Evidence for Spinozan "Unbelieving" in the Right Inferior Prefrontal Cortex.

Humans can think about possible states of the world without believing in them, an important capacity for high-level cognition. Here, we use fMRI and a novel "shell game" task to test two competing theories about the nature of belief and its neural basis. According to the Cartesian theory, information is first understood, then assessed for veracity, and ultimately encoded as either believed or not believed. According to the Spinozan theory, comprehension entails belief by default, such that understanding without believing requires an additional process of "unbelieving." Participants (n = 70) were experimentally induced to have beliefs, desires, or mere thoughts about hidden states of the shell game (e.g., believing that the dog is hidden in the upper right corner). That is, participants were induced to have specific "propositional attitudes" toward specific "propositions" in a controlled way. Consistent with the Spinozan theory, we found that thinking about a proposition without believing it is associated with increased activation of the right inferior frontal gyrus. This was true whether the hidden state was desired by the participant (because of reward) or merely thought about. These findings are consistent with a version of the Spinozan theory whereby unbelieving is an inhibitory control process. We consider potential implications of these results for the phenomena of delusional belief and wishful thinking.

Inferring action structure and causal relationships in continuous sequences of human action.

In the real world, causal variables do not come pre-identified or occur in isolation, but instead are embedded within a continuous temporal stream of events. A challenge faced by both human learners and machine learning algorithms is identifying subsequences that correspond to the appropriate variables for causal inference. A specific instance of this problem is action segmentation: dividing a sequence of observed behavior into meaningful actions, and determining which of those actions lead to effects in the world. Here we present a Bayesian analysis of how statistical and causal cues to segmentation should optimally be combined, as well as four experiments investigating human action segmentation and causal inference. We find that both people and our model are sensitive to statistical regularities and causal structure in continuous action, and are able to combine these sources of information in order to correctly infer both causal relationships and segmentation boundaries.