Dissociation and integration of outcome and state uncertainty signals in cognitive control.

Signals related to uncertainty are frequently observed in regions of the cognitive control network, including anterior cingulate/medial prefrontal cortex (ACC/mPFC), dorsolateral prefrontal cortex (dlPFC), and anterior insular cortex. Uncertainty generally refers to conditions in which decision variables may assume multiple possible values and can arise at multiple points in the perception-action cycle, including sensory input, inferred states of the environment, and the consequences of actions. These sources of uncertainty are frequently correlated: noisy input can lead to unreliable estimates of the state of the environment, with consequential influences on action selection.

Multisensory integration in humans with spinal cord injury.

Although multisensory integration (MSI) has been extensively studied, the underlying mechanisms remain a topic of ongoing debate. Here we investigate these mechanisms by comparing MSI in healthy controls to a clinical population with spinal cord injury (SCI). Deafferentation following SCI induces sensorimotor impairment, which may alter the ability to synthesize cross-modal information.

Degeneracy measures in biologically plausible random Boolean networks.

Background: Degeneracy-the ability of structurally different elements to perform similar functions-is a property of many biological systems. Highly degenerate systems show resilience to perturbations and damage because the system can compensate for compromised function due to reconfiguration of the underlying network dynamics. Degeneracy thus suggests how biological systems can thrive despite changes to internal and external demands.

Surprise, value and control in anterior cingulate cortex during speeded decision-making.

Activity in the dorsal anterior cingulate cortex (dACC) is observed across a variety of contexts, and its function remains intensely debated in the field of cognitive neuroscience. While traditional views emphasize its role in inhibitory control (suppressing prepotent, incorrect actions), recent proposals suggest a more active role in motivated control (invigorating actions to obtain rewards). Lagging behind empirical findings, formal models of dACC function primarily focus on inhibitory control, highlighting surprise, choice difficulty and value of control as key computations.

Task-specific prioritization of reward and effort information: Novel insights from behavior and computational modeling.

Efficient integration of environmental information is critical in goal-directed behavior. Motivational information regarding potential rewards and costs (such as required effort) affects performance and decisions whether to engage in a task. While it is generally acknowledged that costs and benefits are integrated to determine the level of effort to be exerted, how this integration occurs remains an open question.

Fast, Accurate, and Stable Feature Selection Using Neural Networks.

Multi-voxel pattern analysis often necessitates feature selection due to the high dimensional nature of neuroimaging data. In this context, feature selection techniques serve the dual purpose of potentially increasing classification accuracy and revealing sets of features that best discriminate between classes. However, feature selection techniques in current, widespread use in the literature suffer from a number of deficits, including the need for extended computational time, lack of consistency in selecting features relevant to classification, and only marginal increases in classifier accuracy.

The Role of the Anterior Cingulate Cortex in Prediction Error and Signaling Surprise.

In the past two decades, reinforcement learning (RL) has become a popular framework for understanding brain function. A key component of RL models, prediction error, has been associated with neural signals throughout the brain, including subcortical nuclei, primary sensory cortices, and prefrontal cortex. Depending on the location in which activity is observed, the functional interpretation of prediction error may change: Prediction errors may reflect a discrepancy in the anticipated and actual value of reward, a signal indicating the salience or novelty of a stimulus, and many other interpretations.

Predicting Motivation: Computational Models of PFC Can Explain Neural Coding of Motivation and Effort-based Decision-making in Health and Disease.

Human behavior is strongly driven by the pursuit of rewards. In daily life, however, benefits mostly come at a cost, often requiring that effort be exerted to obtain potential benefits. Medial PFC (MPFC) and dorsolateral PFC (DLPFC) are frequently implicated in the expectation of effortful control, showing increased activity as a function of predicted task difficulty.

Prefrontal Cortex in Control: Broadening the Scope to Identify Mechanisms.

Sometime in the past two decades, neuroimaging and behavioral research converged on pFC as an important locus of cognitive control and decision-making, and that seems to be the last thing anyone has agreed on since. Every year sees an increase in the number of roles and functions attributed to distinct subregions within pFC, roles that may explain behavior and neural activity in one context but might fail to generalize across the many behaviors in which each region is implicated. Emblematic of this ongoing proliferation of functions is dorsal ACC (dACC).

Integrative Modeling of Prefrontal Cortex.

pFC is generally regarded as a region critical for abstract reasoning and high-level cognitive behaviors. As such, it has become the focus of intense research involving a wide variety of subdisciplines of neuroscience and employing a diverse range of methods. However, even as the amount of data on pFC has increased exponentially, it appears that progress toward understanding the general function of the region across a broad array of contexts has not kept pace.

Foraging Value, Risk Avoidance, and Multiple Control Signals: How the Anterior Cingulate Cortex Controls Value-based Decision-making.

Recent work on the role of the ACC in cognition has focused on choice difficulty, action value, risk avoidance, conflict resolution, and the value of exerting control among other factors. A main underlying question is what are the output signals of ACC, and relatedly, what is their effect on downstream cognitive processes? Here we propose a model of how ACC influences cognitive processing in other brain regions that choose actions. The model builds on the earlier Predicted Response Outcome model and suggests that ACC learns to represent specifically the states in which the potential costs or risks of an action are high, on both short and long timescales.

Distinct Regions within Medial Prefrontal Cortex Process Pain and Cognition.

Unlabelled: Neuroimaging studies of the medial prefrontal cortex (mPFC) suggest that the dorsal anterior cingulate cortex (dACC) region is responsive to a wide variety of stimuli and psychological states, such as pain, cognitive control, and prediction error (PE). In contrast, a recent meta-analysis argues that the dACC is selective for pain, whereas the supplementary motor area (SMA) and pre-SMA are specifically associated with higher-level cognitive processes (Lieberman and Eisenberger, 2015). To empirically test this claim, we manipulated effects of pain, conflict, and PE in a single experiment using human subjects.

Hierarchical Error Representation: A Computational Model of Anterior Cingulate and Dorsolateral Prefrontal Cortex.

Anterior cingulate and dorsolateral prefrontal cortex (ACC and dlPFC, respectively) are core components of the cognitive control network. Activation of these regions is routinely observed in tasks that involve monitoring the external environment and maintaining information in order to generate appropriate responses. Despite the ubiquity of studies reporting coactivation of these two regions, a consensus on how they interact to support cognitive control has yet to emerge.

Distinct regions of anterior cingulate cortex signal prediction and outcome evaluation.

A number of theories have been proposed to account for the role of anterior cingulate cortex (ACC) and the broader medial prefrontal cortex (mPFC) in cognition. The recent Prediction of Response Outcome (PRO) computational model casts the mPFC in part as performing two theoretically distinct functions: learning to predict the various possible outcomes of actions, and then evaluating those predictions against the actual outcomes. Simulations have shown that this new model can account for an unprecedented range of known mPFC effects, but the central theory of distinct prediction and evaluation mechanisms within ACC remains untested.

Discounting of reward sequences: a test of competing formal models of hyperbolic discounting.

Humans are known to discount future rewards hyperbolically in time. Nevertheless, a formal recursive model of hyperbolic discounting has been elusive until recently, with the introduction of the hyperbolically discounted temporal difference (HDTD) model. Prior to that, models of learning (especially reinforcement learning) have relied on exponential discounting, which generally provides poorer fits to behavioral data.

Computational models of performance monitoring and cognitive control.

The medial prefrontal cortex (mPFC) has been the subject of intense interest as a locus of cognitive control. Several computational models have been proposed to account for a range of effects, including error detection, conflict monitoring, error likelihood prediction, and numerous other effects observed with single-unit neurophysiology, fMRI, and lesion studies. Here, we review the state of computational models of cognitive control and offer a new theoretical synthesis of the mPFC as signaling response–outcome predictions.

Are we there yet? The four-year impact of a VA fellowship program on the recovery orientation of rehabilitation programs.

Objective: This study represents the first program evaluation of the impact of a Psychosocial Rehabilitation (PSR) fellowship program within the Veterans Health Administration (VHA). Specifically, it examines the recovery orientation of five mental health rehabilitation programs at the Edith Nourse Rogers Memorial VA Medical Center (ENRM VAMC) in Bedford, MA by comparing program stakeholder rating of the "recovery orientation" between the initial data and the four-year follow-up during which the PSR fellowship was in operation. The goal of this fellowship program is to increase the VHA's fidelity to recovery-oriented best practice recommendations.

Hyperbolically discounted temporal difference learning.

Hyperbolic discounting of future outcomes is widely observed to underlie choice behavior in animals. Additionally, recent studies (Kobayashi & Schultz, 2008) have reported that hyperbolic discounting is observed even in neural systems underlying choice. However, the most prevalent models of temporal discounting, such as temporal difference learning, assume that future outcomes are discounted exponentially.