We examine the ability of five cognitive models to predict what publications scientists decide to read. The cognitive models are (i) the Publication Assistant, a literature recommender system that is based on a rational analysis of memory and the ACT-R cognitive architecture; (ii-iv) three simple decision heuristics, including two lexicographic ones called take-the-best and naïveLex, as well as unit-weight linear model, and (v) a more complex weighted-additive decision strategy called Franklin’s rule. In an experiment with scientists as participants, we pit these models against (vi) multiple regression. Among the cognitive models, take-the-best best predicts most scientists’ literature preferences best. Altogether, the study shows that individual differences in scientific literature selection may be accounted for by different decision-making strategies.