Is human learning rational

TL;DR: Cheng and Novick as mentioned in this paper proposed a causal power theory of the probabilistic contrast model, which is based on covariation and causal power, and showed that causal relations are neither observable nor deducible.

TL;DR: This paper explored the extent to which measures of cognitive ability and thinking dispositions can predict discrepancies from normative responding on a variety of tasks from the heuristics and biases literature including the selection task, belief bias in syllogistic reasoning, argument evaluation, base-rate use, covariation detection, hypothesis testing, outcome bias, if-only thinking, knowledge calibration, hindsight bias, and the false consensus paradigm.

TL;DR: Computational models of how people infer causal structure from data and how they plan intervention experiments are developed, based on the representational framework of causal graphical models and the inferential principles of optimal Bayesian decision-making and maximizing expected information gain.

Abstract: This article described three heuristics that are employed in making judgements under uncertainty: (i) representativeness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; (ii) availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development; and (iii) adjustment from an anchor, which is usually employed in numerical prediction when a relevant value is available. These heuristics are highly economical and usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and of the biases to which they lead could improve judgements and decisions in situations of uncertainty.

TL;DR: In this article, a new treatment of causality that accords with the pervasively statistical character of contemporary science is proposed, which is based on three fundamental conceptions of scientific explanation: epistemic, modal, and ontic.

TL;DR: In this article, the learned helplessness theory of depression was used to predict the degree of contingency between responses and outcomes relative to the objective degree of contingencies, and the predicted subjective judgments of contingency were surprisingly accurate in all four experiments.