Cognitive measurement models decompose observed behav-ior into latent cognitive processes. For situations with morethan one condition, such models allow to test hypotheses onthe level of the latent processes. We propose a fully Bayesianensemble model approach to test hypotheses on the level ofthe latent processes in situations in which multiple measure-ment models or model classes exist. In the first step, one needsto perform a Bayesian model selection step comparing the hy-potheses within each model class. Aggregating the results ofthe first step yields ensemble posterior model probabilities. Weprovide an example for a working memory data set using anensemble of a resource model and a slots model.

The hippocampus, well known for its role in episodic memory, might also be an important brain region for extracting structure from our experiences in order to guide future decisions. Recent evidence in rodents suggests that the hippocampus supports decision making by representing task structure in cooperation with the orbitofrontal cortex (OFC). Here, we examine how the human hippocampus and OFC represent task structure during an associative learning task that required learning of both context-determined and context-invariant probabilistic associations. We find that after learning, hippocampal and lateral OFC representations differentiated between context-determined and context-invariant task structures. The degree of this differentiation within the hippocampus and lateral OFC is highly correlated. These results advance our understanding of the hippocampus and suggest that the hippocampus and OFC support goal-directed behavior by representing information that guides the selection of appropriate decision strategies.

This study examined the effects of stimulation targeting dorsolateral prefrontal cortex (DLPFC) on behavioral and neural oscillatory markers of proactive cognitive control in healthy adults. We hypothesized that active stimulation targeting the DLPFC would enhance proactive control compared to sham, leading to changes in the pattern of error rates and gamma-band power on the Dot Pattern Expectancy (DPX) task. We recorded EEG while participants completed the DPX, after receiving either 20 minutes of active DLPFC stimulation at 2 mA or sham stimulation in a counterbalanced within-participants design. The results showed significant tDCS-induced changes in the pattern of error rates on the DPX task indicative of enhanced proactive control, as well as predicted increases in gamma power associated with the engagement of proactive control. These results provide support for the role of DLPFC-mediated gamma activity in proactive cognitive control, and further, indicate that proactive control can be enhanced with non-invasive neurostimulation.

Episodic memory depends on interactions between the hippocampus and interconnected neocortical regions. Here, using data-driven analyses of resting-state functional magnetic resonance imaging (fMRI) data, we identified the networks that interact with the hippocampus-the default mode network (DMN) and a "medial temporal network" (MTN) that included regions in the medial temporal lobe (MTL) and precuneus. We observed that the MTN plays a critical role in connecting the visual network to the DMN and hippocampus. The DMN could be further divided into 3 subnetworks: a "posterior medial" (PM) subnetwork comprised of posterior cingulate and lateral parietal cortices; an "anterior temporal" (AT) subnetwork comprised of regions in the temporopolar and dorsomedial prefrontal cortex; and a "medial prefrontal" (MP) subnetwork comprised of regions primarily in the medial prefrontal cortex (mPFC). These networks vary in their functional connectivity (FC) along the hippocampal long axis and represent different kinds of information during memory-guided decision-making. Finally, a Neurosynth meta-analysis of fMRI studies suggests new hypotheses regarding the functions of the MTN and DMN subnetworks, providing a framework to guide future research on the neural architecture of episodic memory.