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 model that infers whether two sensory cues originate from the same location and that also estimates their location(s). This model accurately predicts the nonlinear integration of cues by human subjects in two auditory-visual localization tasks. The results show that indeed humans can efficiently infer the causal structure as well as the location of causes. By combining insights from the study of causal inference with the ideal-observer approach to sensory cue combination, we show that the capacity to infer causal structure is not limited to conscious, high-level cognition; it is also performed continually and effortlessly in perception.

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 by response timing. Response magnitude correlated with the “intention to trust” on the next play of the game, and the peak of these “intention to trust” responses shifted its time of occurrence by 14 seconds as player reputations developed. This temporal transfer resembles a similar shift of reward prediction errors common to reinforcement learning models, but in the context of a social exchange. These data extend previous model-based functional magnetic resonance imaging studies into the social domain and broaden our view of the spectrum of functions implemented by the dorsal striatum.

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 errors in those risk predictions, and update these in light of new information. Risk prediction serves a dual purpose: (1) to guide choice in risk-sensitive organisms and (2) to modulate learning of uncertain rewards. To date, it is not known whether or how the brain accomplishes risk prediction. Using functional imaging during a simple gambling task in which we constantly changed risk, we show that an early-onset activation in the human insula correlates significantly with risk prediction error and that its time course is consistent with a role in rapid updating. Additionally, we show that activation previously associated with general uncertainty emerges with a delay consistent with a role in risk prediction. The activations correlating with risk prediction and risk prediction errors are the analogy for risk of activations correlating with reward prediction and reward prediction errors for reward expectation. As such, our findings indicate that our understanding of the neural basis of reward anticipation under uncertainty needs to be expanded to include risk prediction.

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 decision theory, we modeled expected reward as mathematical expectation of reward, and risk as reward variance. Activations in dopaminoceptive structures correlated with both mathematical parameters. These activations differentiated spatially and temporally. Temporally, the activation related to expected reward was immediate, while the activation related to risk was delayed. Analyses confirmed that our paradigm minimized confounds from learning, motivation, and salience. These results suggest that the primary task of the dopaminergic system is to convey signals of upcoming stochastic rewards, such as expected reward and risk, beyond its role in learning, motivation, and salience.

How do minds emerge from developing brains? According to “neural constructivism,” the representational features of cortex are built from the dynamic interaction between neural growth mechanisms and environmentally derived neural activity. Contrary to popular selectionist models that emphasize regressive mechanisms, the neurobiological evidence suggests that this growth is a progressive increase in the representational properties of cortex. The interaction between the environment and neural growth results in a flexible type of learning: “constructive learning” minimizes the need for prespecification in accordance with recent neurobiological evidence that the developing cerebral cortex is largely free of domain-specific structure. Instead, the representational properties of cortex are built by the nature of the problem domain confronting it. This uniquely powerful and general learning strategy undermines the central assumption of classical learnability theory, that the learning properties of a system can be deduced from a fixed computational architecture. Neural constructivism suggests that the evolutionary emergence of neocortex in mammals is a progression toward more flexible representational structures, in contrast to the popular view of cortical evolution as an increase in innate, specialized circuits. Human cortical postnatal development is also more extensive and protracted than generally supposed, suggesting that cortex has evolved so as to maximize the capacity of environmental structure to shape its structure and function through constructive learning.

Movies S1 and S2 Correction (29 May 2008):The original SOM text incorrectly said that "each child gets $5." The correct text now states that "each set of 3 children was endowed by the experimenters with $5, the monetary equivalent of 24 meals per child."

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, two-person economic exchange and made simultaneous hemodynamic measurements from each player's brain. These joint measurements revealed agent-specific responses in the social domain ("me" and "not me") arranged in a systematic spatial pattern along the cingulate cortex. This systematic response pattern did not depend on metrical aspects of the exchange, and it disappeared completely in the absence of a responding partner.

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, i.e., information available prior to encountering the given stimuli, and are thus not dependent on the current stimuli. While this interpretation is considered as a defining characteristic of Bayesian computation by many, the Bayes rule per se does not require that priors remain constant despite significant changes in the stimulus, and therefore, the demonstration of Bayes-optimality of a task does not imply the invariance of priors to varying likelihoods. This issue has not been addressed before, but here we empirically investigated the independence of the priors from the likelihoods by strongly manipulating the presumed likelihoods (by using two drastically different sets of stimuli) and examining whether the estimated priors change or remain the same. The results suggest that the estimated prior probabilities are indeed independent of the immediate input and hence, likelihood.