A lack of appetite for information and computation. Simple heuristics in food choice

Abstract

The predominant, but largely untested, assumption in research on food choice is that people obey the classic commandments of rational behavior: they carefully look up every piece of relevant information, weight each piece according to subjective importance, and then combine them into a judgment or choice. In real world situations, however, the available time, motivation, and computational resources may simply not suffice to keep these commandments. Indeed, there is a large body of research suggesting that human choice is often better accommodated by heuristics-simple rules that enable decision making on the basis of a few, but important, pieces of information. We investigated the prevalence of such heuristics in a computerized experiment that engaged participants in a series of choices between two lunch dishes. Employing MouselabWeb, a process-tracing technique, we found that simple heuristics described an overwhelmingly large proportion of choices, whereas strategies traditionally deemed rational were barely apparent in our data. Replicating previous findings, we also observed that visual stimulus segments received a much larger proportion of attention than any nutritional values did. Our results suggest that, consistent with human behavior in other domains, people make their food choices on the basis of simple and informationally frugal heuristics.

A Brunswik lens model of consumer health judgments of packaged foods

“…The underlying assumption is that adequate nutrition labeling empowers the consumer to judge the healthfulness of food products and thereby choose a healthier product. However, given the information complexity of nutrition labels, even the simplest strategy for processing the label requires several cognitive operations (for an overview, see Schulte‐Mecklenbeck et al ., ). First of all, the label information must attract sufficient visual attention to enter working memory where it must lead to an adjustment in the representation of the food nutrients (Graham et al ., ).…”

“…Second, given an adequate representation of the nutrient values, the consumer must now compare the product under consideration with a competing product or with a threshold value for the important nutrients. Finally, the consumer must choose the product with the most favorable nutrition values or in the case of a single product accept or reject it based on whether it is above the threshold value (Schulte‐Mecklenbeck et al ., ). If the healthfulness judgment is to adequately reflect the intention behind the nutrition label, one could furthermore add that the judgment must be compensatory, that is, the consumer must take into account more than one nutrition value and if necessary trade off between these values.…”

Testing process predictions of models of risky choice: a quantitative model comparison approach

“…Indeed, there is considerable evidence for people's use of heuristics in inferences under uncertainty (e.g., Pachur et al, 2008; García-Retamero and Dhami, 2009; Bröder, 2011; Gigerenzer et al, 2011; Pachur and Marinello, 2013), in decisions under certainty (e.g., Ford et al, 1989; Schulte-Mecklenbeck et al, in press), as well as in decisions under risk (e.g., Slovic and Lichtenstein, 1968; Payne et al, 1988; Cokely and Kelley, 2009; Venkatraman et al, 2009; Brandstätter and Gussmack, 2013; Pachur and Galesic, 2013; Su et al, 2013). This evidence is consistent with the argument that people find trade-offs—the very core of expectation models—difficult to execute, both cognitively and emotionally (Hogarth, 1987; Luce et al, 1999).…”

Public perceptions of how to reduce carbon footprints of consumer food choices

“…about for example health, management, and finance (Gigerenzer et al 1999, Gigerenzer and Gaissmaier 2011, Artinger et al 2015, Hafenbrädl et al 2016. Consumers already use simple rules when choosing what they would like to have for lunch (Schulte-Mecklenbeck et al 2013). To effectively communicate simple rules that consumers can use for reducing food-related GHG emissions, we first need a better understanding of how they (mis)perceive such rules (Bruine de Bruin and Bostrom 2013).…”