Order matters: How covert value updating during sequential option sampling shapes economic preference

Standard neuroeconomic decision theory assumes that choice is based on a value comparison process, independent from how information about alternative options is collected. Here, we investigate the opposite intuition that preferences are dynamically shaped as options are sampled, through iterative covert pairwise comparisons. Our model builds on two lines of research, one suggesting that a natural frame of comparison for the brain is between default and alternative options, the other suggesting that comparisons spread preferences between options. We therefore assumed that during sequential option sampling, people would 1) covertly compare every new alternative to the current best and 2) update their values such that the winning (losing) option receives a positive (negative) bonus. We confronted this “covert pairwise comparison” model to models derived from standard decision theory and from known memory effects. Our model provided the best account of human choice behavior in a novel task where participants (n = 92 in total) had to browse through a sequence of items (food, music or movie) of variable length and ultimately select their favorite option. Consistently, the order of option presentation, which was manipulated by design, had a significant influence on the eventual choice: the best option was more likely to be chosen when it came earlier in the sequence, because it won more covert comparisons (hence a greater total bonus). Our study provides a mechanistic understanding of how the option sampling process shapes economic preference, which should be integrated into decision theory.Author summary: According to standard views in neuroeconomics, choice is a two-step process, with first the valuation of alternative options and then the comparison of subjective value estimates. Our working hypothesis is, on the contrary, that the comparison process begins during the sequential sampling of alternative options. To capture this idea, we developed a computational model, in which every new alternative is compared with the current best, so as to better contrast their values. This model provided the best account of choice behavior exhibited by participants (n = 92 in total) performing three variants of a novel multi-alternative decision task. Thus, our findings unravel a covert pairwise comparison process, occurring while participants collect information about alternative options, before they are requested to make their choice. They also provide explanations about when this covert process is implemented (when resampling is too costly), why it is implemented (to better discriminate the best options) and how it can bias decisions (because it favors first-encountered valuable options).