Environmental change can lead decision makers to shift rapidly among different behavioral regimes. These behavioral shifts can be accompanied by rapid changes in the firing pattern of neural networks. However, it is unknown what the populations of neurons that participate in such "network reset" phenomena are representing. Here, we investigated the following: (1) whether and where rapid changes in multivariate activity patterns are observable with fMRI during periods of rapid behavioral change and (2) what types of representations give rise to these phenomena. We did so by examining fluctuations in multivoxel patterns of BOLD activity from male and female human subjects making sequential inferences about the state of a partially observable and discontinuously changing variable. We found that, within the context of this sequential inference task, the multivariate patterns of activity in a number of cortical regions contain representations that change more rapidly during periods of uncertainty following a change in behavioral context. In motor cortex, this phenomenon was indicative of discontinuous change in behavioral outputs, whereas in visual regions, the same basic phenomenon was evoked by tracking of salient environmental changes. In most other cortical regions, including dorsolateral prefrontal and anterior cingulate cortex, the phenomenon was most consistent with directly encoding the degree of uncertainty. However, in a few other regions, including orbitofrontal cortex, the phenomenon was best explained by representations of a shifting context that evolve more rapidly during periods of rapid learning. These representations may provide a dynamic substrate for learning that facilitates rapid disengagement from learned responses during periods of change. Significance StatementBrain activity patterns tend to change more rapidly during periods of uncertainty and behavioral adjustment, yet the computational role of such rapid transitions is poorly understood. Here, we identify brain regions with fMRI BOLD activity patterns that change more rapidly during periods of behavioral adjustment and use computational modeling to attribute the phenomenon to specific causes. We demonstrate that the phenomenon emerges in different brain regions for different computational reasons, the most common being the representation of uncertainty itself, but that, in a selective subset of regions including orbitofrontal cortex, the phenomenon was best explained as a shifting latent state signal that may serve to control the degree to which recent temporal context affects ongoing expectations.

To invest effort into any cognitive task, people must be sufficiently motivated. Whereas prior research has focused primarily on how the cognitive control required to complete these tasks is motivated by the potential rewards for success, it is also known that control investment can be equally motivated by the potential negative consequence for failure. Previous theoretical and experimental work has yet to examine how positive and negative incentives differentially influence the manner and intensity with which people allocate control. Here, we develop and test a normative model of control allocation under conditions of varying positive and negative performance incentives. Our model predicts, and our empirical findings confirm, that rewards for success and punishment for failure should differentially influence adjustments to the evidence accumulation rate versus response threshold, respectively. This dissociation further enabled us to infer how motivated a given person was by the consequences of success versus failure.