Christopher Summerfield

An information theoretical approach to prefrontal executive function

The prefrontal cortex subserves executive control--that is, the ability to select actions or thoughts in relation to internal goals. Here, we propose a theory that draws upon concepts from information theory to describe the architecture of executive control in the lateral prefrontal cortex. Supported by evidence from brain imaging in human subjects, the model proposes that action selection is guided by hierarchically ordered control signals, processed in a network of brain regions organized along the anterior-posterior axis of the lateral prefrontal cortex. The theory clarifies how executive control can operate as a unitary function, despite the requirement that information be integrated across multiple distinct, functionally specialized prefrontal regions.

Expectation (and attention) in visual cognition

Visual cognition is limited by computational capacity, because the brain can process only a fraction of the visual sensorium in detail, and by the inherent ambiguity of the information entering the visual system. Two mechanisms mitigate these burdens: attention prioritizes stimulus processing on the basis of motivational relevance, and expectations constrain visual interpretation on the basis of prior likelihood. Of the two, attention has been extensively investigated while expectation has been relatively neglected. Here, we review recent work that has begun to delineate a neurobiology of visual expectation, and contrast the findings with those of the attention literature, to explore how these two central influences on visual perception overlap, differ and interact.

Predictive Codes for Forthcoming Perception in the Frontal Cortex

Incoming sensory information is often ambiguous, and the brain has to make decisions during perception. “Predictive coding” proposes that the brain resolves perceptual ambiguity by anticipating the forthcoming sensory environment, generating a template against which to match observed sensory evidence. We observed a neural representation of predicted perception in the medial frontal cortex, while human subjects decided whether visual objects were faces or not. Moreover, perceptual decisions about faces were associated with an increase in top-down connectivity from the frontal cortex to face-sensitive visual areas, consistent with the matching of predicted and observed evidence for the presence of faces.

Expectation in perceptual decision making: neural and computational mechanisms

Sensory signals are highly structured in both space and time. These structural regularities in visual information allow expectations to form about future stimulation, thereby facilitating decisions about visual features and objects. Here, we discuss how expectation modulates neural signals and behaviour in humans and other primates. We consider how expectations bias visual activity before a stimulus occurs, and how neural signals elicited by expected and unexpected stimuli differ. We discuss how expectations may influence decision signals at the computational level. Finally, we consider the relationship between visual expectation and related concepts, such as attention and adaptation.

Hybrid computing using a neural network with dynamic external memory

Artificial neural networks are remarkably adept at sensory processing, sequence learning and reinforcement learning, but are limited in their ability to represent variables and data structures and to store data over long timescales, owing to the lack of an external memory. Here we introduce a machine learning model called a differentiable neural computer (DNC), which consists of a neural network that can read from and write to an external memory matrix, analogous to the random-access memory in a conventional computer. Like a conventional computer, it can use its memory to represent and manipulate complex data structures, but, like a neural network, it can learn to do so from data. When trained with supervised learning, we demonstrate that a DNC can successfully answer synthetic questions designed to emulate reasoning and inference problems in natural language. We show that it can learn tasks such as finding the shortest path between specified points and inferring the missing links in randomly generated graphs, and then generalize these tasks to specific graphs such as transport networks and family trees. When trained with reinforcement learning, a DNC can complete a moving blocks puzzle in which changing goals are specified by sequences of symbols. Taken together, our results demonstrate that DNCs have the capacity to solve complex, structured tasks that are inaccessible to neural networks without external read–write memory.

Metacognition in human decision-making: confidence and error monitoring

People are capable of robust evaluations of their decisions: they are often aware of their mistakes even without explicit feedback, and report levels of confidence in their decisions that correlate with objective performance. These metacognitive abilities help people to avoid making the same mistakes twice, and to avoid overcommitting time or resources to decisions that are based on unreliable evidence. In this review, we consider progress in characterizing the neural and mechanistic basis of these related aspects of metacognition—confidence judgements and error monitoring—and identify crucial points of convergence between methods and theories in the two fields. This convergence suggests that common principles govern metacognitive judgements of confidence and accuracy; in particular, a shared reliance on post-decisional processing within the systems responsible for the initial decision. However, research in both fields has focused rather narrowly on simple, discrete decisions—reflecting the correspondingly restricted focus of current models of the decision process itself—raising doubts about the degree to which discovered principles will scale up to explain metacognitive evaluation of real-world decisions and actions that are fluid, temporally extended, and embedded in the broader context of evolving behavioural goals.

Amygdalar and hippocampal MRI volumetric reductions in Parkinson's disease with dementia

Parkinsons disease (PD) involves neuropathological changes in the limbic system that lead to neuronal loss and volumetric reductions of several nuclei. We investigated possible volumetric reductions of the amygdala and hippocampus associated to PD. We carried out magnetic resonance imaging (MRI) volumetric studies in 16 patients with PD and dementia (PDD), 16 patients with PD without dementia (PD), and 16 healthy subjects. The general analysis of variance (ANOVA) showed a significant group effect (for the amygdala, P = 0.01; for the hippocampus, P = 0.005). A post‐hoc test demonstrated that the differences were due to PDD and control group comparisons for the amygdala (P = 0.008) and for the hippocampus (P = 0.004). In nondemented PD subjects, we observed an 11% reduction in the amygdala and a 10% reduction in the hippocampus compared with that in controls. In summary, demented PD patients have clear amygdalar and hippocampal atrophy that remains statistically significant after controlling for global cerebral atrophy. Nondemented PD patients also showed a degree of volumetric reduction in these structures although the differences were not statistically significant.

Longitudinal evaluation of cerebral morphological changes in Parkinson's disease with and without dementia

AbstractObjectiveTo investigate the pattern of brain atrophy across time in a sample of Parkinsons disease (PD) patients with and without dementia using voxelbased morphometry (VBM) analysis.MethodsThe initial sample comprised thirteen non–demented PD patients and sixteen demented patients. Longitudinal cognitive assessment and structural MRI were performed. The mean follow–up period was 25 months (SD = 5.2). From this initial group, eight PD patients with dementia (5 men and 3 women) and eleven PD patients without dementia (7 men and 4 women) were reevaluated. MRI 3D structural images were acquired and analyzed by means of the optimized VBM procedure with Statistical Parametric Mapping (SPM2).ResultsVBM analysis showed a progressive grey matter volume decrease in patients with PD without dementia in limbic, paralimbic and neocortical associative temporooccipital regions. In patients with dementia the loss mainly involved neocortical regions.ConclusionVBM revealed a significant loss of grey matter volume in PD patients with and without dementia with disease progression. The decrease in limbic and paralimbic regions is widespread in non–demented patients. Neocortical volume reduction is the most relevant finding in patients with dementia. This suggests that the neocortex is a substrate for dementia in Parkinson disease.

Two Mechanisms for Task Switching in the Prefrontal Cortex

Measuring the cognitive and neural sequelae of switching between tasks permits a window into the flexible functioning of the executive control system. Prolonged reaction times (RTs) after task switches are accompanied by increases in brain activity in the anterior cingulate cortex (ACC) and dorsolateral prefrontal cortex (dlPFC), but the contribution made by these regions to task level control remains controversial. Here, subjects performed a hybrid spatial Stroop/task-switching paradigm, requiring them to respond with a joystick movement to congruent or incongruent spatial/verbal cues. Relative to the previous trial, the active task either switched or remained the same. Calculating switch costs as a function of current and previous trial congruency, we observed both a general RT increase on every switch trial, and additional slowing and impairment to performance when the switch occurred on the second of two successive incongruent trials (iI trials). Imaging data revealed corresponding neural concomitants of these two switch costs: the ACC was activated by task switches regardless of trial type (including congruent trials in which task-relevant and task-irrelevant information did not clash), whereas the caudal dlPFC exhibited a switch cost that was unique to iI trials. We argue that the ACC configures the priorities associated with a new task, whereas the dlPFC tackles interference from recently active, rivalrous task sets. These data contribute to a literature arguing that human cognitive flexibility benefits from the setting of new priorities for future action as well as the overcoming of interference from previously active task sets.

Rhythmic Fluctuations in Evidence Accumulation during Decision Making in the Human Brain

Categorical choices are preceded by the accumulation of sensory evidence in favor of one action or another. Current models describe evidence accumulation as a continuous process occurring at a constant rate, but this view is inconsistent with accounts of a psychological refractory period during sequential information processing. During multisample perceptual categorization, we found that the neural encoding of momentary evidence in human electrical brain signals and its subsequent impact on choice fluctuated rhythmically according to the phase of ongoing parietal delta oscillations (1-3 Hz). By contrast, lateralized beta-band power (10-30 Hz) overlying human motor cortex encoded the integrated evidence as a response preparation signal. These findings draw a clear distinction between central and motor stages of perceptual decision making, with successive samples of sensory evidence competing to pass through a serial processing bottleneck before being mapped onto action.