http://cs.northwestern.edu/~paritosh/papers/sketch-to-models/klahr-dunbar-dual-space-search-cogsci-1988.pdf

Dual space search during scientific reasoning

http://sbnec.org.br/gene/Limon%202001.pdf

On the cognitive conflict as an instructional strategy for conceptual change: a critical appraisal

A widely advocated idea in education is that people learn better when the flow of experience is under their control (i.e., learning is self-directed). However, the reasons why volitional control might result in superior acquisition and the limits to such advantages remain poorly understood. In this article, we review the issue from both a cognitive and computational perspective. On the cognitive side, self-directed learning allows individuals to focus effort on useful information they do not yet possess, can expose information that is inaccessible via passive observation, and may enhance the encoding and retention of materials. On the computational side, the development of efficient “active learning” algorithms that can select their own training data is an emerging research topic in machine learning. This review argues that recent advances in these related fields may offer a fresh theoretical perspective on how people gather information to support their own learning.

/pdf/self-directed-learning-a-cognitive-and-computational-389dc3hrgw.pdf

Self-Directed Learning: A Cognitive and Computational Perspective.

Une experience examine la strategie de test d'hypothese dans le cadre d'un paradigme inspire de Wason, la structure de la tâche etant manipulee par une variation des relations entre la regle correcte a decouvrir et les hypotheses initiales des sujets

Hypothesis testing in rule discovery: Strategy, structure, and content.

Several norms for how people should assess a question's usefulness have been proposed, notably Bayesian diagnosticity, information gain (mutual information), Kullback-Liebler distance, probability gain (error minimization), and impact (absolute change). Several probabilistic models of previous experiments on categorization, covariation assessment, medical diagnosis, and the selection task are shown to not discriminate among these norms as descriptive models of human intuitions and behavior. Computational optimization found situations in which information gain, probability gain, and impact strongly contradict Bayesian diagnosticity. In these situations, diagnosticity's claims are normatively inferior. Results of a new experiment strongly contradict the predictions of Bayesian diagnosticity. Normative theoretical concerns also argue against use of diagnosticity. It is concluded that Bayesian diagnosticity is normatively flawed and empirically unjustified.

/pdf/finding-useful-questions-on-bayesian-diagnosticity-5cuqsywrdm.pdf

Finding useful questions: on Bayesian diagnosticity, probability, impact, and information gain.

This paper reports two experiments in which subjects worked to solve a more difficult version of Wason's 2–4–6 task: instead of the usual “numbers must ascend in order of magnitude” rule, a more general rule, “the three numbers must be different”, was used. The first experiment established that instructing subjects to disconfirm on the “three different numbers” did not significantly improve their performance, as compared with confirmatory and control groups. Disconfirmatory subjects did try to propose more triples at variance with their hypotheses but were unable to obtain the necessary disconfirmatory information. To help subjects represent the task in a way that facilitated disconfirmation, the second experiment utilized a procedure in which subjects were told that they were looking for two rules, Dax and Med–the Dax rule corresponding to “three different numbers” and the Med rule to its complement, i.e. two or more numbers the same. Of subjects in the Dax–Med condition, 88% solve the rule, as opposed t...

Ann Stafford

Papers

Disconfirmation and dual hypotheses on a more difficult version of wason's 2–4–6 task