Review
Mind the gap: bridging economic and naturalistic risk-taking with cognitive neuroscience

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Economists define risk in terms of the variability of possible outcomes, whereas clinicians and laypeople generally view risk as exposure to possible loss or harm. Neuroeconomic studies using relatively simple behavioral tasks have identified a network of brain regions that respond to economic risk, but these studies have had limited success predicting naturalistic risk-taking. By contrast, more complex behavioral tasks developed by clinicians (e.g. Balloon Analogue Risk Task and Iowa Gambling Task) correlate with naturalistic risk-taking but resist decomposition into distinct cognitive constructs. We propose here that to bridge this gap and better understand neural substrates of naturalistic risk-taking, new tasks are needed that: are decomposable into basic cognitive and/or economic constructs; predict naturalistic risk-taking; and engender dynamic, affective engagement.

Section snippets

Defining risk

When economists and clinical psychologists characterize behavior as ‘risky’, they use the same word but mean different things. Risk in the economics and finance literatures (e.g. [1]) is usually defined in terms of the variance of possible monetary outcomes, and risk seeking is defined as a preference for a higher variance payoff, holding expected value (EV) constant. By contrast, when clinicians and lay people identify behaviors as risky (e.g. drug use, unprotected-sex, or mountain climbing)

Neuroeconomics of risk perception and risk-taking

Since Knight [6], economists have distinguished decision under risk, in which the decision maker knows the objective probability distribution over possible outcomes, from decision under uncertainty, in which this information is assessed with some degree of vagueness (Box 3).

Early neuroimaging studies of risk relied largely on task paradigms (Table 1, Table 2) that manipulate variance in the probability distribution of reward, enabling the identification of neural responses associated with

Individual risk attitudes

A first step towards linking economic models to naturalistic risk-taking is to identify neural systems in which activity is correlated with individual differences in economic risk attitudes. Recent work has shown that many (but not all) of the brain areas that exhibit sensitivity to economic risk (i.e. variance in the probability distribution over possible outcomes) also reveal individual differences that co-vary with risk preferences. Tobler et al. [15] found positive associations between risk

Characterizing the components of naturalistic risk-taking behavior

The neuroeconomic perspective on risk-taking has begun to lay a foundation for understanding how the brain responds to risky monetary payoffs, but the question remains how to bridge the gap with risk-taking in situ. To do so, one first needs to characterize risk-taking in naturalistic environments. A popular inventory of such behaviors, the domain-specific risk-attitude scale (DOSPERT; [28]) identifies five domains of risk-taking (recreational, financial, health, social and ethical) that differ

Decomposing current naturalistic risk-taking tasks

Well-designed neuroeconomic tasks have been relatively decomposable (Table 2), but as discussed above, they often lack external validity. Two prominent behavioral paradigms have had unique success predicting naturalistic risk-taking behaviors. The first is the Iowa Gambling Task (IGT), described in Table 1. The original study using this task showed that patients with vmPFC lesions who exhibited ‘real-life’ risky behaviors were impaired on the task [38] (for a recent fMRI study with healthy

Exhilaration and tension in naturalistic risk-taking

Despite the limitations of BART, it has appealing features. First, as discussed above, it predicts self-reported measures of naturalistic risk-taking reasonably well and distinguishes clinical populations. Second, it uses a familiar naturalistic metaphor that engenders a strong affective response (a sense of escalating tension and exhilaration) that mimics the affective phenomenological experience of risk-taking in naturalistic environments, which could partially explain its capacity to predict

Bridging the gap

To bridge the gap between economic models and naturalistic risk-taking behaviors, we suggest that the former models must incorporate both the positive and negative affective dimensions of risk-taking, through empirical paradigms that can capture them in more compelling ways. We thus propose three criteria for such new laboratory paradigms:(i) Decomposable: the tasks must allow for decomposition and analysis in terms of cognitive and economic primitives (e.g. magnitude of gains and losses, and

Concluding remarks

There is still a great distance to cover in bridging the gap between economic and naturalistic risk-taking, which we suggest will require development of new empirical paradigms. Many existing paradigms exhibit one or two of the three criteria suggested above. For instance, most tasks in the neuroeconomics literature are decomposable but are not especially predictively valid or emotionally engaging. By contrast, tasks in the naturalistic side of the divide, such as the BART and IGT, tend to be

Acknowledgments

We thank Eliza Congdon, Adriana Galvan, Liat Hadar, Brian Knutson, Elke Weber and an anonymous reviewer for their helpful comments on an earlier version of this article. This work was supported by the National Institues of Health (NIH RO1MH082795 to R.P.). T.S. would like to thank the United States-Israel Educational Foundation (Fulbright post-doctoral fellowship) for financial support.

References (95)

  • W.M. Aklin

    Evaluation of behavioral measures of risk taking propensity with inner city adolescents

    Behav. Res. Ther.

    (2005)
  • H. Rao

    Neural correlates of voluntary and involuntary risk taking in the human brain: an fMRI Study of the Balloon Analog Risk Task (BART)

    Neuroimage

    (2008)
  • L. Clark

    Gambling near-misses enhance motivation to gamble and recruit win-related brain circuitry

    Neuron

    (2009)
  • M.P. Paulus et al.

    Anterior cingulate activity modulates nonlinear decision weight function of uncertain prospects

    Neuroimage

    (2006)
  • G.S. Berns

    Nonlinear neurobiological probability weighting functions for aversive outcomes

    Neuroimage

    (2008)
  • A. Pollatsek et al.

    A theory of risk

    J. Math. Psychol.

    (1970)
  • R.K. Sarin et al.

    Risk-value models

    Eur. J. Oper. Res.

    (1993)
  • C.R. Fox et al.

    Ambiguity aversion, comparative ignorance, and decision context

    Organ. Behav. Hum. Decis. Process.

    (2002)
  • S.A. Huettel

    Neural signatures of economic preferences for risk and ambiguity

    Neuron

    (2006)
  • R. Hertwig et al.

    The description-experience gap in risky choice

    Trends Cogn. Sci.

    (2009)
  • V. Venkatraman

    Separate neural mechanisms underlie choices and strategic preferences in risky decision making

    Neuron

    (2009)
  • H. Markowitz

    Portfolio selection

    J. Finance

    (1952)
  • J.G. March et al.

    Managerial perspectives on risk and risk taking

    Manage. Sci.

    (1987)
  • P. Slovic

    Perception of risk

    Science

    (1987)
  • P.W. Glimcher

    Neuroeconomics: Decision Making and the Brain

    (2008)
  • F. Knight

    Risk, Uncertainty and Profit

    (1921)
  • R.D. Rogers

    Choosing between small, likely rewards and large, unlikely rewards activates inferior and orbital prefrontal cortex

    J. Neurosci.

    (1999)
  • C.D. Fiorillo

    Discrete coding of reward probability and uncertainty by dopamine neurons

    Science

    (2003)
  • Y. Niv

    Dopamine, uncertainty and TD learning

    Behav. Brain Funct.

    (2005)
  • K. Preuschoff

    Human insula activation reflects risk prediction errors as well as risk

    J. Neurosci.

    (2008)
  • P.N. Tobler

    Reward value coding distinct from risk attitude-related uncertainty coding in human reward systems

    J. Neurophysiol.

    (2007)
  • P.N. Tobler

    Risk-dependent reward value signal in human prefrontal cortex

    Proc. Natl. Acad. Sci. U. S. A.

    (2009)
  • G. Xue

    Functional dissociations of risk and reward processing in the medial prefrontal cortex

    Cereb. Cortex

    (2009)
  • G.I. Christopoulos

    Neural correlates of value, risk, and risk aversion contributing to decision making under risk

    J. Neurosci.

    (2009)
  • P.N. Mohr

    Neural processing of risk

    J. Neurosci.

    (2010)
  • T.A. Hare

    Self-control in decision-making involves modulation of the vmPFC valuation system

    Science

    (2009)
  • D. Knoch

    Disruption of right prefrontal cortex by low-frequency repetitive transcranial magnetic stimulation induces risk-taking behavior

    J. Neurosci.

    (2006)
  • S. Fecteau

    Diminishing risk-taking behavior by modulating activity in the prefrontal cortex: a direct current stimulation study

    J. Neurosci.

    (2007)
  • J.M.E. Pennings et al.

    Assessing the construct validity of risk attitude

    Manage. Sci.

    (2000)
  • D.A. Jaeger

    Direct evidence on risk attitudes and migration

    Rev. Econ. Stat.

    (2009)
  • S. Brown

    Risk preference and employment contract type

    J. R. Stat. Soc. A

    (2006)
  • Y. Hanoch

    Domain specificity in experimental measures and participant recruitment

    Psychol. Sci.

    (2006)
  • E.U. Weber

    A domain-specific risk-attitude scale: measuring risk perceptions and risk behaviors

    J. Behav. Decis. Making

    (2002)
  • A. Tversky et al.

    Rational choice and the framing of decisions

    J. Bus.

    (1986)
  • E.U. Weber

    Communicating asset risk: how name recognition and the format of historic volatility information affect risk perception and investment decisions

    Risk Anal.

    (2005)
  • M.W. Morris

    Mistaken identity

    Psychol. Sci.

    (2008)
  • A. Tversky

    The causes of preference reversal

    Am. Econ. Rev.

    (1990)
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