Linear integration and lexicographic models of choice: A cue weight learning perspective

Choosing between options characterized by multiple cues can be a daunting task. People may integrate all information at hand or just use lexicographic strategies that ignore most of it. Notably, integrative strategies require knowing exact cue weights, whereas lexicographic heuristics can operate by merely knowing the importance order of cues. Here we study how using integrative or lexicographic strategies interacts with learning about cues. In our choice-learning-estimation paradigm people first make choices, learning about cues from the experienced qualities of chosen options, and then estimate qualities of new options. We developed delta-elimination (DE), a new lexicographic strategy that generalizes previous heuristics to any type of environment, and compared it to the integrative weighted-additive (WADD) strategy. Our results show that participants learned cue weights, regardless of whether the DE strategy or the WADD strategy described their choices the best. Still, there was an interaction between the adopted strategy and the cue weight learning process: the DE users learned cue weights slower than the WADD users. This work advances the study of lexicographic choice strategies, both empirically and theoretically, and deepens our understanding of strategy selection, in particular the interaction between the strategy used and learning the structure of the environment.