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Slide 1-

David Laibson Harvard University Department of Economics Mannheim Lectures July 13, 2009 Lecture 2: Neuroeconomics and the multiple systems hypothesis
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Lecture 2 Outline Neuroeconomics: definition Multiple Systems Hypothesis
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Neuroeconomics: definition. Definition: Neuroeconomics is the study of the biological microfoundations of economic cognition. Biological microfoundations are neurochemical mechanisms and pathways, like brain systems, neurons, genes, and neurotransmitters. Economic cognition is cognitive activity that is associated with economic perceptions, beliefs and decisions, including mental representations, emotions, expectations, learning, memory, preferences, decision-making, and behavior.
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An example: The Multiple Systems Hypothesis Statement of Hypothesis Variations on a theme Caveats Illustrative predictions Cognitive load manipulations Willpower manipulations Affect vs. analytic manipulations Cognitive Function Development Neuroimaging Directions for future research
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Statement of Multiple Systems Hypothesis (MSH) The brain makes decisions (e.g. constructs value) by integrating signals from multiple systems These multiple systems process information in qualitatively different ways and in some cases differentially weight attributes of rewards (e.g., time delay)
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An (oversimplified) multiple systems model System 1 System 2 Integration Behavior
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An uninteresting example Addition Division Integration Behavior What is 6 divided by 3?
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A more interesting example Abstract goal: diet Visceral reward: pleasure Integration Behavior Would you like a piece of chocolate?
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A more interesting example Abstract goal: diet Visceral reward: pleasure Integration Behavior Would you like a piece of chocolate?
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Variations on a theme Interests vs passions (Smith) Superego vs Ego vs Id (Freud) Controlled vs Automatic (Schneider & Shiffrin, 1977; Benhabib & Bisin, 2004) Cold vs Hot (Metcalfe and Mischel, 1979) System 2 vs System 1 (Frederick and Kahneman, 2002) Deliberative vs Impulsive (Frederick, 2002) Conscious vs Unconscious (Damasio, Bem) Effortful vs Effortless (Baumeister) Planner vs Doer (Shefrin and Thaler, 1981) Patient vs Myopic (Fudenburg and Levine, 2006) Abstract vs Visceral (Loewenstein & O’Donoghue 2006; Bernheim & Rangel, 2003) PFC & parietal cortex vs Mesolimbic dopamine (McClure et al, 2004)
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Commonalities between classification schemes Affective system fast unconscious reflexive myopic effortless Analytic system slow conscious reflective forward-looking (but still prone to error: heuristics may be analytic) self-regulatory effortful and exhaustible
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Mesolimbic dopamine reward system Frontal cortex Parietal cortex Affective vs. Analytic Cognition mPFC mOFC vmPFC
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Hypothesize that the fronto-parietal system is patient Hypothesize that mesolimbic system is impatient. Then integrated preferences are quasi-hyperbolic Relationship to quasi-hyperbolic model
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where β = (1+x)-1 . Generalized relationship to quasi-hyperbolic model
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Generalized relationship to quasi-hyperbolic model Hypothesize that mesolimbic dopamine system is impatient. Hypothesize that the fronto-parietal system is patient Here’s one implementation of this idea: Ut = ut + b [dut+1 + d2ut+2 + d3ut+3 + ...] (1/b)Ut = (1/b)ut + dut+1 + d2ut+2 + d3ut+3 + ... (1/b)Ut =(1/b-1)ut + [d0ut + d1ut+1 + d2ut+2 + d3ut+3 + ...] limbic fronto-parietal cortex
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Caveats N ≥ 2 The systems do not have well-defined boundaries (they are densely interconnected) Maybe we should not say “system,” but should instead say “multiple processes” Some systems may not have a value/utility representation Making my diet salient is not the same as assigning utils/value to a Devil Dog If you look downstream enough, you’ll find what looks like an integrated system
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Predictions Cognitive Load Manipulations Shiv and Fedorikhin (1999), Hinson, Jameson, and Whitney (2003) Willpower manipulations Baumeister and Vohs (2003) Affect vs. analytic manipulations Rodriguez, Mischel and Shoda (1989) Cognitive Function Benjamin, Brown, and Shapiro (2006), Shamosh and Gray (forth.) Developmental Dynamics Green, Fry, and Myerson (1994), Krietler and Zigler (1990) Neuroimaging Studies Tanaka et al (2004), McClure et al (2004), Hariri et al (2006), McClure et al (2007), Kabel and Glimcher (2007), Hare, Camerer, and Rangel (2009)
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Cognitive Load Should Decrease Self-regulation Shiv and Fedorikhin (1999) Load manipulated by having people keep either a 2-digit or 7-digit number in mind during experiment Subjects choose between cake or fruit-salad
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Cognitive Load Should Increase Discounting Hinson, Jameson, and Whitney (2003) Task: Subjects choose one reward from a set of two or more time-dated rewards. Some subjects are under cognitive load: hold 5-digit string in memory One month discount rate* Control (2 items): 26.3% Treatment (2 items + load): 49.8% Treatment (3 items): 48.4% *Discount rate for 1 month is ln(1+k), where discount function is 1/(1+kt).
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Willpower should be a domain general and exhaustable resource Muraven, Tice and Baumeister (1998) All subjects watch upsetting video clip Treatment subjects are asked to control emotions and moods Control subjects are not asked to regulate emotions All subjects then try to squeeze a handgrip as long as possible Treatment group gives up sooner on the grip task Interpretation: executive control is a scarce resource that is depleted by use Many confounds and many variations on this theme
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Affect Augmentation/Reduction Should Predictably Change Patience Rodriguez, Mischel and Shoda (1989) Task: Children try to wait 15 minutes, to exchange a smaller immediate reward for a larger delayed reward. Manipulations: Control Affect Augmentation: exposure to rewards Affect Reduction: represent the delayed reward abstractly (pretzels are logs, marshmallows are clouds) Results: Ability to wait goes down after affect augmentation Ability to wait goes up after affect reduction
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Delay of Gratification Paradigm
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Individuals with greater analytic intelligence should be more patient Shamosh and Gray (2008) Meta-analysis of the relationship between delay discounting and analytic intelligence e.g. g, IQ, math ability In 24 studies, nearly all found a negative relationship Quantitative synthesis across 26 effect sizes produce a correlation of -0.23 See also Benjamin, Brown, and Shapiro (2006): a standard deviation increase in a subject’s math test score is associated with a 9.3% increase in the subject’s likelihood of choosing patiently See also Rustichini et al. (2008)
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Shamosh and Gray (forthcoming) “Delay discounting paradigms appear to require some of the specific abilities related to both working memory and intelligence, namely the active maintenance of goal-relevant information in the face of potentially distracting information, as well as the integration of complex or abstract information.” Manage affect: “controlling one’s excitement over the prospect of receiving an immediate reward” Shift attention away from affective properties of rewards Transform reward representation to make them more abstract Deploy strategies Recall past rewards and speculate about future rewards
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Individuals with greater analytic intelligence should be more patient Benjamin, Brown, and Shapiro (2006) Choice task: “500 pesos today vs. 550 pesos in a week” One standard deviation increase in a subject’s math test score is associated with a 9.3% increase in the subject’s likelihood of choosing patiently
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Patience should track PFC development PFC undergoes development more slowly than other brain regions Changes in patience track timing of PFC development Green, Fry, and Myerson (1994) Krietler and Zigler (1990) Galvan (2008) Cross-species evidence is also supportive Monkeys have a 50% discount for juice rewards delayed 10 seconds (Livingstone data)
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Neural activity in frontoparietal regions should be differentiated from neural activity in mesolimbic dopamine regions Not clear. At least five studies of classical delay discounting: McClure et al (2004) – supportive McClure et al (2007) – supportive Kable and Glimcher (2007) – critical Hariri et al (2007) – supportive Hare, Camerer, and Rangel (2009) – supportive (integration) And one other study that is related (and supportive): Tanaka et al (2004): different behavioral task
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McClure, Laibson, Loewenstein, Cohen (2004) Intertemporal choice with time-dated Amazon gift certificates. Subjects make binary choices: $20 now or $30 in two weeks $20 in two weeks or $30 in four weeks $20 in four weeks or $30 in six weeks
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$20 $20 $20 $30 $30 $30 Fronto-parietal cortex Fronto-parietal cortex Fronto-parietal cortex Meso-limbic dopamine
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y = 8mm x = -4mm z = -4mm 0 7 T1 0.2% areas respond “only” to immediate rewards BOLD Signal 2 sec Time
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All voxels significant at p < 0.001 b Analysis Summary of Significant Voxels
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x = 44mm x = 0mm 0 15 T1 VCtx PMA RPar DLPFC VLPFC LOFC d Areas respond equally to all rewards
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d Analysis: Summary of Significant Voxels All voxels significant at p < 0.001
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0% 25% 50% 75% 100% 1-3% 5-25% 35-50% P(choose early) Difficult Easy 2.5 3.0 3.5 4.0 4.5 Difficult Easy Effect of Difficulty Response Time (sec) (R’-R)/R 1-3% 35-50% 5-25% Sec Sec BOLD Signal BOLD Signal
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0.0 -0.05 0.05 Choose Immediate Reward Choose Delayed Reward DRS Fronto-parietal cortex Brain Activity Brain activity in the frontoparietal system and mesolimbic dopamine reward system predict behavior (Data for choices with an immediate option.)
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McClure, Ericson, Laibson, Loewenstein, Cohen (2007)
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Free (10s max.) 2s Free (1.5s Max) Variable Duration 15s (i) Decision Period (ii) Choice Made (iii) Pause (iv) Reward Delivery 15s 10s 5s iv. Juice/Water squirt (1s ) … Time i ii iii A B Figure 1
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d d'-d (R, R')  { This minute, 10 minutes, 20 minutes }  { 1 minute, 5 minutes }  {(1,2), (1,3), (2,3)} Experiment Design d = This minute d'-d = 5 minutes (R, R') = (2,3)
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P(choose early) d = delay to early reward Behavioral evidence
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P(choose early) 0 0.2 0.4 0.6 0.8 Now 10 Minutes 20 Minutes d’-d = 5 min d’-d = 1 min d = delay to early reward d = delay to early reward Behavioral evidence
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Discount functions fit to behavioral data Mesolimbic Cortical β = 0.53 (se = 0.041) δ = 0.98 (se = 0.014) = 0.47 (se = 0.101) d = 1.02 (se = 0.018) Evidence for two-system model Can reject exponential restriction with t-stat > 5 Double exponential generalization fits data best
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y = 12mm VStr SMA Ins 0 11 T 2s 0.2% Juice Water Juice and Water treated equally (both behavioral and neurally) Time (2 second increments)
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Figure 4 x = -12mm x = -2mm x = -8mm z = -10mm NAcc MOFC/SGC ACC PCu PCC NAcc ACC SGC PCu x = 0mm x = 40mm x = -48mm PCC SMA/PMA Vis Ctx PPar BA10 Ant Ins BA9/44 BA46 0 11 T A B  areas: respond only to immediate rewards  areas: respond to all rewards Neuroimaging data estimated with general linear model.
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Figure 5 x = 0mm x = -48mm x = 0mm y = 8mm Primary only Money only Both areas (p<0.001) areas (p<0.001) Relationship to Amazon experiment:
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Figure 5 Primary only Money only Both x = 0mm x = -48mm x = -4mm y = 12mm areas (p<0.01) areas (p<0.01) Relationship to Amazon experiment:
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Fitting discount functions using neuroimaging data. β = 0.963 (0.004) 0.96325 = 0.39 δ=0.990 (0.003) 0.99025 = 0.78 N(t) = βdR + βd'R' + X(t)·θ + ε(t) N(t) = δdR + δd'R' + X(t)·θ + ε(t) β systems d systems
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Measuring discount functions using neuroimaging data Impatient voxels are in the emotional (mesolimbic) reward system Patient voxels are in the analytic (prefrontal and parietal) cortex Average (exponential) discount rate in the impatient regions is 4% per minute. Average (exponential) discount rate in the patient regions is 1% per minute.
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What determines immediacy? Is mesolimbic DRS activation associated with relatively “early” (or earliest) options? or Do juice and money have different discount functions? or Does thirst invoke more intense discounting?
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Our working hypotheses. One system associated with midbrain dopamine neurons (mesolimbic DRS) shows high sensitivity to time delay. Second system associated with lateral prefrontal and posterior parietal cortex shows less sensitivity to time delay. Combined function of these two systems explains decision making across choice domains.
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Hare, Camerer, and Rangel (2009) + 4s food item presentation ?-?s fixation Rate Health Rate Health + Rate Taste Rate Taste + Decide Decide Health Session Taste Session Decision Session
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Rating Details Taste and health ratings made on five point scale: -2,-1,0,1,2 Decisions also reported on a five point scale: SN,N,0,Y,SY “strong no” to “strong yes”
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Subjects SC (self-control) group = 19 dieting subjects who showed self-control during the decision phase NSC (no self-control) group = 18 comparison subjects who did not exhibit self-control during the decision phase
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Who is classified as a self-controller: SC? (must meet all criteria below) Use self-control on > %50 of trials in which self-control is required (decline Liked-Unhealthy items or choose Disliked-Healthy ones) Decision = 1HR + 2LR +  1> 2 R2 for HR > R2 for LR
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Examples of individual behavioral fits Self-controller Non- self-controller
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Disliked Healthy Disliked Unhealthy Liked Unhealthy Liked Healthy Percent Yes Result: NSC group chose based on taste
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* * Disliked Healthy Disliked Unhealthy Liked Unhealthy Liked Healthy Percent Yes Result: SC group chose based on taste and health
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* * Disliked Healthy Disliked Unhealthy Liked Unhealthy Liked Healthy Percent Yes SC group versus NSC group
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Question: Is there evidence for a single valuation system? Neuroimaging Results
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Activity in vmPFC is correlated with a behavioral measure of decision value (regardless of SC) L p < .001 p < .005
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* * * Taste Rating Health Rating Beta vmPFC BOLD signal reflects both taste and health ratings
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BOLD Health Rating Beta Decision Health Rating Beta The effect of HR in the vmPFC is correlated with its effect on behavior Robust reg Coef = .847
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Neuroimaging Results Question: Does self-control involve DLPFC modulation of the vmPFC valuation network?
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What is self control? Rejecting good tasting foods that are unhealthy? Accepting bad tasting foods that are healthy?
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More activity in DLPFC in successful SC trials than in failed SC trials L p < .001 p < .005
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Future work: Are multiple system models a useful way of generating new hypotheses and models? Are these systems localized? If so, where? How do the systems communicate? How are the inputs integrated? When are the systems cooperative and when conflictual? When they are in conflict, are they strategic? What manipulations enhance or weaken the signals coming from these systems? Can we influence individual systems in the lab? Can we influence individual systems in the field? Can we produce useful formalizations of their operation?