Reliance on Small Samples, the Wavy Recency Effect, and Similarity-Based Learning

Many behavioral phenomena, including underweighting of rare events and probability matching, can be the product of a tendency to rely on small samples of experiences. Why would small samples be used, and which experiences are likely to be included in these samples? Previous studies suggest that a cognitively efficient reliance on the most recent experiences can be very effective. We explore a very different and more cognitively demanding process explaining the tendency to rely on small samples: exploitation of environmental regularities. The first part of our study shows that across wide classes of dynamic binary choice environments, focusing only on experiences that followed the same sequence of outcomes preceding the current task is more effective than focusing on the most recent experiences. The second part of our study examines the psychological significance of these sequence-based rules. It shows that these tractable rules reproduce well-known indications of sensitivity to sequences and predict a nontrivial wavy recency effect of rare events. Analysis of published data supports this wavy recency prediction, but suggests an even wavier effect than these sequence-based rules predict. This pattern, and the main behavioral phenomena documented in basic decisions from experience and probability learning tasks, can be captured with a similarity-based model assuming that people follow sequences of outcomes most of the time but sometimes respond to trends. We conclude with theoretical notes on similarity-based learning.