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Outside the cognitive psychologist's laboratory, problem solving is an activity that takes place in a rich web of interactions involving people and artefacts. This interactivity is constituted by fine-grained actions-perception cycles, and it allows a reasoner's comprehension of the problem to emerge from a coalition of internal and external resources.Taking an ecological approach to problem solving, this article introduces a qualitative method, Cognitive Event Analysis, for studying the fine-grained interactivity between a problem solving agent and his/her environment. To demonstrate the potential of this method, it is used to study a single subject solving the so-called 17 Animals problem using a material model. The fine-grained procedure allows tracking the solution to a serendipity that was brought about because of the participant's aesthetic considerations and a change in her perceptual figure-ground configuration. While a qualitative single-case method cannot prove specific models of problem solving, it questions prevalent mentalist models, and it generates new hypotheses on insight problem solving, because it allows the researcher to attend to outliers and to variability on a fast and fine-grained between-measurement timescale.

In two causal induction experiments subjects rated the importance of pairs of candidate causes in the production of a target effect; one candidate was present on every trial (constant cause), whereas the other was present on only some trials (variable cause). The design of both experiments consisted of a factorial combination of two values of the variable cause's covariation with the effect and three levels of the base rate of the effect. Judgements of the constant cause were inversely proportional to the level of covariation of the variable cause but were proportional to the base rate of the effect. The judgements were consistent with the predictions derived from the Rescorla-Wagner (1972) model of associative learning and with the predictions of the causal power theory of the probabilistic contrast model (Cheng, 1997) or “power PC theory”. However, judgements of the importance of the variable candidate cause were proportional to the base rate of the effect, a phenomenon that is in some cases anticipated by the power PC theory. An alternative associative model, Pearce's (1987) similarity-based generalization model, predicts the influence of the base rate of the effect on the estimates of both the constant and the variable cause.

The acquisition of a negative evaluation of a fictitious minority social group in spite of the absence of any objective correlation between group membership and negative behaviours was described by Hamilton and Gifford (1976) as an instance of an illusory correlation. We studied the acquisition and attenuation through time of this correlation learning effect. In two experiments we asked for participants' judgements of two fictitious groups using an online version of a group membership belief paradigm. We tested how judgements of the two groups changed as a function of the amount of training they received. Results suggest that the perception of the illusory correlation effect is initially absent, emerges with intermediate amounts of absolute experience, but diminishes and is eliminated with increased experience. This illusory correlation effect can be considered to reflect incomplete learning rather than a bias due to information loss in judgements or distinctiveness.