The publication data currently available has been vetted by Vanderbilt faculty, staff, administrators and trainees. The data itself is retrieved directly from NCBI's PubMed and is automatically updated on a weekly basis to ensure accuracy and completeness.
If you have any questions or comments, please contact us.
The marriage of cognitive neurophysiology and mathematical psychology to understand decision-making has been exceptionally productive. This interdisciplinary area is based on the proposition that particular neurons or circuits instantiate the accumulation of evidence specified by mathematical models of sequential sampling and stochastic accumulation. This linking proposition has earned widespread endorsement. Here, a brief survey of the history of the proposition precedes a review of multiple conundrums and paradoxes concerning the accuracy, precision, and transparency of that linking proposition. Correctly establishing how abstract models of decision-making are instantiated by particular neural circuits would represent a remarkable accomplishment in mapping mind to brain. Failing would reveal challenging limits for cognitive neuroscience. This is such a vigorous area of research because so much is at stake.
Copyright © 2019 Elsevier Ltd. All rights reserved.
Rapid and accurate detection of threat is adaptive. Yet, threat-related attentional biases, including hypervigilance, avoidance, and attentional disengagement delays, may contribute to the etiology and maintenance of anxiety disorders. Behavioral measures of attentional bias generally indicate that threat demands more attentional resources; however, indices exploring differential allocation of attention using reaction time fail to clarify the time course by which attention is deployed under threatening circumstances in healthy and anxious populations. In this review, we conduct an interpretive synthesis of 28 attentional bias studies focusing on event-related potentials (ERPs) as a primary outcome to inform an ERP model of the neural chronometry of attentional bias in healthy and anxious populations. The model posits that both healthy and anxious populations display modulations of early ERP components, including the P1, N170, P2, and N2pc, in response to threatening and emotional stimuli, suggesting that both typical and abnormal patterns of attentional bias are characterized by enhanced allocation of attention to threat and emotion at earlier stages of processing. Compared to anxious populations, healthy populations more clearly demonstrate modulations of later components, such as the P3, indexing conscious and evaluative processing of threat and emotion and disengagement difficulties at later stages of processing. Findings from the interpretive synthesis, existing bias models, and extant neural literature on attentional systems are then integrated to inform a conceptual model of the processes and substrates underlying threat appraisal and resource allocation in healthy and anxious populations. To conclude, we discuss therapeutic interventions for attentional bias and future directions.
Copyright © 2019 Elsevier B.V. All rights reserved.
Response inhibition is essential for navigating everyday life. Its derailment is considered integral to numerous neurological and psychiatric disorders, and more generally, to a wide range of behavioral and health problems. Response-inhibition efficiency furthermore correlates with treatment outcome in some of these conditions. The stop-signal task is an essential tool to determine how quickly response inhibition is implemented. Despite its apparent simplicity, there are many features (ranging from task design to data analysis) that vary across studies in ways that can easily compromise the validity of the obtained results. Our goal is to facilitate a more accurate use of the stop-signal task. To this end, we provide 12 easy-to-implement consensus recommendations and point out the problems that can arise when they are not followed. Furthermore, we provide user-friendly open-source resources intended to inform statistical-power considerations, facilitate the correct implementation of the task, and assist in proper data analysis.
© 2019, Verbruggen et al.
Parkinson's disease (PD) is characterized by dysfunction in frontal cortical and striatal networks that regulate action control. We investigated the pharmacological effect of dopamine agonist replacement therapy on frontal cortical activity and motor inhibition. Using Arterial Spin Labeling MRI, we examined 26 PD patients in the off- and on-dopamine agonist medication states to assess the effect of dopamine agonists on frontal cortical regional cerebral blood flow. Motor inhibition was measured by the Simon task in both medication states. We applied the dual process activation suppression model to dissociate fast response impulses from motor inhibition of incorrect responses. General linear regression model analyses determined the medication effect on regional cerebral blood flow and motor inhibition, and the relationship between regional cerebral blood flow and motor inhibitory proficiency. We show that dopamine agonist administration increases frontal cerebral blood flow, particularly in the pre-supplementary motor area (pre-SMA) and the dorsolateral prefrontal cortex (DLPFC). Higher regional blood flow in the pre-SMA, DLPFC and motor cortex was associated with better inhibitory control, suggesting that treatments which improve frontal cortical activity could ameliorate motor inhibition deficiency in PD patients.
Copyright © 2019 Elsevier Ltd. All rights reserved.
The medial frontal cortex enables performance monitoring, indexed by the error-related negativity (ERN) and manifested by performance adaptations. We recorded electroencephalogram over and neural spiking across all layers of the supplementary eye field, an agranular cortical area, in monkeys performing a saccade-countermanding (stop signal) task. Neurons signaling error production, feedback predicting reward gain or loss, and delivery of fluid reward had different spike widths and were concentrated differently across layers. Neurons signaling error or loss of reward were more common in layers 2 and 3 (L2/3), whereas neurons signaling gain of reward were more common in layers 5 and 6 (L5/6). Variation of error- and reinforcement-related spike rates in L2/3 but not L5/6 predicted response time adaptation. Variation in error-related spike rate in L2/3 but not L5/6 predicted ERN magnitude. These findings reveal novel features of cortical microcircuitry supporting performance monitoring and confirm one cortical source of the ERN.
Frontal eye field (FEF) in macaque monkeys contributes to visual attention, visual-motor transformations and production of eye movements. Traditionally, neurons in FEF have been classified by the magnitude of increased discharge rates following visual stimulus presentation, during a waiting period, and associated with eye movement production. However, considerable heterogeneity remains within the traditional visual, visuomovement, and movement categories. Cluster analysis is a data-driven method of identifying self-segregating groups within a dataset. Because many cluster analysis techniques exist and outcomes vary with analysis assumptions, consensus clustering aggregates over multiple analyses, identifying robust groups. To describe more comprehensively the neuronal composition of FEF, we applied a consensus clustering technique for unsupervised categorization of patterns of spike rate modulation measured during a memory-guided saccade task. We report 10 functional categories, expanding on the traditional 3 categories. Categories were distinguished by latency, magnitude, and sign of visual response; the presence of sustained activity; and the dynamics, magnitude and sign of saccade-related modulation. Consensus clustering can include other metrics and can be applied to datasets from other brain regions to provide better information guiding microcircuit models of cortical function.
The neural underpinnings of perceptual awareness have been extensively studied using unisensory (e.g., visual alone) stimuli. However, perception is generally multisensory, and it is unclear whether the neural architecture uncovered in these studies directly translates to the multisensory domain. Here, we use EEG to examine brain responses associated with the processing of visual, auditory, and audiovisual stimuli presented near threshold levels of detectability, with the aim of deciphering similarities and differences in the neural signals indexing the transition into perceptual awareness across vision, audition, and combined visual-auditory (multisensory) processing. More specifically, we examine (1) the presence of late evoked potentials (∼>300 msec), (2) the across-trial reproducibility, and (3) the evoked complexity associated with perceived versus nonperceived stimuli. Results reveal that, although perceived stimuli are associated with the presence of late evoked potentials across each of the examined sensory modalities, between-trial variability and EEG complexity differed for unisensory versus multisensory conditions. Whereas across-trial variability and complexity differed for perceived versus nonperceived stimuli in the visual and auditory conditions, this was not the case for the multisensory condition. Taken together, these results suggest that there are fundamental differences in the neural correlates of perceptual awareness for unisensory versus multisensory stimuli. Specifically, the work argues that the presence of late evoked potentials, as opposed to neural reproducibility or complexity, most closely tracks perceptual awareness regardless of the nature of the sensory stimulus. In addition, the current findings suggest a greater similarity between the neural correlates of perceptual awareness of unisensory (visual and auditory) stimuli when compared with multisensory stimuli.
Most data analyses rely on models. To complement statistical models, psychologists have developed cognitive models, which translate observed variables into psychologically interesting constructs. Response time models, in particular, assume that response time and accuracy are the observed expression of latent variables including 1) ease of processing, 2) response caution, 3) response bias, and 4) non-decision time. Inferences about these psychological factors, hinge upon the validity of the models' parameters. Here, we use a blinded, collaborative approach to assess the validity of such model-based inferences. Seventeen teams of researchers analyzed the same 14 data sets. In each of these two-condition data sets, we manipulated properties of participants' behavior in a two-alternative forced choice task. The contributing teams were blind to the manipulations, and had to infer what aspect of behavior was changed using their method of choice. The contributors chose to employ a variety of models, estimation methods, and inference procedures. Our results show that, although conclusions were similar across different methods, these "modeler's degrees of freedom" did affect their inferences. Interestingly, many of the simpler approaches yielded as robust and accurate inferences as the more complex methods. We recommend that, in general, cognitive models become a typical analysis tool for response time data. In particular, we argue that the simpler models and procedures are sufficient for standard experimental designs. We finish by outlining situations in which more complicated models and methods may be necessary, and discuss potential pitfalls when interpreting the output from response time models.
Cognitive models aim to explain complex human behavior in terms of hypothesized mechanisms of the mind. These mechanisms can be formalized in terms of mathematical structures containing parameters that are theoretically meaningful. For example, in the case of perceptual decision making, model parameters might correspond to theoretical constructs like response bias, evidence quality, response caution, and the like. Formal cognitive models go beyond verbal models in that cognitive mechanisms are instantiated in terms of mathematics and they go beyond statistical models in that cognitive model parameters are psychologically interpretable. We explore three key elements used to formally evaluate cognitive models: parameter estimation, model prediction, and model selection. We compare and contrast traditional approaches with Bayesian statistical approaches to performing each of these three elements. Traditional approaches rely on an array of seemingly ad hoc techniques, whereas Bayesian statistical approaches rely on a single, principled, internally consistent system. We illustrate the Bayesian statistical approach to evaluating cognitive models using a running example of the Linear Ballistic Accumulator model of decision making (Brown SD, Heathcote A. The simplest complete model of choice response time: linear ballistic accumulation. Cogn Psychol 2008, 57:153-178). WIREs Cogn Sci 2018, 9:e1458. doi: 10.1002/wcs.1458 This article is categorized under: Neuroscience > Computation Psychology > Reasoning and Decision Making Psychology > Theory and Methods.
© 2017 Wiley Periodicals, Inc.
Attending to a visual stimulus increases its detectability, even if gaze is directed elsewhere. This covert attentional selection is known to enhance spiking across many brain areas, including the primary visual cortex (V1). Here we investigate the temporal dynamics of attention-related spiking changes in V1 of macaques performing a task that separates attentional selection from the onset of visual stimulation. We found that preceding attentional enhancement there was a sharp, transient decline in spiking following presentation of an attention-guiding cue. This disruption of V1 spiking was not observed in a task-naïve subject that passively observed the same stimulus sequence, suggesting that sensory activation is insufficient to cause suppression. Following this suppression, attended stimuli evoked more spiking than unattended stimuli, matching previous reports of attention-related activity in V1. Laminar analyses revealed a distinct pattern of activation in feedback-associated layers during both the cue-induced suppression and subsequent attentional enhancement. These findings suggest that top-down modulation of V1 spiking can be bidirectional and result in either suppression or enhancement of spiking responses.