We have developed a process model that learns in multiple ways while finding faults in a simple control panel device. The model predicts human participants' learning through its own learning. The model's performance was systematically compared to human learning data, including the time course and specific sequence of learned behaviors. These comparisons show that the model accounts very well for measures such as problem‐solving strategy, the relative difficulty of faults, and average fault‐finding time. More important, because the model learns and (...) transfers its learning across problems, it also accounts for the faster problem‐solving times due to learning when examined across participants, across faults, and across the series of 20 trials on an individual participant basis. The model shows how learning while problem solving can lead to more recognition‐based performance, and helps explain how the shape of the learning curve can arise through learning and be modified by differential transfer. Overall, the quality of the correspondence appears to have arisen from procedural, declarative, and episodic learning all taking place within individual problem‐solving episodes. (shrink)

Computational models of memory presented in this issue reflect varied empirical data and levels of representation. From mathematical models to neural and cognitive architectures, all aim to converge on a unified theory of the mind.

1. Bd. Lebensschöpfung und Vernunftfrevel.--2. Bd. Schmarotzerzüchtung und Erbopferung.--3. Bd. Nietzsche, der gerechtfertigye Seher. b.

A common finding is that information order influences belief revision (e.g., Hogarth & Einhorn, 1992). We tested personal experience as a possible mitigator. In three experiments participants experienced the probabilistic relationship between pieces of information and object category through a series of trials where they assigned objects (planes) into one of two possible categories (hostile or commercial), given two sequentially presented pieces of probabilistic information (route and ID), and then they had to indicate their belief about the object category before (...) feedback. The results generally confirm the predictions from the Hogarth and Einhorn model. Participants showed a recency effect in their belief revision. Extending previous model evaluations the results indicate that the model predictions also hold for classification decisions, and for pieces of information that vary in their diagnostic values. Personal experience does not appear to prevent order effects in classification decisions based on sequentially presented pieces of information and in belief revision. (shrink)