Using Recognition in Multi-Attribute Decision Environments

Abstract

An experiment examined the effect of ‘pure’ recognition — in the absence of concomitant evaluation — on inferences. In the first stage of the experiment, participants indicated whether they recognized a number of Italian and US cities. In the second stage, they decided which of two cities had the larger population. Crucially, names of the cities were not available in the second stage, but participants could find out whether they had recognized them (yes/no) in the first stage of the experiment (i.e., pure recognition). Additional predictive cues (e.g., presence/absence of a university) were also available. Participants used the recognition cue about 50% of the time, rarely examined it first, and used it differently as a function of whether recognition information was binary or continuous. Furthermore, participants used the recognition cue more often if they recognized more items, irrespective of its predictive validity. Implications for theoretical frameworks that view inference as driven by discrete heuristics or processes of evidence–accumulation are briefly discussed.


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