Many models of learning describe both the end product of learning (e.g., causal judgments) and the cognitive mechanisms that unfold on a trial-by-trial basis. However, the methods employed in the literature typically provide only indirect evidence about the unfolding cognitive processes. Here, we utilized a simultaneous secondary task to measure cognitive processing during a straightforward causal-learning task. The results from three experiments demonstrated that covariation information is not subject to uniform cognitive processing. Instead, we observed systematic variation in the processing dedicated to individual pieces of covariation information. In particular, observations that are inconsistent with previously presented covariation information appear to elicit greater cognitive processing than do observations that are consistent with previously presented covariation information. In addition, the degree of cognitive processing appears to be driven by learning per se, rather than by nonlearning processes such as memory and attention. Overall, these findings suggest that monitoring learning processes at a finer level may provide useful psychological insights into the nature of learning.
Keywords Causal learning . Human learningWhen forming beliefs about causal relationships, people frequently rely on information about whether the potential cause tends to occur with its potential effect. For example, imagine a person becoming sick each time she eats a certain kind of nut, but not after eating other sorts of food. Eventually, she will likely believe that the nuts cause her sickness and avoid eating them in the future. Prevailing theories of causal learning (Busemeyer, 1991;Cheng, 1997;Einhorn & Hogarth, 1986;Jenkins & Ward, 1965;Rescorla & Wagner, 1972;Schustack & Sternberg, 1981;White, 2002) have formalized such evidence using a covariation matrix such as that shown in Fig. 1. Assuming that events can be either present or absent, there are four types of observations, each represented by a different cell in the covariation matrix. For example, cell A of the matrix represents the frequency with which the cause and the effect are both present. Evidence from cells A and D (positive covariation) tends to suggest that the cause generates the outcome, whereas evidence from cells B and C (negative covariation) tends to suggest that the cause prevents the outcome (see Luhmann & Ahn, 2011, for systematic exceptions).A variety of learning theories, both associative and nonassociative, describe the processes that operate to transform sequences of covariation information into causal judgments (Danks, Griffiths, & Tenenbaum, 2003;Hogarth & Einhorn, 1992;Lu, Rojas, Beckers, & Yuille, 2008;Luhmann & Ahn, 2007;Mackintosh, 1975;Pearce, 1994;Pearce & Hall, 1980;Rescorla & Wagner, 1972;Wagner, 1981). These models specify what causal judgments should be made on the basis of any given set of covariation information, and provide details of the constituent processes that transform individual pieces of covariation information...