Do people exploit risk–reward structures to simplify information processing in risky choice?

Abstract

The high rewards people desire are often unlikely. Here, we investigated whether decision-makers exploit such ecological correlations between risks and rewards to simplify their information processing. In a learning phase, participants were exposed to options in which risks and rewards were negatively correlated, positively correlated, or uncorrelated. In a subsequent risky choice task, where the emphasis was on making either a 'fast' or the 'best' possible choice, participants' eye movements were tracked. The changes in the number, distribution, and direction of eye fixations in 'fast' trials did not differ between the risk-reward conditions. In 'best' trials, however, participants in the negatively correlated condition lowered their evidence threshold, responded faster, and deviated from expected value maximization more than in the other risk-reward conditions. The results underscore how conclusions about people's cognitive processing in risky choice can depend on risk-reward structures, an often neglected environmental property. Keywords Risk-reward • Decisions under risk • Ecological rationality • Eye tracking • Drift-diffusion model JEL Classification D91 • D81 Monetary lotteries have been playing a lead role in studies of how people should and do make decisions under risk (e.g.

The ecology of competition: A theory of risk–reward environments in adaptive decision making.

“…There is growing evidence that people use the risk–reward heuristic to infer missing probabilities and missing payoffs, which in turn impacts the choices they make (Hoffart, Rieskamp, & Dutilh, 2018; Leuker, Pachur, Hertwig, & Pleskac, 2018; Pleskac & Hertwig, 2014; Skylark & Prabhu-Naik, 2018). The risk–reward structure also impacts other aspects of decision making, including how people evaluate options with known probabilities (Leuker, Pachur, Hertwig, & Pleskac, 2019a, 2019b). Enlisting an environmental structure like the risk–reward structure—be it via a heuristic or within a larger framework, such as Bayesian inference (e.g., Chater, Oaksford, Hahn, & Heit, 2010; Gershman, Horvitz, & Tenenbaum, 2015; Griffiths, Chater, Kemp, Perfors, & Tenenbaum, 2010) or reinforcement learning (e.g., Daw, Niv, & Dayan, 2005; Niv, 2009)—is appealing because it requires relatively little time and effort; decision makers can instead invest more of their limited resources in seeking out other information or achieving other goals.…”

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“…These expectations can stem from regularities in the environment, such as larger rewards are less likely to be received than smaller rewards (Pleskac & Hertwig, 2014). There is evidence that people make use of this relation as participants take longer to make choices and show higher levels of attention (measured by eye-tracking) when lotteries are atypical and do not follow this risk-reward regularity in DfD (Leuker et al, 2019). Similarly, probability judgments as well as search patterns in DfE have shown that people usually assume a negative relation between probability and outcome (Hoffart et al, 2019).…”

Choice-Process Data in Experimental Economics

“…Several articles in this special issue discuss SSMs in detail, with connections to random utility theory (Smith et al 2019), risky decision-making (Leuker et al 2019), and distinguishing between effort and ability (Alekseev 2019). Another article (Gawryluk and Krawczyk 2019) investigates changes of mind over time.…”

“…In this special issue, Smith et al (2019) provide a simple method for estimating this effect of gaze on choice, using standard logistic regression. Another article examines similar SSM gaze effects in risky choice (Leuker et al 2019). A third article also examines dwell-time effects in risky choice, albeit within a different modeling framework (Harrison and Swarthout 2019).…”