In this article, 41 models of covariation detection from 2 × 2 contingency tables were evaluated against past data in the literature and against data from new experiments. A new model was also included based on a limiting case of the normative phi‐coefficient under an extreme rarity assumption, which has been shown to be an important factor in covariation detection (McKenzie & Mikkelsen, 2007) and data selection (Hattori, 2002; Oaksford & Chater, 1994, 2003). The results were supportive of the new (...) model. To investigate its explanatory adequacy, a rational analysis using two computer simulations was conducted. These simulations revealed the environmental conditions and the memory restrictions under which the new model best approximates the normative model of covariation detection in these tasks. They thus demonstrated the adaptive rationality of the new model. (shrink)

Generalising what is learned about one stimulus to other but perceptually related stimuli is a basic behavioural phenomenon. We evaluated whether a rule learning mechanism may serve to explain such generalisation. To this end, we assessed whether inference rules communicated through verbal instructions affect generalisation. Expectancy ratings, but not valence ratings, proved sensitive to this manipulation. In addition to revealing a role for inference rules in generalisation, our study has clinical implications as well. More specifically, we argue that targeting inference (...) rules might prove to be an effective strategy to affect the excessive generalisation that is often observed in psychopathology. (shrink)