Causal learning requires integrating constraints provided by domain-specific theories with domaingeneral statistical learning. In order to investigate the interaction between these factors, the authors presented preschoolers with stories pitting their existing theories against statistical evidence. Each child heard 2 stories in which 2 candidate causes co-occurred with an effect. Evidence was presented in the

Functional magnetic resonance imaging (fMRI) was used to examine the neural correlates of perceptual causality. Participants were imaged while viewing alternating blocks of causal events in which a ball collides with, and causes movement of another ball, versus non-causal events in which a spatial or a temporal gap precedes the movement of a second ball. There were significantly higher levels of relative activation in the right middle frontal gyrus and the right inferior parietal lobule for causal relative to non-causal events. Furthermore, when the differential effects of spatial and temporal incontiguities were subtracted from the contiguous stimuli, we observed both common (right prefrontal) and unique (right parietal and right temporal) regions of activation as a function of spatial and temporal processing of contiguity, respectively. Taken together, these data provide a means to help determine how the visual system extracts causality from dynamic visual information in the environment using spatial and temporal cues. D 2004 Elsevier B.V. All rights reserved.

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People use information about the covariation between a putative cause and an outcome to determine if a causal relationship obtains. When there are two candidate causes and one is more strongly related to the effect than the other, the influence of the second is underestimated. This phenomenon is called causal discounting. In two studies, we adapted paradigms for studying causal learning to apply signal detection analysis to this phenomenon. We investigated whether the presence of a stronger alternative makes the task more difficult (indexed by differences in d’), or if people change the standard by which they assess causality (measured by β). Our results indicate the effect is due to bias. R579B Causal Discounting 3 Humans can use knowledge of covariation to predict events and to infer their underlying causes (Cheng, 1997). Although research has demonstrated a number of systematic phenomena in covariation and causal judgment, it is unclear whether these effects occur during the learning or decision process. Here we use signal detection theory (SDT) to tease apart these alternatives for one phenomenon: causal discounting. Discounting is a cue interaction effect, in which someone judges a moderately effective

* corresponding author Normative theories of individual choice in economics typically assume that interacting agents should each act individualistically: i.e., they should maximize their own utility function. Specifically, game theory proposes that interaction should be governed by Nash equilibria. Computationally limited agents (whether artificial, animal or human) may not, however, have the capacity to carry out the sophisticated reasoning to converge directly on Nash equilibria. Nonetheless it is often assumed that Nash equilibria will be obtained, in any case, if agents embody simple learning algorithms like reinforcement learning. If so, then learners should converge on Nash equilibria, after sufficient practice in playing a game---and hence, for example, individualistic agents should end up playing D (defect) in one-shot Prisoners ’ Dilemmas (PD). In an experiment and in a multi-agent simulation, we show, however, that this is not always the case---under certain circumstances, reinforcement learners can converge on co-operative behaviour in PD. That is, even though each agent would receive higher pay-off from switching to D, agents obtain more reinforcement, on average, from playing C, and hence C is more strongly reinforced. This effect arises from a well-known statistical paradox, Simpson’s paradox. We speculate that this effect may be relevant to some aspects of real-world human co-operative behaviour. 2

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In four experiments, the predictions made by causal-model theory and the Rescorla-Wagner model are tested by using a cue-interaction paradigm that measures the relative response to a given event based on the influence or salience of an alternative event. Experiments 1 and 2 uncorrelate two variables that have typically been confounded in the literature (causal order and the number of cues and outcomes) and demonstrate that overall contingency judgments are influenced by the causal structure of the events. Experiment 3 shows that trial-by-trial prediction responses, a second measure of causal assessment, are not influenced by the causal structure of the described events. Experiment 4 revealed that participants became less sensitive to the influence of the causal structure in both their ratings and their predictions as trials progressed. Thus, two experiments provide evidence for high-level (causal reasoning) processes, and two experiments provide evidence for low-level (associative) processes. We argue that both factors influence causal assessment depending on what is being asked about the events, and participants ’ experience with those events. In the past decade, the debate between causal-model and associative learning theorists has centered on whether or not