The article presents a Bayesian model of causal learning that incorporates generic priors-systematic assumptions about abstract properties of a system of cause-effect relations. The proposed generic priors for causal learning favor sparse and strong (SS) causes-causes that are few in number and high in their individual powers to produce or prevent effects. The SS power model couples these generic priors with a causal generating function based on the assumption that unobservable causal influences on an effect operate independently (P. W. Cheng, 1997). The authors tested this and other Bayesian models, as well as leading nonnormative models, by fitting multiple data sets in which several parameters were varied parametrically across multiple types of judgments. The SS power model accounted for data concerning judgments of both causal strength and causal structure (whether a causal link exists). The model explains why human judgments of causal structure (relative to a Bayesian model lacking these generic priors) are influenced more by causal power and the base rate of the effect and less by sample size. Broader implications of the Bayesian framework for human learning are discussed.

No abstract

It is widely held that the interaction between instrumental and Pavlovian conditioning induces powerful motivational biases. PavlovianInstrumental Transfer (PIT) is one of the key paradigms demonstrating this effect, which can further be decomposed into a general and specific component. Although these two forms of PIT have been studied at the level of amygdalar subregions in rodents, it is still unknown whether they involve different areas of the human amygdala. Using a high-resolution fMRI (hr-fMRI) protocol optimized for the amygdala in combination with a novel free operant task designed to elicit effects of both general and specific PIT, we demonstrate that a region of ventral amygdala within the boundaries of the basolateral complex and the ventrolateral putamen are involved in specific PIT, while a region of dorsal amygdala within the boundaries of the centromedial complex is involved in general PIT. These results add to a burgeoning literature indicating different functional contributions for these different amygdalar subregions in reward-processing and motivation.

It has long been recognized that the striatum is composed of distinct functional sub-units that are part of multiple cortico-striatal-thalamic circuits. Contemporary research has focused on the contribution of striatal sub-regions to three main phenomena: learning of associations between stimuli, actions and rewards; selection between competing response alternatives; and motivational modulation of motor behavior. Recent proposals have argued for a functional division of the striatum along these lines, attributing, for example, learning to one region and performance to another. Here, we consider empirical data from human and animal studies, as well as theoretical notions from both the psychological and computational literatures, and conclude that striatal sub-regions instead differ most clearly in terms of the associations being encoded in each region.

Contingency theories of goal-directed action propose that experienced disjunctions between an action and its specific consequences, as well as conjunctions between these events, contribute to encoding the action-outcome association. Although considerable behavioral research in rats and humans has provided evidence for this proposal, relatively little is known about the neural processes that contribute to the two components of the contingency calculation. Specifically, while recent findings suggest that the influence of action-outcome conjunctions on goal-directed learning is mediated by a circuit involving ventromedial prefrontal, medial orbitofrontal cortex, and dorsomedial striatum, the neural processes that mediate the influence of experienced disjunctions between these events are unknown. Here we show differential responses to probabilities of conjunctive and disjunctive reward deliveries in the ventromedial prefrontal cortex, the dorsomedial striatum, and the inferior frontal gyrus. Importantly, activity in the inferior parietal lobule and the left middle frontal gyrus varied with a formal integration of the two reward probabilities, ⌬P, as did response rates and explicit judgments of the causal efficacy of the action.