Determining how both humans and animals make decisions in risky situations is a central problem in economics, experimental psychology, behavioral economics, and neurobiology. Typically, humans are risk seeking for gains and risk averse for losses, while animals may display a variety of preferences under risk depending on, amongst other factors, internal state. Such differences in behavior may reflect major cognitive and cultural differences or they may reflect differences in the way risk sensitivity is probed in humans and animals. Notably, in most studies humans make one or a few choices amongst hypothetical or real monetary options, while animals make dozens of repeated choices amongst options offering primary rewards like food or drink. To address this issue, we probed risk-sensitive decision making in human participants using a paradigm modeled on animal studies, in which rewards were either small squirts of Gatorade or small amounts of real money. Possible outcomes and their probabilities were not made explicit in either case. We found that individual patterns of decision making were strikingly similar for both juice and for money, both in overall risk preferences and in trial-to-trial effects of reward outcome on choice. Comparison with decisions made by monkeys for juice in a similar task revealed highly similar gambling styles. These results unite known patterns of risk-sensitive decision making in human and nonhuman primates and suggest that factors such as the way a decision is framed or internal state may underlie observed variation in risk preferences between and within species.
Risk Primary reinforcer Neuroeconomics
This is a preview of subscription content, log in to check access
We thank Jason-Flor Sisante for helping to set up the human experimental system. We thank Ashley Nutter and Cameron Martin for collecting the data, and the SROP program at Duke for financial support. Experiments were performed in compliance with the laws of the United States.
Barraclough DJ, Conroy ML, Lee D (2004) Prefrontal cortex and decision making in a mixed-strategy game. Nat Neurosci 7:404–410PubMedCrossRefGoogle Scholar
Kuhberger A, Schulte-Mecklenbeck M, Perner J (1999) The Effects of framing, reflection, probability, and payoff on risk preference in choice tasks. Organ Behav Hum Decis Process 78:204–231PubMedCrossRefGoogle Scholar
Rachlin H (2000) The science of self-control. Harvard University Press, CambridgeGoogle Scholar
Reboreda J, Kacelnik A (1991) Risk sensitivity in starlings: variability in food amount and food delay. Behav Ecol 2:301–308CrossRefGoogle Scholar
Samuelson PA (1963) Risk and uncertainty: a fallacy of large numbers. Scientia 98:108–113Google Scholar
Schuck-Paim C, Pompilio L, Kacelnik A (2004) State-dependent decisions cause apparent violations of rationality in animal choice. PLos Biol 2:12CrossRefGoogle Scholar
Shafir S, Wiegmann DD, Smith BH, Real LA (1999) Risk-sensitive foraging: choice behaviour of honeybees in response to variability in volume of reward. Anim Behav 57:1055–1061PubMedCrossRefGoogle Scholar
Stephens DW, Krebs JR (1986) Foraging theory. Princeton University Press, PrincetonGoogle Scholar
Sugrue LP, Corrado GS, Newsome WT (2004) Matching behavior and the representation of value in the parietal cortex. Science 304:1782–1787PubMedCrossRefGoogle Scholar
Sugrue LP, Corrado GS, Newsome WT (2005) Choosing the greater of two goods: neural currencies for valuation and decision making. Nat Rev Neurosci 6:363–375PubMedCrossRefGoogle Scholar
Sutton RS, Barto AG (1998) Reinforcement learning: an introduction. MIT Press, CambridgeGoogle Scholar
Weber EU, Shafir S, Blais AR (2004) Predicting risk sensitivity in humans and lower animals: risk as variance or coefficient of variation. Psychol Rev 111:430–445PubMedCrossRefGoogle Scholar