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Neuroeconomics: the Neurobiology of Decision-Making

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Neuroeconomics: the Neurobiology of Decision-Making Neuroeconomics: The Neurobiology of Decision-Making Ifat Levy Section of Comparative Medicine Department of Neurobiology Interdepartmental Neuroscience Program Yale School of Medicine Harnessing eHealth and Behavioral Economics for HIV Prevention and Treatment April 2012 Overview • Introduction to neuroeconomics • Decision under uncertainty – Brain and behavior – Adolescent behavior – Medical decisions Overview • Introduction to neuroeconomics • Decision under uncertainty – Brain and behavior – Adolescent behavior – Medical decisions Neuroeconomics NeuroscienceNeuronal MentalPsychology states “asEconomics if” models architecture Abstraction Neuroeconomics Behavioral Economics Neuroscience Psychology Economics Abstraction Neuroscience functional MRI VISUAL STIMULUS functional MRI: Blood Oxygenation Level Dependent signals Neural Changes in oxygen Change in activity consumption, blood flow concentration of and blood volume deoxyhemoglobin Change in Signal from each measured point in space at signal each point in time t = 1 t = 2 t = 3 t = 4 t = 5 t = 6 dorsal anterior posterior lateral ventral dorsal anterior medial posterior ventral Anterior The cortex Cingulate Cortex (ACC) Medial Prefrontal Cortex (MPFC) Posterior Cingulate Cortex (PCC) Ventromedial Prefrontal Cortex (vMPFC) Orbitofrontal Cortex (OFC) Sub-cortical structures fMRI signal • Spatial resolution: ~3x3x3mm3 Low • Temporal resolution: ~1-2s Low • Number of voxels: ~150,000 High • Typical signal change: 0.2%-2% Low • Typical noise: more than the signal… High But… • Intact human brain • Behaving human • Whole brain • Non-invasive Neuroeconomics Cognitive Neuroscience Behavioral Economics Neuroscience Psychology Economics Abstraction New challenge: how do you make sense of such huge amounts of data?? Neuroeconomics Mechanistic constraints of the human brain Cognitive Behavioral Neuroscience Economics Economic models as normative theory Overview • Introduction to neuroeconomics • Decision under uncertainty – Brain and behavior – Adolescent behavior – Medical decisions certainty risk ambiguity 100% 50% unknown partial full full relief remission remission Choose one: A B Risky OR Ambiguous Most people choose A, implying that B has fewer red than blue chips: red < blue A B Risky OR Ambiguous Most people choose A, implying that B has fewer blue than red chips: blue < red The Ellsberg paradox: a bag cannot have fewer red chips and fewer blue chips at the same time Non-certain outcomes • Risk – probabilities of different outcomes are known • Ambiguity – probabilities of different outcomes are not known • Partial ambiguity – partial information known probability risk aversion high probability low low probability high reward reward $40 unknown probability ambiguity aversion known probability unknown probability low reward high reward $110$0 $0$110 Value of risk and ambiguity Risk and ambiguity affect the subjective value of an option in very different ways Overview • Introduction to neuroeconomics • Decision under uncertainty – Brain and behavior – Adolescent behavior – Medical decisions Research Question neural representation of value multiple systems single system reward reward punishment punishment immediate delayed cognitive cognitive delayed immediate emotional . emotional . Research Question neural representation of value multiple systems single system ambiguity vs. risk ambiguity & risk x,y,0.5,5 Experimental design Experimental design Amount Probability Ambiguity level OR: - Winning color - $5 Parametric design Real bags One trial played for real money Gain-risk trials p = 0.75 p (chose (chose risky) p Amount [$] $5 subject 1 Gain-risk trials p = 0.75 p (chose (chose risky) p Amount [$] $5 subject 1 Gain-risk trials p = 0.75 p (chose (chose risky) p Amount [$] $5 subject 1 Gain-risk trials p = 0.75 p (chose (chose risky) p Amount [$] $5 subject 1 Gain-risk trials p = 0.75 p (chose (chose risky) p Amount [$] $5 subject 1 Gain-risk trials p = 0.75 p = 0.50 p (chose (chose risky) p Amount [$] $5 subject 1 Gain-risk trials p = 0.75 p = 0.50 p (chose (chose risky) p p = 0.38 Amount [$] $5 subject 1 Gain-risk trials p = 0.75 p = 0.50 p (chose (chose risky) p p = 0.38 p = 0.25 Amount [$] $5 subject 1 Gain-risk trials p = 0.75 p = 0.50 p (chose (chose risky) p p = 0.38 p = 0.25 p = 0.13 Amount [$] $5 subject 1 Gain ambiguity trials A = 0.25 p (chose (chose ambiguous) p Amount [$] $5 subject 1 Gain ambiguity trials A = 0.25 p (chose (chose ambiguous) p Amount [$] $5 subject 1 Gain ambiguity trials A = 0.25 A = 0.50 p (chose (chose ambiguous) p Amount [$] $5 subject 1 Gain ambiguity trials A = 0.25 A = 0.50 A = 0.75 p (chose (chose ambiguous) p Amount [$] $5 subject 1 Behavioral model MaxMin, Gilboa and Schmeidler 1989 subjective value ambiguity level − β A α ( p ) · V risk 2 preference amount probability ambiguity aversion stochastic choice model S1: gains S2: gains α = 0.55, β = 0.89 α = 0.58, β = -0.03 p = 0.75 p = 0.50 p = 0.38 p = 0.25 p = 0.13 A = 0.25 A = 0.50 p (chose p (chose lottery) A = 0.75 Amount [$] Amount [$] S1: gains S1: losses α = 0.55, β = 0.89 α = 0.58, β = -0.04 p = 0.75 p = 0.50 p = 0.38 p = 0.25 p = 0.13 A = 0.25 A = 0.50 p (chose p (chose lottery) A = 0.75 Amount [$] Amount [$] Gains Losses Ambiguity aversion Ambiguity Risk aversion Risk aversion Risk aversion Ambiguity aversion Losses Gains Gains A ( p − β )⋅V α 2 under risk subjectivevalue under ambiguity … … time Subjective value under ambiguity ACC / MPFC R L caudate posterior amygdala cingulate 19 subjects, random effect analysis P<0.002 P<0.0001 Subjective value under risk ACC / MPFC R L caudate posterior amygdala cingulate 19 subjects, random effect analysis P<0.01 P<0.001 Unique areas for SV under ambiguity? PCC amygdala No… %signal change %signal change risk ambiguity risk ambiguity Research Question neural representation of value multiple systems single system ambiguity vs. risk ambiguity & risk x,y,0.5,5 Uncertainty Summary 1 • High variability in risk and ambiguity attitudes across individuals • Areas in MPFC and striatum represent subjective value under both risk and ambiguity Can attitudes towards risk and ambiguity explain phenomena like risk-taking in adolescents and overeating in obese individuals? Overview • Introduction to neuroeconomics • Decision under uncertainty – Brain and behavior – Adolescent behavior – Medical decisions Adolescents take risks • 200% increase in morbidity and mortality rates in adolescence compared to childhood (Dahl, 2004) • Adolescents are physically healthier and stronger than both children and adults (Dey et al., 2004) • Increase mostly attributed to risky behaviors: car accidents, alcohol and substance abuse, violence, eating disorders, unsafe sex (Reyna and Farley, 2006) • Not due to flawed reasoning capabilities, poor decision- making skills or failure to understand the consequences of their actions (Reyna and Farley, 2006) Subjects Age \ Female Male Total Gender 12-17 17 16 33 21-25 18 16 34 30-50 17 15 32 65-90 18 17 35 Total: 70 64 134 In collaboration with Paul Glimcher Adolescents vs. adults Uncertainty Summary 2 • Adolescents are more risk averse, but less ambiguity averse than adults • Young organisms need to learn about their world Do people treat risk and ambiguity similarly in different domains? In collaboration with Terri Fried Overview • Introduction to neuroeconomics • Decision under uncertainty – Brain and behavior – Adolescent behavior – Medical decisions “You were involved in a car accident and as a result suffered traumatic brain injury. You were immediately rushed to the nearest hospital and were informed by the doctor that without immediate treatment you will not survive.” Gains and losses in medical decisions Gains: cognitive improvement No effect Slight improvement Moderate improvement Major improvement Recovery Worst outcome Best outcome Major improvement = Mild cognitive disability: mild memory impairment resulting in forgetting some appointments, forgetting people’s names, needing a list to do food shopping No effect Slight improvement or recovery Major improvement Slight improvement or No effect Gains and losses in medical decisions Losses: headache as an adverse effect Critical headache Severe headache Moderate headache Mild headache Recovery Worst outcome Best outcome Moderate headache: improves but does not resolve with acetaminophen (Tylenol); requires you to lie down occasionally to relieve pain; occurs a couple of times a week. moderate headache Mild headache or recovery Severe headache Mild headache or recovery Decision under risk 1 Positive outcomes 1 Negative outcomes 0.9 0.9 0.8 0.8 0.7 0.7 0.6 0.6 0.5 0.5 0.4 0.4 0.3 0.3 0.2 0.2 Proportionof choices lottery 0.1 0.1 0 0 0.13 0.25 0.38 0.5 0.75 0.13 0.25 0.38 0.5 0.75 Outcome probability Outcome probability Money Medical N = 29 Decision under ambiguity 1 Money 1 Medical 0.9 0.9 0.8 0.8 0.7 0.7 0.6 0.6 0.5 0.5 0.4 0.4 0.3 0.3 Proportionof choices lottery 0.2 0.2 0.1 0.1 0 0 gain loss improvement adverse effect Ambiguity level: 0 0.25 0.50 0.75 N = 29 Uncertainty Summary 3 • Ambiguity aversion was observed both under gains and under losses when making medical decisions Summary • Economic models can be used to make sense of neural data • High variability across subjects in attitudes towards risk and ambiguity • Adolescents are more risk averse and less ambiguity averse than adults • Ambiguity aversion in medical decision- making for both positive and negative outcomes Acknowledgements Lab Collaborators Sarah Abdallah Paul Glimcher, NYU Jennifer Fanning Aldo Rustichini,
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