<p>A persistent problem in the philosophy of induction concerns the variety of analogies among experiences that might guide one’s learning. Given that any observational record will exhibit a multiplicity of regularities, which ones should we attend to in forming empirical beliefs and expectations? Call this the <i>projectibility problem</i>. Nelson&#xa0;Goodman (<i>Fact, fiction, and forecast.</i> 1983) dramatized the problem with his “new riddle of induction,” and David Lewis (<i>Convention: A philosophical study.</i> 1969) recognized the challenges it raises for the possibility of establishing conventions by precedent. Learners in nature face a version of the projectibility problem inasmuch as they must learn which regularities in their environments are useful guides for action and prediction in order to survive. The present paper considers one method by which this problem might be solved: trial-and-error learning. The strategy is to begin with an experiment from comparative psychology in which subjects face a version of the projectibility problem, then to develop a concrete model of a learner that can solve that problem. In the model, the dispositions of three separate modules, or <i>subagents</i>, responsible for the learner’s attentional and behavioral responses coevolve under a simple reinforcement learning dynamics. On simulation, the model reliably learns to attend to the practically relevant regularities in its environment.</p>