Cathodal electrical stimulation of frontoparietal cortex disrupts statistical learning of visual configural information

Attentional performance is facilitated by exploiting regularities and redundancies in the environment by way of incidental statistical learning. For example, during visual search, response times to a target are reduced by repeating distractor configurations-a phenomenon known as contextual cueing (Chun & Jiang, 1998). A range of neuroscientific methods have provided evidence that incidental statistical learning relies on subcortical neural structures associated with long-term memory, such as the hippocampus. Functional neuroimaging studies have also implicated the prefrontal cortex (PFC) and posterior parietal cortex (PPC) in contextual cueing. However, the extent to which these cortical regions are causally involved in statistical learning remains unclear. Here, we delivered anodal, cathodal, or sham transcranial direct current stimulation (tDCS) to the left PFC and left PPC online while participants performed a contextual cueing task. Cathodal stimulation of both PFC and PPC disrupted the early cuing effect, relative to sham and anodal stimulation. These findings causally implicate frontoparietal regions in incidental statistical learning that acts on visual configural information. We speculate that contextual cueing may rely on the availability of cognitive control resources in frontal and parietal regions

Attention and working memory capacity: insights from blocking, highlighting, and knowledge restructuring

The concept of attention is central to theorizing in learning as well as in working memory. However, research to date has yet to establish how attention as construed in one domain maps onto the other. We investigate two manifestations of attention in category- and cue-learning to examine whether they might provide common ground between learning and working memory. Experiment 1 examined blocking and highlighting effects in an associative learning paradigm, which are widely thought to be attentionally mediated. No relationship between attentional performance indicators and working memory capacity (WMC) was observed, despite the fact that WMC was strongly associated with overall learning performance. Experiment 2 used a knowledge restructuring paradigm, which is known to require recoordination of partial category knowledge using representational attention. We found that the extent to which people successfully recoordinated their knowledge was related to WMC. The results illustrate a link between WMC and representational— but not dimensional—attention in category learning

Attentional control in visual signal detection: effects of abrupt-onset and no-onset stimuli

The attention literature distinguishes two general mechanisms by which attention can benefit performance: gain (or resource) models and orienting (or switching) models. In gain models, processing efficiency is a function of a spatial distribution of capacity or resources; in orienting models, an attentional spotlight must be aligned with the stimulus location, and processing efficiency is a function of when this occurs. Although they involve different processing mechanisms, these models are difficult to distinguish empirically. We compared performance with abrupt-onset and no-onset Gabor patch stimuli in a cued detection task in which we obtained distributions of reaction time (RT) and accuracy as a function of stimulus contrast. In comparison to abrupt-onset stimuli, RTs to miscued no-onset stimuli were increased and accuracy was reduced. Modeling the data with the integrated system model of Philip L. Smith and Roger Ratcliff (2009) provided evidence for reallocation of processing resources during the course of a trial, consistent with an orienting account. Our results support a view of attention in which processing efficiency depends on a dynamic spatiotemporal distribution of resources that has both gain and orienting properties

Performance of a cognitive load inventory during simulated handoffs: Evidence for validity.

BackgroundAdvancing patient safety during handoffs remains a public health priority. The application of cognitive load theory offers promise, but is currently limited by the inability to measure cognitive load types.ObjectiveTo develop and collect validity evidence for a revised self-report inventory that measures cognitive load types during a handoff.MethodsBased on prior published work, input from experts in cognitive load theory and handoffs, and a think-aloud exercise with residents, a revised Cognitive Load Inventory for Handoffs was developed. The Cognitive Load Inventory for Handoffs has items for intrinsic, extraneous, and germane load. Students who were second- and sixth-year students recruited from a Dutch medical school participated in four simulated handoffs (two simple and two complex cases). At the end of each handoff, study participants completed the Cognitive Load Inventory for Handoffs, Paas' Cognitive Load Scale, and one global rating item for intrinsic load, extraneous load, and germane load, respectively. Factor and correlational analyses were performed to collect evidence for validity.ResultsConfirmatory factor analysis yielded a single factor that combined intrinsic and germane loads. The extraneous load items performed poorly and were removed from the model. The score from the combined intrinsic and germane load items associated, as predicted by cognitive load theory, with a commonly used measure of overall cognitive load (Pearson's r = 0.83, p < 0.001), case complexity (beta = 0.74, p < 0.001), level of experience (beta = -0.96, p < 0.001), and handoff accuracy (r = -0.34, p < 0.001).ConclusionThese results offer encouragement that intrinsic load during handoffs may be measured via a self-report measure. Additional work is required to develop an adequate measure of extraneous load

Cellular-level versus receptor-level response threshold hierarchies in T-Cell activation

Peptide-MHC (pMHC) ligand engagement by T-cell receptors (TCRs) elicits a variety of cellular responses, some of which require substantially more TCR-mediated stimulation than others. This threshold hierarchy could reside at the receptor level, where different response pathways branch off at different stages of the TCR/CD3 triggering cascade, or at the cellular level, where the cumulative TCR signal registered by the T-cell is compared to different threshold values. Alternatively, dual-level thresholds could exist. In this study, we show that the cellular hypothesis provides the most parsimonious explanation consistent with data obtained from an in-depth analysis of distinct functional responses elicited in a clonal T-cell system by a spectrum of biophysically defined altered peptide ligands across a range of concentrations. Further, we derive a mathematical model that describes how ligand density, affinity, and off-rate all affect signaling in distinct ways. However, under the kinetic regime prevailing in the experiments reported here, the TCR/pMHC class I (pMHCI) dissociation rate was found to be the main governing factor. The CD8 coreceptor modulated the TCR/pMHCI interaction and altered peptide ligand potency. Collectively, these findings elucidate the relationship between TCR/pMHCI kinetics and cellular function, thereby providing an integrated mechanistic understanding of T-cell response profiles

The promise of γδ T cells and the γδ T cell receptor for cancer immunotherapy

γδ T cells form an important part of adaptive immune responses against infections and malignant transformation. The molecular targets of human γδ T cell receptors (TCRs) remain largely unknown, but recent studies have confirmed the recognition of phosphorylated prenyl metabolites, lipids in complex with CD1 molecules and markers of cellular stress. All of these molecules are upregulated on various cancer types, highlighting the potential importance of the γδ T cell compartment in cancer immunosurveillance and paving the way for the use of γδ TCRs in cancer therapy. Ligand recognition by the γδ TCR often requires accessory/co-stimulatory stress molecules on both T cells and target cells; this cellular stress context therefore provides a failsafe against harmful self-reactivity. Unlike αβ T cells, γδ T cells recognise their targets irrespective of HLA haplotype and therefore offer exciting possibilities for off-the-shelf, pan-population cancer immunotherapies. Here, we present a review of known ligands of human γδ T cells and discuss the promise of harnessing these cells for cancer treatment

The T cell antigen receptor: the Swiss army knife of the immune

The mammalian T cell receptor (TCR) orchestrates immunity by responding to many billions of different ligands that it has never encountered before and cannot adapt to at the protein sequence level. This remarkable receptor exists in two main heterodimeric isoforms: ab TCR and gd TCR. The ab TCR is expressed on the majority of peripheral T cells. Most ab T cells recognize peptides, derived from degraded proteins, presented at the cell surface in molecular cradles called major histocompatibility complex (MHC) molecules. Recent reports have described other ab T cell subsets. These ‘unconventional’ T cells bear TCRs that are capable of recognizing lipid ligands presented in the context of the MHC-like CD1 protein family or bacterial metabolites bound to the MHC-related protein 1 (MR1). gd T cells constitute a minority of the T cell pool in human blood, but can represent up to half of total T cells in tissues such as the gut and skin. The identity of the preferred ligands for gd T cells remains obscure, but it is now known that this receptor can also functionally engage CD1-lipid, or immunoglobulin (Ig) superfamily proteins called butyrophilins in the presence of pyrophosphate intermediates of bacterial lipid biosynthesis. Interactions between TCRs and these ligands allow the host to discriminate between self and non-self and co-ordinate an attack on the latter. Here, we describe how cells of the T lymphocyte lineage and their antigen receptors are generated and discuss the various modes of antigen recognition by these extraordinarily versatile receptors

The separable effects of feature precision and item load in visual short-term memory

Visual short-term memory (VSTM) has been described as being limited by the number of discrete visual objects, the aggregate quantity of information across multiple visual objects, or some combination of the two. Many recent studies examining these capacity limitations have shown that increasing the number of items in VSTM increases the frequency and magnitude of errors in a participant's recall of the stimulus. This increase in response dispersion has been interpreted as a loss of precision in an item's representation as the number of items in memory increases, possibly due to a change in the tuning of the underlying representation. However, increased response dispersion can also be caused by a reduction in the total memory strength available for decision making as a consequence of a reduction in the total amount of a fixed resource representing a stimulus. We investigated the effects of load on the precision of memory representations in a fine orientation discrimination task. Accuracy was well captured by extending a simple sample-size model of VSTM, using a tuning function to account for the effect of orientation precision on performance. The best model of the data was one in which the item strength decreased progressively with memory load at all stimulus exposure durations but in which tuning bandwidth was invariant. Our results imply that memory strength and feature precision are experimentally dissociable attributes of VSTM

Erratum: Causal Knowledge Promotes Behavioral Self-Regulation: An Example using Climate Change Dynamics (PLoS ONE (2017) 12:9 (E0184480) DOI: 10.1371/Journal.pone.0184480)

In the Task overview: Managing a dynamic human-climate system subsection of the Introduction, there is an error in equation 4. There is a factor of τ that is missing from the denominator of the first term that appears on the right-hand side of the equation. Please view the complete, correct equation here [Formula Presented]