Naïve Point Estimation

The capacity of short-term memory is a key constraint when people make online judgments requiringthem to rely on samples retrieved from memory (e.g., Dougherty & Hunter, 2003). In this article, theauthors compare 2 accounts of how people use knowledge of statistical distributions to make pointestimates: either by retrieving precomputed large-sample representations or by retrieving small samplesof similar observations post hoc at the time of judgment, as constrained by short-term memory capacity(the naı¨ve sampling model: Juslin, Winman, & Hansson, 2007). Results from four experiments supportthe predictions by the naı¨ve sampling model, including that participants sometimes guess values thatthey, when probed, demonstrably know have the lowest probability of occurring. Experiment 1 alsodemonstrated the operations of an unpredicted recognition-based inference. Computational modeling alsoincorporating this process demonstrated that the data from all 4 experiments were better predicted byassuming a post hoc sampling process constrained by short-term memory capacity than by assumingabstraction of large-sample representations of the distribution.