Humans display more conditioned fear when the conditioned stimulus in a fear conditioning paradigm is a picture of an individual from another race than when it is a picture of an individual from their own race (Olsson, Ebert, Banaji, & Phelps, 2005). These results have been interpreted in terms of a genetic “preparedness” to learn to fear individuals from different social groups (Ohman, 2005; Olsson et al., 2005). However, the associability of conditioned stimuli is strongly influenced by prior exposure to (...) those or similar stimuli. Using the Kalman filter, a normative statistical model, this article shows that superior fear conditioning to individuals from other groups is precisely what one would expect if participants perform optimal, Bayesian inference that takes their prior exposures to the different groups into account. There is therefore no need to postulate a genetic preparedness to learn to fear individuals from other races or social groups. (shrink)

Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause-effect links, or do learners also acquire knowledge about abstract causal constraints? Recent empirical studies have revealed that experience with one set of causal cues can dramatically alter subsequent learning and performance with entirely different cues, suggesting that (...) learning involves abstract transfer, and such transfer effects involve sequential presentation of distinct sets of causal cues. It has been demonstrated that pre-training can modulate classic causal learning phenomena such as forward and backward blocking. To account for these effects, we propose a Bayesian theory of sequential causal learning. The theory assumes that humans are able to consider and use several alternative causal generative models, each instantiating a different causal integration rule. Model selection is used to decide which integration rule to use in a given learning environment in order to infer causal knowledge from sequential data. Detailed computer simulations demonstrate that humans rely on the abstract characteristics of outcome variables to select a causal integration rule, which in turn alters causal learning in a variety of blocking and overshadowing paradigms. When the nature of the outcome variable is ambiguous, humans select the model that yields the best fit with the recent environment, and then apply it to subsequent learning tasks. Based on sequential patterns of cue-outcome co-occurrence, the theory can account for a range of phenomena in sequential causal learning, including various blocking effects, primacy effects in some experimental conditions, and apparently abstract transfer of causal knowledge. (shrink)

Inspired by, and in close relation with, the contributions of this special issue, Kuperberg elegantly links event comprehension, production, and learning. She proposes an overarching hierarchical generative framework of processing events enabling us to make sense of the world around us and to interact with it in a competent manner.