Aim: We aimed to explore the organization of the calendar knowledge base underlying date calculation by assessing the ability of savant calendar calculators to free recall a series of date lists. Method: Four experiments are reported that assessed recall of structural and non-structural features of the calendar in eight savant calendar calculators…

In order to be adaptive, cognition requires knowledge about the statistical structure of the environment. We show that decision performance and the selection between cue-based and exemplar-based inference mechanisms can depend critically on how this knowledge is acquired. Two types of learning tasks are distinguished: "learning by comparison", by…

We present a new modeling framework for recognition memory and repetition priming based on signal detection theory. We use this framework to specify and test the predictions of 4 models: (a) a single-system (SS) model, in which one continuous memory signal drives recognition and priming; (b) a multiple-systems-1 (MS1) model, in which completely…

Inducing causal relationships from observations is a classic problem in scientific inference, statistics, and machine learning. It is also a central part of human learning, and a task that people perform remarkably well given its notorious difficulties. People can learn causal structure in various settings, from diverse forms of data: observations…

Meta-analysis is a frequent tool among education and behavioral researchers to combine results from multiple experiments to arrive at a clear understanding of some effect of interest. One of the traditional assumptions in a meta-analysis is the independence of the effect sizes from the studies under consideration. This article presents a…