Risk preferences impose a hidden distortion on measures of choice impulsivity

Abstract

Measuring temporal discounting through the use of intertemporal choice tasks is now the gold standard method for quantifying human choice impulsivity (impatience) in neuroscience, psychology, behavioral economics, public health and computational psychiatry. A recent area of growing interest is individual differences in discounting levels, as these may predispose to (or protect from) mental health disorders, addictive behaviors, and other diseases. At the same time, more and more studies have been dedicated to the quantification of individual attitudes towards risk, which have been measured in many clinical and non-clinical populations using closely related techniques. Economists have pointed to interactions between measurements of time preferences and risk preferences that may distort estimations of the discount rate. However, although becoming standard practice in economics, discount rates and risk preferences are rarely measured simultaneously in the same subjects in other fields, and the magnitude of the imposed distortion is unknown in the assessment of individual differences. Here, we show that standard models of temporal discounting -such as a hyperbolic discounting model widely present in the literature which fails to account for risk attitudes in the estimation of discount rates-result in a large and systematic pattern of bias in estimated discounting parameters. This can lead to the spurious attribution of differences in impulsivity between individuals when in fact differences in risk attitudes account for observed behavioral differences. We advance a model which, when applied to standard choice tasks typically used in psychology and neuroscience, provides both a better fit to the data and successfully de-correlates risk and impulsivity parameters. This results in measures that are more accurate and thus of greater utility to the many fields interested in individual differences in impulsivity.

Computational psychiatry of impulsivity and risk: how risk and time preferences interact in health and disease

“…Interestingly, and providing further support for the unified system perspective, a conjunction analysis again revealed that the same striatal voxels represented undiscounted and discounted utility (with each side of the conjunction statistically controlling for the other; figure 2), further suggesting that this region might support the integration of undiscounted utility and delay to drive temporal discounting. Also of note, as shown behaviourally in Lopez-Guzman et al [89], the temporal discounting model that best fit the neural data was the modified hyperbolic model, providing converging evidence that the brain implements this particular algorithm [89,96].…”

“…(a) Bias in estimated discount rate from simulated intertemporal choice data at different levels of risk preference (from risk averse to risk seeking). Bias is computed as the difference between standard hyperbolic model (Mazur [59]) and the extended hyperbolic model (Lopez-Guzman et al [89]), which discounts utility rather than value. At risk aversion levels (a , 1), the standard model overestimates the discount rate and at risk seeking levels (a .…”

“…The extended hyperbolic model, which takes risk attitude into account, successfully decorrelates the two parameters. Adapted with permission from [89]. (Online version in colour.)…”

“…However, while risk and delay were assumed to be independent and non-interacting in Luhmann et al [97], this is likely not the case given strong theoretical and empirical evidence to the contrary. To account directly for this interaction, Pine et al [96] performed a traditional intertemporal choice experiment but, critically, using similar procedures to those described above in Lopez-Guzman et al [89], incorporated a subject's idiosyncratic utility function curvature into the estimation of his or her discount rate. This allowed the authors to construct, for each subject, a risk preference-adjusted discounted subjective value regressor for their fMRI analyses.…”

The appropriate response of Spanish Gitanos: short-run orientation beyond current socio-economic status

“…Our method assumes that individuals' utility functions are approximately linear over the range of stakes involved (this is common in the experimental literature on DD). However, a more concave utility function can be confounded with a higher discount rate (Andersen et al, 2008;Frederick et al, 2002;Lopez-Guzman et al, 2018). In this vein, our results could also be partially explained if Gitanos have a more concave utility function compared to the majority.…”