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Perceptual events derive their significance to an animal from their meaning about the world, that is from the information they carry about their causes. The brain should thus be able to efficiently infer the causes underlying our sensory events. Here we use multisensory cue combination to study causal inference in perception. We formulate an ideal-observer(More)
In decision-making under uncertainty, economic studies emphasize the importance of risk in addition to expected reward. Studies in neuroscience focus on expected reward and learning rather than risk. We combined functional imaging with a simple gambling task to vary expected reward and risk simultaneously and in an uncorrelated manner. Drawing on financial(More)
Using a multiround version of an economic exchange (trust game), we report that reciprocity expressed by one player strongly predicts future trust expressed by their partner-a behavioral finding mirrored by neural responses in the dorsal striatum. Here, analyses within and between brains revealed two signals-one encoded by response magnitude, and the other(More)
Understanding how organisms deal with probabilistic stimulus-reward associations has been advanced by a convergence between reinforcement learning models and primate physiology, which demonstrated that the brain encodes a reward prediction error signal. However, organisms must also predict the level of risk associated with reward forecasts, monitor the(More)
Distributive justice concerns how individuals and societies distribute benefits and burdens in a just or moral manner. We combined distribution choices with functional magnetic resonance imaging to investigate the central problem of distributive justice: the trade-off between equity and efficiency. We found that the putamen responds to efficiency, whereas(More)
Interactions with other responsive agents lie at the core of all social exchange. During a social exchange with a partner, one fundamental variable that must be computed correctly is who gets credit for a shared outcome; this assignment is crucial for deciding on an optimal level of cooperation that avoids simple exploitation. We carried out an iterated,(More)
How the brain integrates signals from specific areas has been a longstanding critical question for neurobiologists. Two recent observations suggest a new approach to fMRI data analysis of this question. First, in many instances, the brain analyzes inputs by decomposing the information along several salient dimensions. For example, earlier work demonstrated(More)
It has been shown that human combination of crossmodal information is highly consistent with an optimal Bayesian model performing causal inference. These findings have shed light on the computational principles governing crossmodal integration/segregation. Intuitively, in a Bayesian framework priors represent a priori information about the environment,(More)