists posit a dual system for decision making: ‘heart’ vs ‘mind.’ • Expected utility theory is used to model decisions based on the ‘mind’, however no formal model is presented for the decisions based on the ‘heart.’ • Decision field theory provides a formal model that integrates the ‘mind’ and ‘heart’ into a common dynamic system. • In DFT, Motivation/Emotion moderates the amount of attention to consequences. • This agrees with Simon’s (1967) hypothesis that motivation serves as a control mechanism for cognition.