Neural Contributions to Reduced Fluid Intelligence across the Adult Lifespan.

Fluid intelligence, the ability to solve novel, complex problems, declines steeply during healthy human aging. Using fMRI, fluid intelligence has been repeatedly associated with activation of a frontoparietal brain network, and impairment following focal damage to these regions suggests that fluid intelligence depends on their integrity. It is therefore possible that age-related functional differences in frontoparietal activity contribute to the reduction in fluid intelligence. This paper reports on analysis of the Cambridge Center for Ageing and Neuroscience data, a large, population-based cohort of healthy males and females across the adult lifespan. The data support a model in which age-related differences in fluid intelligence are partially mediated by the responsiveness of frontoparietal regions to novel problem-solving. We first replicate a prior finding of such mediation using an independent sample. We then precisely localize the mediating brain regions, and show that mediation is specifically associated with voxels most activated by cognitive demand, but not with voxels suppressed by cognitive demand. We quantify the robustness of this result to potential unmodeled confounders, and estimate the causal direction of the effects. Finally, exploratory analyses suggest that neural mediation of age-related differences in fluid intelligence is moderated by the variety of regular physical activities, more reliably than by their frequency or duration. An additional moderating role of the variety of nonphysical activities emerged when controlling for head motion. A better understanding of the mechanisms that link healthy aging with lower fluid intelligence may suggest strategies for mitigating such decline.SIGNIFICANCE STATEMENT Global populations are living longer, driving urgency to understand age-related cognitive declines. Fluid intelligence is of prime importance because it reflects performance across many domains, and declines especially steeply during healthy aging. Despite consensus that fluid intelligence is associated with particular frontoparietal brain regions, little research has investigated suggestions that under-responsiveness of these regions mediates age-related decline. We replicate a recent demonstration of such mediation, showing specific association with brain regions most activated by cognitive demand, and robustness to moderate confounding by unmodeled variables. By showing that this mediation model is moderated by the variety of regular physical activities, more reliably than by their frequency or duration, we identify a potential modifiable lifestyle factor that may help promote successful aging.

Structural connectivity of the multiple demand network in humans and comparison to the macaque brain.

Fluid intelligence encompasses a wide range of abilities such as working memory, problem-solving, and relational reasoning. In the human brain, these abilities are associated with the Multiple Demand Network, traditionally thought to involve combined activity of specific regions predominantly in the prefrontal and parietal cortices. However, the structural basis of the interactions between areas in the Multiple Demand Network, as well as their evolutionary basis among primates, remains largely unexplored. Here, we exploit diffusion MRI to elucidate the major white matter pathways connecting areas of the human core and extended Multiple Demand Network. We then investigate whether similar pathways can be identified in the putative homologous areas of the Multiple Demand Network in the macaque monkey. Finally, we contrast human and monkey networks using a recently proposed approach to compare different species' brains within a common organizational space. Our results indicate that the core Multiple Demand Network relies mostly on dorsal longitudinal connections and, although present in the macaque, these connections are more pronounced in the human brain. The extended Multiple Demand Network relies on distinct pathways and communicates with the core Multiple Demand Network through connections that also appear enhanced in the human compared with the macaque.

NeoSLAM: Long-Term SLAM Using Computational Models of the Brain.

Simultaneous Localization and Mapping (SLAM) is a fundamental problem in the field of robotics, enabling autonomous robots to navigate and create maps of unknown environments. Nevertheless, the SLAM methods that use cameras face problems in maintaining accurate localization over extended periods across various challenging conditions and scenarios. Following advances in neuroscience, we propose NeoSLAM, a novel long-term visual SLAM, which uses computational models of the brain to deal with this problem. Inspired by the human neocortex, NeoSLAM is based on a hierarchical temporal memory model that has the potential to identify temporal sequences of spatial patterns using sparse distributed representations. Being known to have a high representational capacity and high tolerance to noise, sparse distributed representations have several properties, enabling the development of a novel neuroscience-based loop-closure detector that allows for real-time performance, especially in resource-constrained robotic systems. The proposed method has been thoroughly evaluated in terms of environmental complexity by using a wheeled robot deployed in the field and demonstrated that the accuracy of loop-closure detection was improved compared with the traditional RatSLAM system.

Anomaly Detection Methods in Autonomous Robotic Missions.

Since 2015, there has been an increase in articles on anomaly detection in robotic systems, reflecting its growing importance in improving the robustness and reliability of the increasingly utilized autonomous robots. This review paper investigates the literature on the detection of anomalies in Autonomous Robotic Missions (ARMs). It reveals different perspectives on anomaly and juxtaposition to fault detection. To reach a consensus, we infer a unified understanding of anomalies that encapsulate their various characteristics observed in ARMs and propose a classification of anomalies in terms of spatial, temporal, and spatiotemporal elements based on their fundamental features. Further, the paper discusses the implications of the proposed unified understanding and classification in ARMs and provides future directions. We envisage a study surrounding the specific use of the term anomaly, and methods for their detection could contribute to and accelerate the research and development of a universal anomaly detection system for ARMs.

Lifespan differences in visual short-term memory load-modulated functional connectivity.

Working memory is critical to higher-order executive processes and declines throughout the adult lifespan. However, our understanding of the neural mechanisms underlying this decline is limited. Recent work suggests that functional connectivity between frontal control and posterior visual regions may be critical, but examinations of age differences therein have been limited to a small set of brain regions and extreme group designs (i.e., comparing young and older adults). In this study, we build on previous research by using a lifespan cohort and a whole-brain approach to investigate working memory load-modulated functional connectivity in relation to age and performance. The article reports on analysis of the Cambridge center for Ageing and Neuroscience (Cam-CAN) data. Participants from a population-based lifespan cohort (N = 101, age 23-86) performed a visual short-term memory task during functional magnetic resonance imaging. Visual short-term memory was measured with a delayed recall task for visual motion with three different loads. Whole-brain load-modulated functional connectivity was estimated using psychophysiological interactions in a hundred regions of interest, sorted into seven networks (Schaefer et al., 2018, Yeo et al., 2011). Results showed that load-modulated functional connectivity was strongest within the dorsal attention and visual networks during encoding and maintenance. With increasing age, load-modulated functional connectivity strength decreased throughout the cortex. Whole-brain analyses for the relation between connectivity and behavior were non-significant. Our results give additional support to the sensory recruitment model of working memory. We also demonstrate the widespread negative impact of age on the modulation of functional connectivity by working memory load. Older adults might already be close to ceiling in terms of their neural resources at the lowest load and therefore less able to further increase connectivity with increasing task demands.

Evolution of a cross-feeding interaction following a key innovation in a long-term evolution experiment with Escherichia coli.

The evolution of a novel trait can profoundly change an organism's effects on its environment, which can in turn affect the further evolution of that organism and any coexisting organisms. We examine these effects and feedbacks following the evolution of a novel function in the Long-Term Evolution Experiment (LTEE) with Escherichia coli. A characteristic feature of E. coli is its inability to grow aerobically on citrate (Cit-). Nonetheless, a Cit+ variant with this capacity evolved in one LTEE population after 31 000 generations. The Cit+ clade then coexisted stably with another clade that retained the ancestral Cit- phenotype. This coexistence was shaped by the evolution of a cross-feeding relationship based on C4-dicarboxylic acids, particularly succinate, fumarate, and malate, that the Cit+ variants release into the medium. Both the Cit- and Cit+ cells evolved to grow on these excreted resources. The evolution of aerobic growth on citrate thus led to a transition from an ecosystem based on a single limiting resource, glucose, to one with at least five resources that were either shared or partitioned between the two coexisting clades. Our findings show that evolutionary novelties can change environmental conditions in ways that facilitate diversity by altering ecosystem structure and the evolutionary trajectories of coexisting lineages.

Roles of the Default Mode and Multiple-Demand Networks in Naturalistic versus Symbolic Decisions.

The default mode network (DMN) is often associated with representing semantic, social, and situational content of contexts and episodes. The DMN may therefore be important for contextual decision-making, through representing situational constraints and simulating common courses of events. Most decision-making paradigms, however, use symbolic stimuli and instead implicate cognitive control regions, such as the multiple demand (MD) system. This fMRI study aimed to contrast the brain mechanisms underlying decision-making based on rich naturalistic contexts or symbolic cues. While performing an ongoing task, 40 human participants (25 female) responded to different sounds. For one sound, the stimulus-response mapping was fixed; responses for the other sounds depended on the visual context: either lifelike scenes or letter symbols, varying across participants. Despite minimal behavioral differences between the groups, posterior DMN regions showed increased activity during context-dependent decision-making using the naturalistic scenes only, compared with symbolic cues. More anterior temporal and frontal DMN regions showed a different pattern, with sensitivity to the need for contextual control, but not to the type of context. Furthermore, in the scenes group, widespread DMN regions showed stronger representation of not just the context but also the sound whose significance it modulated. In comparison, the MD system showed strong univariate activity for every decision, but, intriguingly, somewhat reduced activity in the case of a scene-based but demanding context-dependent decision. Depending on context, we suggest, either DMN or MD regions may play a prominent role in selection and control of appropriate behavior.SIGNIFICANCE STATEMENT Contextual knowledge is widely believed to be important for guiding real-world goal-directed behavior. Much remains to be understood, however, regarding the underlying brain mechanisms. Using a novel paradigm to contrast decisions based on richly meaningful naturalistic scenes with decisions based on symbolic cues, we find that both multiple demand regions and default mode regions may contribute to the cognitive control of behavior. Rich semantic context enhances representation not just of the context itself, but also of the contents of the decision that it controls. Dependence of a decision on naturalistic context can also reverse the common pattern of multiple demand regions responding more, and default mode regions responding less, to more difficult decisions.

C-type lectin receptors MR and DC-SIGN are involved in recognition of hemocyanins, shaping their immunostimulatory effects on human dendritic cells.

Hemocyanins are used as immunomodulators in clinical applications because they induce a strong Th1-biased cell-mediated immunity, which has beneficial effects. They are multiligand glycosylated molecules with abundant and complex mannose-rich structures. It remains unclear whether these structures influence hemocyanin-induced immunostimulatory processes in human APCs. We have previously shown that hemocyanin glycans from Concholepas concholepas (CCH), Fissurella latimarginata (FLH), and Megathura crenulata (KLH), participate in their immune recognition and immunogenicity in mice, interacting with murine C-type lectin receptors (CLRs). Here, we studied the interactions of these hemocyanins with two major mannose-binding CLRs on monocyte-derived human DCs: MR (mannose receptor) and DC-SIGN (DC-specific ICAM-3-grabbing nonintegrin). Diverse analyses showed that hemocyanins are internalized by a mannose-sensitive mechanism. This process was calcium dependent. Moreover, hemocyanins colocalized with MR and DC-SIGN, and were partly internalized through clathrin-mediated endocytosis. The hemocyanin-mediated proinflammatory cytokine response was impaired when using deglycosylated FLH and KLH compared to CCH. We further showed that hemocyanins bind to human MR and DC-SIGN in a carbohydrate-dependent manner with affinity constants in the physiological concentration range. Overall, we showed that these three clinically valuable hemocyanins interact with human mannose-sensitive CLRs, initiating an immune response and promoting a Th1 cell-driving potential.

Hierarchical Representation of Multistep Tasks in Multiple-Demand and Default Mode Networks.

Task episodes consist of sequences of steps that are performed to achieve a goal. We used fMRI to examine neural representation of task identity, component items, and sequential position, focusing on two major cortical systems-the multiple-demand (MD) and default mode networks (DMN). Human participants (20 males, 22 females) learned six tasks each consisting of four steps. Inside the scanner, participants were cued which task to perform and then sequentially identified the target item of each step in the correct order. Univariate time course analyses indicated that intra-episode progress was tracked by a tonically increasing global response, plus an increasing phasic step response specific to MD regions. Inter-episode boundaries evoked a widespread response at episode onset, plus a marked offset response specific to DMN regions. Representational similarity analysis (RSA) was used to examine representation of task identity and component steps. Both networks represented the content and position of individual steps, however the DMN preferentially represented task identity while the MD network preferentially represented step-level information. Thus, although both MD and DMN networks are sensitive to step-level and episode-level information in the context of hierarchical task performance, they exhibit dissociable profiles in terms of both temporal dynamics and representational content. The results suggest collaboration of multiple brain regions in control of multistep behavior, with MD regions particularly involved in processing the detail of individual steps, and DMN adding representation of broad task context.SIGNIFICANCE STATEMENT Achieving one's goals requires knowing what to do and when. Tasks are typically hierarchical, with smaller steps nested within overarching goals. For effective, flexible behavior, the brain must represent both levels. We contrast response time courses and information content of two major cortical systems-the multiple-demand (MD) and default mode networks (DMN)-during multistep task episodes. Both networks are sensitive to step-level and episode-level information, but with dissociable profiles. Intra-episode progress is tracked by tonically increasing global responses, plus MD-specific increasing phasic step responses. Inter-episode boundaries evoke widespread responses at episode onset, plus DMN-specific offset responses. Both networks represent content and position of individual steps; however, the DMN and MD networks favor task identity and step-level information, respectively.

Corticocortical and Thalamocortical Changes in Functional Connectivity and White Matter Structural Integrity after Reward-Guided Learning of Visuospatial Discriminations in Rhesus Monkeys.

The frontal cortex and temporal lobes together regulate complex learning and memory capabilities. Here, we collected resting-state functional and diffusion-weighted MRI data before and after male rhesus macaque monkeys received extensive training to learn novel visuospatial discriminations (reward-guided learning). We found functional connectivity changes in orbitofrontal, ventromedial prefrontal, inferotemporal, entorhinal, retrosplenial, and anterior cingulate cortices, the subicular complex, and the dorsal, medial thalamus. These corticocortical and thalamocortical changes in functional connectivity were accompanied by related white matter structural alterations in the uncinate fasciculus, fornix, and ventral prefrontal tract: tracts that connect (sub)cortical networks and are implicated in learning and memory processes in monkeys and humans. After the well-trained monkeys received fornix transection, they were impaired in learning new visuospatial discriminations. In addition, the functional connectivity profile that was observed after the training was altered. These changes were accompanied by white matter changes in the ventral prefrontal tract, although the integrity of the uncinate fasciculus remained unchanged. Our experiments highlight the importance of different communication relayed among corticocortical and thalamocortical circuitry for the ability to learn new visuospatial associations (learning-to-learn) and to make reward-guided decisions.SIGNIFICANCE STATEMENT Frontal neural networks and the temporal lobes contribute to reward-guided learning in mammals. Here, we provide novel insight by showing that specific corticocortical and thalamocortical functional connectivity is altered after rhesus monkeys received extensive training to learn novel visuospatial discriminations. Contiguous white matter fiber pathways linking these gray matter structures, namely, the uncinate fasciculus, fornix, and ventral prefrontal tract, showed structural changes after completing training in the visuospatial task. Additionally, different patterns of functional and structural connectivity are reported after removal of subcortical connections within the extended hippocampal system, via fornix transection. These results highlight the importance of both corticocortical and thalamocortical interactions in reward-guided learning in the normal brain and identify brain structures important for memory capabilities after injury.

The Functional Convergence and Heterogeneity of Social, Episodic, and Self-Referential Thought in the Default Mode Network.

The default mode network (DMN) is engaged in a variety of cognitive settings, including social, semantic, temporal, spatial, and self-related tasks. Andrews-Hanna et al. (2010; Andrews-Hanna 2012) proposed that the DMN consists of three distinct functional-anatomical subsystems-a dorsal medial prefrontal cortex (dMPFC) subsystem that supports social cognition; a medial temporal lobe (MTL) subsystem that contributes to memory-based scene construction; and a set of midline core hubs that are especially involved in processing self-referential information. We examined activity in the DMN subsystems during six different tasks: 1) theory of mind, 2) moral dilemmas, 3) autobiographical memory, 4) spatial navigation, 5) self/other adjective judgment, and 6) a rest condition. At a broad level, we observed similar whole-brain activity maps for the six contrasts, and some response to every contrast in each of the three subsystems. In more detail, both univariate analysis and multivariate activity patterns showed partial functional separation, especially between dMPFC and MTL subsystems, though with less support for common activity across the midline core. Integrating social, spatial, self-related, and other aspects of a cognitive situation or episode, multiple components of the DMN may work closely together to provide the broad context for current mental activity.

Structure of the C1r-C1s interaction of the C1 complex of complement activation.

The multiprotein complex C1 initiates the classical pathway of complement activation on binding to antibody-antigen complexes, pathogen surfaces, apoptotic cells, and polyanionic structures. It is formed from the recognition subcomponent C1q and a tetramer of proteases C1r2C1s2 as a Ca2+-dependent complex. Here we have determined the structure of a complex between the CUB1-EGF-CUB2 fragments of C1r and C1s to reveal the C1r-C1s interaction that forms the core of C1. Both fragments are L-shaped and interlock to form a compact antiparallel heterodimer with a Ca2+ from each subcomponent at the interface. Contacts, involving all three domains of each protease, are more extensive than those of C1r or C1s homodimers, explaining why heterocomplexes form preferentially. The available structural and biophysical data support a model of C1r2C1s2 in which two C1r-C1s dimers are linked via the catalytic domains of C1r. They are incompatible with a recent model in which the N-terminal domains of C1r and C1s form a fixed tetramer. On binding to C1q, the proteases become more compact, with the C1r-C1s dimers at the center and the six collagenous stems of C1q arranged around the perimeter. Activation is likely driven by separation of the C1r-C1s dimer pairs when C1q binds to a surface. Considerable flexibility in C1s likely facilitates C1 complex formation, activation of C1s by C1r, and binding and activation of downstream substrates C4 and C4b-bound C2 to initiate the reaction cascade.

Purification of a heterodimeric Reelin construct to investigate binding stoichiometry.

Reelin is a secreted glycoprotein that is integral in neocortex development and synaptic function. Reelin exists as a homodimer with two chains linked by a disulfide bond at cysteine 2101, a feature that is vital to the protein's function. This is highlighted by the fact that only dimeric Reelin can elicit efficient, canonical signaling, even though a mutated (C2101A) monomeric construct of Reelin retains the capacity to bind to its receptors. Receptor clustering has been shown to be important in the signaling pathway, however direct evidence regarding the stoichiometry of Reelin-receptor binding interaction is lacking. Here we describe the construction and purification of a heterodimeric Reelin construct to investigate the stoichiometry of Reelin-receptor binding and how it affects Reelin pathway signaling. We have devised different strategies and have finalized a protocol to produce a heterodimer of Reelin's central fragment using differential tagging and tandem affinity chromatography, such that chain A is wild type in amino acid sequence whereas chain B includes a receptor binding site mutation (K2467A). We also validate that the heterodimer is capable of binding to the extracellular domain of one of Reelin's known receptors, calculating the KD of the interaction. This heterodimeric construct will enable us to understand in greater detail the mechanism by which Reelin interacts with its known receptors and initiates pathway signaling.

C3d-positive donor-specific antibodies have a role in pretransplant risk stratification of cross-match-positive HLA-incompatible renal transplantation: United Kingdom multicentre study.

Anti-HLA-antibody characteristics aid to risk-stratify patients and improve long-term renal graft outcomes. Complement activation by donor-specific antibody (DSA) is an important characteristic that may determine renal allograft outcome. There is heterogeneity in graft outcomes within the moderate to high immunological risk cases (cross-match-positive). We explored the role of C3d-positive DSAs in sub-stratification of cross-match-positive cases and relate to the graft outcomes. We investigated 139 cross-match-positive living-donor renal transplant recipients from four transplant centres in the United Kingdom. C3d assay was performed on serum samples obtained at pretreatment (predesensitization) and Day 14 post-transplant. C3d-positive DSAs were found in 52 (37%) patients at pretreatment and in 37 (27%) patients at Day 14 post-transplant. Median follow-up of patients was 48 months (IQR 20.47-77.57). In the multivariable analysis, pretreatment C3d-positive DSA was independently associated with reduced overall graft survival, the hazard ratio of 3.29 (95% CI 1.37-7.86). The relative risk of death-censored five-year graft failure was 2.83 (95% CI 1.56-5.13). Patients with both pretreatment and Day 14 C3d-positive DSAs had the worst five-year graft survival at 45.5% compared with 87.2% in both pretreatment and Day 14 C3d-negative DSA patients with the relative risk of death-censored five-year graft failure was 4.26 (95% CI 1.79, 10.09). In this multicentre study, we have demonstrated for the first time the utility of C3d analysis as a distinctive biomarker to sub-stratify the risk of poor graft outcome in cross-match-positive living-donor renal transplantation.

Complexity and compositionality in fluid intelligence.

Compositionality, or the ability to build complex cognitive structures from simple parts, is fundamental to the power of the human mind. Here we relate this principle to the psychometric concept of fluid intelligence, traditionally measured with tests of complex reasoning. Following the principle of compositionality, we propose that the critical function in fluid intelligence is splitting a complex whole into simple, separately attended parts. To test this proposal, we modify traditional matrix reasoning problems to minimize requirements on information integration, working memory, and processing speed, creating problems that are trivial once effectively divided into parts. Performance remains poor in participants with low fluid intelligence, but is radically improved by problem layout that aids cognitive segmentation. In line with the principle of compositionality, we suggest that effective cognitive segmentation is important in all organized behavior, explaining the broad role of fluid intelligence in successful cognition.

Azimuthal decomposition of the radiated noise from supersonic shock-containing jets.

Acoustic measurements of unheated supersonic underexpanded jets with ideally expanded Mach numbers of 1.14, 1.38, and 1.50 are presented. Of the three components of supersonic jet noise, the focus is on the broadband shock-associated noise (BBSAN) component. Motivated by the modelling of BBSAN using the wavepacket framework, a traversable microphone ring is used to decompose the acoustic pressure into azimuthal Fourier modes. Unlike noise radiated downstream, BBSAN is dominated by azimuthal modes 1-3, which are approximately 3-4 dB/St stronger than the axisymmetric component. Crucially, the relative contribution of successive modes to BBSAN is sensitive to the observer angle and jet operating condition. Four azimuthal modes are necessary to reconstruct the total BBSAN signal to within 1 dB/St accuracy for the conditions presented here. The analysis suggests, however, that the number of modes required to maintain this accuracy increases as the peak frequency shifts upward. The results demonstrate the need to carefully consider the azimuthal content of BBSAN when comparing acoustic measurements to predictions made by jet noise models built on instability theory.

Progressive Recruitment of the Frontoparietal Multiple-demand System with Increased Task Complexity, Time Pressure, and Reward.

A distributed, frontoparietal "multiple-demand" (MD) network is involved in tasks of many different kinds. Integrated activity across this network may be needed to bind together the multiple features of a mental control program (Duncan, 2013). Previous data suggest that, especially with low cognitive load, there may be some differentiation between MD regions (e.g., anterior vs. posterior regions of lateral frontal cortex), but with increasing load, there is progressive recruitment of the entire network. Differentiation may reflect preferential access to different task features, whereas co-recruitment may reflect information exchange and integration. To examine these patterns, we used manipulations of complexity, time pressure, and reward while participants solved a spatial maze. Complexity was manipulated by combining two simple tasks. Time pressure was added by fading away the maze during route planning, and on some of these trials, there was the further possibility of a substantial reward. Simple tasks evoked activity only in posterior MD regions, including posterior lateral frontal cortex, pre-supplementary motor area/anterior cingulate, and intraparietal sulcus. With increasing complexity, time pressure, and reward, increases in activity were broadly distributed across the MD network, though with quantitative variations. Across the MD network, the results show a degree of functional differentiation, especially at low load, but strong co-recruitment with increased challenge or incentive.

The effect of rule retrieval on activity in the default mode network.

The default mode network (DMN) is often associated with internally-directed cognition, distinct from the constraints of the external environment. However, a recent finding is that the DMN shows strong activation after large task switches during a demanding externally-directed task (Crittenden et al., 2015; Smith et al., 2018). Following other proposals, we have suggested that the DMN encodes cognitive or environmental context, and that context representations are momentarily strengthened during large cognitive switches, perhaps so that new activity can be checked against current environmental constraints. An alternative account, consistent with the role of the DMN in episodic memory, might be that switches to a substantially new task increase demands on rule retrieval. To test this alternative, we directly manipulated rule retrieval demands. Contrary to the retrieval account, increased retrieval demand led to reduced DMN activity, accompanied by increased activation in prefrontal and lateral parietal cognitive control areas. Unlike episodic retrieval, with its rich contextual representations, rule retrieval does not drive DMN activity. Accordingly, it cannot explain increased DMN activity during large cognitive switches.

The time-course of component processes of selective attention.

Attentional selection shapes human perception, enhancing relevant information, according to behavioral goals. While many studies have investigated individual neural signatures of attention, here we used multivariate decoding of electrophysiological brain responses (MEG/EEG) to track and compare multiple component processes of selective attention. Auditory cues instructed participants to select a particular visual target, embedded within a subsequent stream of displays. Combining single and multi-item displays with different types of distractors allowed multiple aspects of information content to be decoded, distinguishing distinct components of attention, as the selection process evolved. Although the task required comparison of items to an attentional "template" held in memory, signals consistent with such a template were largely undetectable throughout the preparatory period but re-emerged after presentation of a non-target choice display. Choice displays evoked strong neural representation of multiple target features, evolving over different timescales. We quantified five distinct processing operations with different time-courses. First, visual properties of the stimulus were strongly represented. Second, the candidate target was rapidly identified and localized in multi-item displays, providing the earliest evidence of modulation by behavioral relevance. Third, the identity of the target continued to be enhanced, relative to distractors. Fourth, only later was the behavioral significance of the target explicitly represented in single-item displays. Finally, if the target was not identified and search was to be resumed, then an attentional template was weakly reactivated. The observation that an item's behavioral relevance directs attention in multi-item displays prior to explicit representation of target/non-target status in single-item displays is consistent with two-stage models of attention.

Role of the Default Mode Network in Cognitive Transitions.

A frequently repeated finding is that the default mode network (DMN) shows activation decreases during externally focused tasks. This finding has led to an emphasis in DMN research on internally focused self-relevant thought processes. A recent study, in contrast, implicates the DMN in substantial externally focused task switches. Using functional magnetic resonance imaging, we scanned 24 participants performing a task switch experiment. Whilst replicating previous DMN task switch effects, we also found large DMN increases for brief rests as well as task restarts after rest. Our findings are difficult to explain using theories strictly linked to internal or self-directed cognition. In line with principal results from the literature, we suggest that the DMN encodes scene, episode or context, by integrating spatial, self-referential, and temporal information. Context representations are strong at rest, but rereference to context also occurs at major cognitive transitions.