Memory failure predicted by attention lapsing and media multitasking.

With the explosion of digital media and technologies, scholars, educators and the public have become increasingly vocal about the role that an 'attention economy' has in our lives1. The rise of the current digital culture coincides with longstanding scientific questions about why humans sometimes remember and sometimes forget, and why some individuals remember better than others2-6. Here we examine whether spontaneous attention lapses-in the moment7-12, across individuals13-15 and as a function of everyday media multitasking16-19-negatively correlate with remembering. Electroencephalography and pupillometry measures of attention20,21 were recorded as eighty young adults (mean age, 21.7 years) performed a goal-directed episodic encoding and retrieval task22. Trait-level sustained attention was further quantified using task-based23 and questionnaire measures24,25. Using trial-to-trial retrieval data, we show that tonic lapses in attention in the moment before remembering, assayed by posterior alpha power and pupil diameter, were correlated with reductions in neural signals of goal coding and memory, along with behavioural forgetting. Independent measures of trait-level attention lapsing mediated the relationship between neural assays of lapsing and memory performance, and between media multitasking and memory. Attention lapses partially account for why we remember or forget in the moment, and why some individuals remember better than others. Heavier media multitasking is associated with a propensity to have attention lapses and forget.

Nickel oxide nanoparticles: A new generation nanoparticles to combat bacteria Xanthomonas oryzae pv. oryzae and enhance rice plant growth.

Recently, nanotechnology is among the most promising technologies used in all areas of research. The production of metal nanoparticles using plant parts has received significant attention for its environmental friendliness and effectiveness. Therefore, we investigated the possible applications of biological synthesized nickel oxide nanoparticles (NiONPs). In this study, NiONPs were synthesized through biological method using an aqueous extract of saffron stigmas (Crocus sativus L). The structure, morphology, purity, and physicochemical properties of the obtained NPs were confirmed through Scanning/Transmission Electron Microscopy attached with Energy Dispersive Spectrum, X-ray Diffraction, and Fourier transform infrared. The spherically shaped NiONPs were found by Debye Scherer's formula to have a mean dimension of 41.19 nm. The application of NiONPs in vitro at 50, 100, and 200 µg/mL, respectively, produced a clear region of 2.0, 2.2, and 2.5 cm. Treatment of Xoo cell with NiONPs reduced the growth and biofilm formation, respectively, by 88.68% and 83.69% at 200 µg/mL. Adding 200 µg/mL NiONPs into Xoo cells produced a significant amount of ROS in comparison with the control. Bacterial apoptosis increased dramatically from 1.05% (control) to 99.80% (200 µg/mL NiONPs). When compared to the control, rice plants treated with 200 µg/mL NiONPs significantly improved growth characteristics and biomass. Interestingly, the proportion of diseased leaf area in infected plants with Xoo treated with NiONPs reduced to 22% from 74% in diseased plants. Taken together, NiONPs demonstrates its effectiveness as a promising tool as a nano-bactericide in managing bacterial infection caused by Xoo.

Encoding contexts are incidentally reinstated during competitive retrieval and track the temporal dynamics of memory interference.

The ability to remember an episode from our past is often hindered by competition from similar events. For example, if we want to remember the article a colleague recommended during the last lab meeting, we may need to resolve interference from other article recommendations from the same colleague. This study investigates if the contextual features specifying the encoding episodes are incidentally reinstated during competitive memory retrieval. Competition between memories was created through the AB/AC interference paradigm. Individual word-pairs were presented embedded in a slowly drifting real-word-like context. Multivariate pattern analysis (MVPA) of high temporal-resolution electroencephalographic (EEG) data was used to investigate context reactivation during memory retrieval. Behaviorally, we observed proactive (but not retroactive) interference; that is, performance for AC competitive retrieval was worse compared with a control DE noncompetitive retrieval, whereas AB retrieval did not suffer from competition. Neurally, proactive interference was accompanied by an early reinstatement of the competitor context and interference resolution was associated with the ensuing reinstatement of the target context. Together, these findings provide novel evidence showing that the encoding contexts of competing discrete events are incidentally reinstated during competitive retrieval and that such reinstatement tracks retrieval competition and subsequent interference resolution.

Role of Sp1 in atherosclerosis.

Specificity protein (Sp) is a famous family of transcription factors including Sp1, Sp2 and Sp3. Sp1 is the first one of Sp family proteins to be characterized and cloned in mammalian. It has been proposed that Sp1 acts as a modulator of the expression of target gene through interacting with a series of proteins, especially with transcriptional factors, and thereby contributes to the regulation of diverse biological processes. Notably, growing evidence indicates that Sp1 is involved in the main events in the development of atherosclerosis (AS), such as inflammation, lipid metabolism, plaque stability, vascular smooth muscle cells (VSMCs) proliferation and endothelial dysfunction. This review is designed to provide useful clues to further understanding roles of Sp1 in the pathogenesis of AS, and may be helpful for the design of novel efficacious therapeutics agents targeting Sp1.

Flexible top-down control in the interaction between working memory and perception.

Successful goal-directed behavior often requires continuous sensory processing while simultaneously maintaining task-related information in working memory (WM). Although WM and perception are known to interact, little is known about how their interactions are controlled. Here, we tested the hypothesis that WM perception interactions engage two distinct modes of control - proactive and reactive - in a manner similar to classic conflict-adaptation tasks (e.g. Stroop, flanker, and Simon). Participants performed a delayed recall-of-orientation WM task, plus a standalone visual discrimination-of-orientation task the occurred during the delay period, and with the congruity in orientation between the tasks manipulated. Proactive control was seen in the sensitivity of task performance to the previous trial's congruity (i.e. a Gratton effect). Reactive control was observed in a repulsive serial-dependence produced by incongruent discriminanda. Quantitatively, these effects were explained by parameters from a reinforcement learning-based model that tracks trial-to-trial fluctuations in control demand: reactive control by a phasic control prediction error (control PE), and proactive control by a tonic level of predicted conflict updated each trial by the control PE. Thus, WM-perception interactions may be controlled by the same mechanisms that govern conflict in other domains of cognition, such as response selection.

Temporal Dynamics of Memory-guided Cognitive Control and Generalization of Control via Overlapping Associative Memories.

Goal-directed behavior can benefit from proactive adjustments of cognitive control that occur in anticipation of forthcoming cognitive control demands (CCD). Predictions of forthcoming CCD are thought to depend on learning and memory in two ways: First, through direct experience, associative encoding may link previously experienced CCD to its triggering item, such that subsequent encounters with the item serve to cue retrieval of (i.e., predict) the associated CCD. Second, in the absence of direct experience, pattern completion and mnemonic integration mechanisms may allow CCD to be generalized from its associated item to other items related in memory. While extant behavioral evidence documents both types of CCD prediction, the neurocognitive mechanisms giving rise to these predictions remain largely unexplored. Here, we tested two hypotheses: (1) memory-guided predictions about CCD precede control adjustments due to the actual CCD required; and (2) generalization of CCD can be accomplished through integration mechanisms that link partially overlapping CCD-item and item-item associations in memory. Supporting these hypotheses, the temporal dynamics of theta and alpha power in human electroencephalography data (n = 43, 26 females) revealed that an associative CCD effect emerges earlier than interaction effects involving actual CCD. Furthermore, generalization of CCD from one item (X) to another item (Y) was predicted by a decrease in alpha power following the presentation of the X-Y pair. These findings advance understanding of the mechanisms underlying memory-guided adjustments of cognitive control.SIGNIFICANCE STATEMENT Cognitive control adaptively regulates information processing to align with task goals. Experience-based expectations enable adjustments of control, leading to improved performance when expectations match the actual control demand required. Using EEG, we demonstrate that memory for past cognitive control demand proactively guides the allocation of cognitive control, preceding adjustments of control triggered by the demands of the present environment. Furthermore, we demonstrate that learned cognitive control demands can be generalized through mnemonic integration processes, enabling the spread of expectations about cognitive control demands to items associated in memory. We reveal that this generalization is linked to decreased alpha oscillation in medial frontal channels. Collectively, these findings provide new insights into how memory-control interactions facilitate goal-directed behavior.

Neural Mechanisms of Strategic Adaptation in Attentional Flexibility.

Individuals are able to adjust their readiness to shift spatial attention, referred to as "attentional flexibility," according to the changing demands of the environment, but the neural mechanisms underlying learned adjustments in flexibility are unknown. In the current study, we used fMRI to identify the brain structures responsible for learning shift likelihood. Participants were cued to covertly hold or shift attention among continuous streams of alphanumeric characters and to indicate the parity of target stimuli. Unbeknown to the participants, the stream locations were predictive of the likelihood of having to shift (or hold) attention. Participants adapted their attentional flexibility according to contextual demands, such that the RT cost associated with shifting attention was smallest when shift cues were most likely. Learning model-derived shift prediction error scaled positively with activity within dorsal and ventral frontoparietal regions, documenting that these regions track and update shift likelihood. A complementary inverted encoding model analysis revealed that the pretrial difference in attentional selection strength between to-be-attended and to-be-ignored locations did not change with increasing shift likelihood. The behavioral improvement associated with learned flexibility may primarily arise from a speeding of the shift process rather than from preparatory broadening of attentional selection.

Noise Modeling and Simulation of Giant Magnetic Impedance (GMI) Magnetic Sensor.

The detection resolution of a giant magneto-impedance (GMI) sensor is mainly limited by its equivalent input magnetic noise. The noise characteristics of a GMI sensor are evaluated by noise modeling and simulation, which can further optimize the circuit design. This paper first analyzes the noise source of the GMI sensor. It discusses the noise model of the circuit, the output sensitivity model and the modeling process of equivalent input magnetic noise. The noise characteristics of three modules that have the greatest impact on the output noise are then simulated. Finally, the simulation results are verified by experiments. By comparing the simulated noise spectrum curve and the experimental noise spectrum curve, it is demonstrated that the preamplifier and the multiplier contribute the most to the output white noise, and the low-pass filter plays a major role in the output 1/f noise. These modules should be given priority in the optimization of the noise of the conditioning circuit. The above results provide technical support for the practical application of low-noise GMI magnetometers.

Causal Evidence for Learning-Dependent Frontal Lobe Contributions to Cognitive Control.

The lateral prefrontal cortex (LPFC) plays a central role in the prioritization of sensory input based on task relevance. Such top-down control of perception is of fundamental importance in goal-directed behavior, but can also be costly when deployed excessively, necessitating a mechanism that regulates control engagement to align it with changing environmental demands. We have recently introduced the "flexible control model" (FCM), which explains this regulation as resulting from a self-adjusting reinforcement-learning mechanism that infers latent statistical structure in dynamic task environments to predict forthcoming states. From this perspective, LPFC-based control is engaged as a function of anticipated cognitive demand, a notion for which we previously obtained correlative neuroimaging evidence. Here, we put this hypothesis to a rigorous, causal test by combining the FCM with a transcranial magnetic stimulation (TMS) intervention that transiently perturbed the LPFC. Human participants (male and female) completed a nonstationary version of the Stroop task with dynamically changing probabilities of conflict between task-relevant and task-irrelevant stimulus features. TMS was given on each trial before stimulus onset either over the LPFC or over a control site. In the control condition, we observed adaptive performance fluctuations consistent with demand predictions that were inferred from recent and remote trial history and effectively captured by our model. Critically, TMS over the LPFC eliminated these fluctuations while leaving basic cognitive and motor functions intact. These results provide causal evidence for a learning-based account of cognitive control and delineate the nature of the signals that regulate top-down biases over stimulus processing.SIGNIFICANCE STATEMENT A core function of the human prefrontal cortex is to control the signal flow in sensory brain regions to prioritize processing of task-relevant information. Abundant work suggests that such control is flexibly recruited to accommodate dynamically changing environmental demands, yet the nature of the signals that serve to engage control remains unknown. Here, we combined computational modeling with noninvasive brain stimulation to show that changes in control engagement are captured by a self-adjusting reinforcement-learning mechanism that tracks changing environmental statistics to predict forthcoming processing demands and that transient perturbation of the prefrontal cortex abolishes these adjustments. These findings delineate the learning signals that underpin adaptive engagement of prefrontal control functions and provide causal evidence for their relevance in behavioral control.

The Caudate Nucleus Mediates Learning of Stimulus-Control State Associations.

A longstanding dichotomy in cognitive psychology and neuroscience pits controlled, top-down driven behavior against associative, bottom-up driven behavior, where cognitive control processes allow us to override well-learned stimulus-response (S-R) associations. By contrast, some previous studies have raised the intriguing possibility of an integration between associative and controlled processing in the form of stimulus-control state (S-C) associations, the learned linkage of specific stimuli to particular control states, such as high attentional selectivity. The neural machinery mediating S-C learning remains poorly understood, however. Here, we combined human functional magnetic resonance imaging (fMRI) with a previously developed Stroop protocol that allowed us to dissociate reductions in Stroop interference based on S-R learning from those based on S-C learning. We modeled subjects' acquisition of S-C and S-R associations using an associative learning model and then used trial-by-trial S-C and S-R prediction error (PE) estimates in model-based behavioral and fMRI analyses. We found that PE estimates derived from S-C and S-R associations accounted for the reductions in behavioral Stroop interference effects in the S-C and S-R learning conditions, respectively. Moreover, model-based fMRI analyses identified the caudate nucleus as the key structure involved in selectively updating stimulus-control state associations. Complementary analyses also revealed a greater reliance on parietal cortex when using the learned S-R versus S-C associations to minimize Stroop interference. These results support the emerging view that generalizable control states can become associated with specific bottom-up cues, and they place the caudate nucleus of the dorsal striatum at the center of the neural stimulus-control learning machinery. SIGNIFICANCE STATEMENT: Previous behavioral studies have demonstrated that control states, for instance, heightened attentional selectivity, can become directly associated with, and subsequently retrieved by, particular stimuli, thus breaking down the traditional dichotomy between top-down and bottom-up driven behavior. However, the neural mechanisms underlying this type of stimulus-control learning remain poorly understood. We therefore combined noninvasive human neuroimaging with a task that allowed us to dissociate the acquisition of stimulus-control associations from that of stimulus-response associations. The results revealed the caudate nucleus as the key brain structure involved in selectively driving stimulus-control learning. These data represent the first identification of the neural mechanisms of stimulus-specific control associations, and they significantly extend current conceptions of the type of learning processes mediated by the caudate.

Hierarchically Organized Medial Frontal Cortex-Basal Ganglia Loops Selectively Control Task- and Response-Selection.

Adaptive behavior requires context-sensitive configuration of task-sets that specify time-varying stimulus-response mappings. Intriguingly, response time costs associated with changing task-sets and motor responses are known to be strongly interactive: switch costs at the task level are small in the presence of a response-switch but large when accompanied by a response-repetition, and vice versa for response-switch costs. The reasons behind this well known interdependence between task- and response-level control processes are currently not well understood. Here, we formalized and tested a model assuming a hierarchical organization of superordinate task-set and subordinate response-set selection processes to account for this effect. The model was found to successfully explain the full range of behavioral task- and response-switch costs across first and second order trial transitions. Using functional magnetic resonance imaging (fMRI) in healthy humans, we then characterized the neural circuitry mediating these effects. We found that presupplementary motor area (preSMA) activity tracked task-set control costs, SMA activity tracked response-set control costs, and basal ganglia (BG) activity mirrored the interaction between task- and response-set regulation processes that characterized participants' response times. A subsequent fMRI-guided transcranial magnetic stimulation experiment confirmed dissociable roles of the preSMA and SMA in determining response costs. Together, these data provide evidence for a hierarchical organization of posterior medial frontal cortex and its interaction with the BG, where a superordinate preSMA-BG loop establishes task-set selection, which imposes a (unidirectional) constraint on a subordinate SMA-BG loop that determines response-selection, resulting in the characteristic interdependence in task- and response-switch costs in behavior.SIGNIFICANCE STATEMENT The ability to use context-sensitive task-sets to guide our responses is central to human adaptive behavior. Task and response selection are strongly interactive: it is more difficult to repeat a response in the context of a changing task-set, and vice versa. However, the neurocognitive architecture giving rise to this interdependence is currently not understood. Here we use modeling, neuroimaging, and noninvasive neurostimulation to show that this phenomenon derives from a hierarchical organization of posterior medial frontal cortex and its interaction with the basal ganglia, where a more anterior corticostriatal loop establishes task-set selection, which constrains a more posterior loop responsible for response-selection. These data provide a neural explanation for a key behavioral signature of human cognitive control.

Visual Prediction Error Spreads Across Object Features in Human Visual Cortex.

Visual cognition is thought to rely heavily on contextual expectations. Accordingly, previous studies have revealed distinct neural signatures for expected versus unexpected stimuli in visual cortex. However, it is presently unknown how the brain combines multiple concurrent stimulus expectations such as those we have for different features of a familiar object. To understand how an unexpected object feature affects the simultaneous processing of other expected feature(s), we combined human fMRI with a task that independently manipulated expectations for color and motion features of moving-dot stimuli. Behavioral data and neural signals from visual cortex were then interrogated to adjudicate between three possible ways in which prediction error (surprise) in the processing of one feature might affect the concurrent processing of another, expected feature: (1) feature processing may be independent; (2) surprise might "spread" from the unexpected to the expected feature, rendering the entire object unexpected; or (3) pairing a surprising feature with an expected feature might promote the inference that the two features are not in fact part of the same object. To formalize these rival hypotheses, we implemented them in a simple computational model of multifeature expectations. Across a range of analyses, behavior and visual neural signals consistently supported a model that assumes a mixing of prediction error signals across features: surprise in one object feature spreads to its other feature(s), thus rendering the entire object unexpected. These results reveal neurocomputational principles of multifeature expectations and indicate that objects are the unit of selection for predictive vision. SIGNIFICANCE STATEMENT: We address a key question in predictive visual cognition: how does the brain combine multiple concurrent expectations for different features of a single object such as its color and motion trajectory? By combining a behavioral protocol that independently varies expectation of (and attention to) multiple object features with computational modeling and fMRI, we demonstrate that behavior and fMRI activity patterns in visual cortex are best accounted for by a model in which prediction error in one object feature spreads to other object features. These results demonstrate how predictive vision forms object-level expectations out of multiple independent features.

Memory Meets Control in Hippocampal and Striatal Binding of Stimuli, Responses, and Attentional Control States.

The human brain encodes experience in an integrative fashion by binding together the various features of an event (i.e., stimuli and responses) into memory "event files." A subsequent reoccurrence of an event feature can then cue the retrieval of the memory file to "prime" cognition and action. Intriguingly, recent behavioral studies indicate that, in addition to linking concrete stimulus and response features, event coding may also incorporate more abstract, "internal" event features such as attentional control states. In the present study, we used fMRI in healthy human volunteers to determine the neural mechanisms supporting this type of holistic event binding. Specifically, we combined fMRI with a task protocol that dissociated the expression of event feature-binding effects pertaining to concrete stimulus and response features, stimulus categories, and attentional control demands. Using multivariate neural pattern classification, we show that the hippocampus and putamen integrate event attributes across all of these levels in conjunction with other regions representing concrete-feature-selective (primarily visual cortex), category-selective (posterior frontal cortex), and control demand-selective (insula, caudate, anterior cingulate, and parietal cortex) event information. Together, these results suggest that the hippocampus and putamen are involved in binding together holistic event memories that link physical stimulus and response characteristics with internal representations of stimulus categories and attentional control states. These bindings then presumably afford shortcuts to adaptive information processing and response selection in the face of recurring events. SIGNIFICANCE STATEMENT: Memory binds together the different features of our experience, such as an observed stimulus and concurrent motor responses, into so-called event files. Recent behavioral studies suggest that the observer's internal attentional state might also become integrated into the event memory. Here, we used fMRI to determine the brain areas responsible for binding together event information pertaining to concrete stimulus and response features, stimulus categories, and internal attentional control states. We found that neural signals in the hippocampus and putamen contained information about all of these event attributes and could predict behavioral priming effects stemming from these features. Therefore, medial temporal lobe and dorsal striatum structures appear to be involved in binding internal control states to event memories.

Using neural pattern classifiers to quantify the modularity of conflict-control mechanisms in the human brain.

Resolving conflicting sensory and motor representations is a core function of cognitive control, but it remains uncertain to what degree control over different sources of conflict is implemented by shared (domain general) or distinct (domain specific) neural resources. Behavioral data suggest conflict-control to be domain specific, but results from neuroimaging studies have been ambivalent. Here, we employed multivoxel pattern analyses that can decode a brain region's informational content, allowing us to distinguish incidental activation overlap from actual shared information processing. We trained independent sets of "searchlight" classifiers on functional magnetic resonance imaging data to decode control processes associated with stimulus-conflict (Stroop task) and ideomotor-conflict (Simon task). Quantifying the proportion of domain-specific searchlights (capable of decoding only one type of conflict) and domain-general searchlights (capable of decoding both conflict types) in each subject, we found both domain-specific and domain-general searchlights, though the former were more common. When mapping anatomical loci of these searchlights across subjects, neural substrates of stimulus- and ideomotor-specific conflict-control were found to be anatomically consistent across subjects, whereas the substrates of domain-general conflict-control were not. Overall, these findings suggest a hybrid neural architecture of conflict-control that entails both modular (domain specific) and global (domain general) components.

Attention sharpens the distinction between expected and unexpected percepts in the visual brain.

Attention, the prioritization of goal-relevant stimuli, and expectation, the modulation of stimulus processing by probabilistic context, represent the two main endogenous determinants of visual cognition. Neural selectivity in visual cortex is enhanced for both attended and expected stimuli, but the functional relationship between these mechanisms is poorly understood. Here, we adjudicated between two current hypotheses of how attention relates to predictive processing, namely, that attention either enhances or filters out perceptual prediction errors (PEs), the PE-promotion model versus the PE-suppression model. We acquired fMRI data from category-selective visual regions while human subjects viewed expected and unexpected stimuli that were either attended or unattended. Then, we trained multivariate neural pattern classifiers to discriminate expected from unexpected stimuli, depending on whether these stimuli had been attended or unattended. If attention promotes PEs, then this should increase the disparity of neural patterns associated with expected and unexpected stimuli, thus enhancing the classifier's ability to distinguish between the two. In contrast, if attention suppresses PEs, then this should reduce the disparity between neural signals for expected and unexpected percepts, thus impairing classifier performance. We demonstrate that attention greatly enhances a neural pattern classifier's ability to discriminate between expected and unexpected stimuli in a region- and stimulus category-specific fashion. These findings are incompatible with the PE-suppression model, but they strongly support the PE-promotion model, whereby attention increases the precision of prediction errors. Our results clarify the relationship between attention and expectation, casting attention as a mechanism for accelerating online error correction in predicting task-relevant visual inputs.

Age of onset of blindness affects brain anatomical networks constructed using diffusion tensor tractography.

Studying blindness with various onset ages may elucidate the ways that unimodal sensory deprivation at different periods of development shape the human brain. In order to determine the effect of the onset age on brain anatomical networks, we extended a previous study of 17 early blind (EB) subjects with an additional 97 subjects with various onset ages. We constructed binary anatomical networks of these subjects and sighted controls (SC) using diffusion tensor tractography and calculated the topological properties of the network. Based on onset age, the subjects were divided into congenitally blind (CB), EB, adolescent-blind (AB), and late-blind (LB) subgroups. The LB subjects demonstrated a greater connectivity density and a higher global efficiency, similar to the SC. The CB and EB subgroups showed large group differences from the other groups in their topological networks, specifically, a reduced connectivity density and a decreased global efficiency compared with the SC, especially in the frontal and occipital cortices. Additionally, significant correlations were found between age of onset and the topological properties of the anatomical network in the blind. Our results suggest that visual experience during an early period of development is critical for establishing an intact efficient anatomical network in the human brain.

Cost-benefit Tradeoff Mediates the Rule- to Memory-based Transition during Practice.

Practice not only improves task performance, but also changes task execution from rule- to memory-based processing by incorporating experiences from practice. We tested the hypothesis that strategy transition in task learning results from a cost-benefit analysis of candidate strategies. Participants learned two task sequences and were then queried the task type at a cued sequence and position. Behavioral improvement with practice can be accounted for by a computational model implementing cost-benefit analysis. Model-guided fMRI analysis shows frontal and parietal activations scaling with the demand of executing rule and memory strategy, respectively. fMRI activation pattern analysis further reveals widespread strategy-specific neural representations when their corresponding strategy is executed. Lastly, strategy transition is related to neural representation change in the dorsolateral prefrontal cortex and pattern separation in the ventromedial prefrontal cortex and the hippocampus. These findings shed light on how practice optimizes task performance by shifting task representations at the strategy level.

Task switch costs scale with dissimilarity between task rules.

Cognitive flexibility enables humans to voluntarily switch tasks. Task switching requires replacing the previously active task representation with a new one, an operation that typically results in a switch cost. Thus, understanding cognitive flexibility requires understanding how tasks are represented in the brain. We hypothesize that task representations are cognitive map-like, such that the magnitude of the difference between task representations reflects their conceptual differences: The greater the distinction between the two task representations, the more updating is required. This hypothesis predicts that switch costs should increase with between task dissimilarity. To test this hypothesis, we use an experimental design that parametrically manipulates the similarity between task rules. We observe that response time scales with the dissimilarity between the task rules. The findings shed light on the organizational principles of task representations and extend the conventional binary task-switch effect (task repeat vs. switch) to a theoretical framework with parametric task switches. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Dorsolateral prefrontal activity supports a cognitive space organization of cognitive control.

Cognitive control resolves conflicts between task-relevant and -irrelevant information to enable goal-directed behavior. As conflicts can arise from different sources (e.g., sensory input, internal representations), how a limited set of cognitive control processes can effectively address diverse conflicts remains a major challenge. Based on the cognitive space theory, different conflicts can be parameterized and represented as distinct points in a (low-dimensional) cognitive space, which can then be resolved by a limited set of cognitive control processes working along the dimensions. It leads to a hypothesis that conflicts similar in their sources are also represented similarly in the cognitive space. We designed a task with five types of conflicts that could be conceptually parameterized. Both human performance and fMRI activity patterns in the right dorsolateral prefrontal cortex support that different types of conflicts are organized based on their similarity, thus suggesting cognitive space as a principle for representing conflicts.

You are reading a book in your local coffeeshop, when your focus gets broken by the couple at the next table, passionately discussing mortgage rates. To minimise this interruption your brain engages in 'cognitive control', resolving conflicts between competing stimuli to prioritise one over another. Having finally regained your focus, another distraction emerges, this time of a different nature. Does your brain use the same mental mechanisms as before, and therefore a common brain circuit? Or does each kind of stimulus require a specific process? Tasks that involve successively presenting different distractors can help explore these questions by testing for a process known as generalization: if the same mental mechanism underpins the resolution of all conflicts, distractors should become easier to ignore after the first trial. Based on this paradigm, Yang et al. recorded brain activity during a modified version of a spatial Stroop-Simon task. Participants were asked to press a left or right button based on whether an arrow was pointing up or down, with both the vertical and horizontal position of the symbol potentially causing interference. For instance, accurate decision-making may be impaired when an arrow 'down' the bottom of the screen is pointing up (Stroop effect); or when participants must press the left button for an arrow shown on their right (Simon effect). Overall, the arrows could appear in 10 possible locations, giving rise to five types of conflicts with a unique blend of Stroop and Simon effects, with different levels of similarity. The results showed that the degree to which conflicts could generalize to each other depended on their similarity: the more similar the conflicts, the easier it was to resolve one after having faced another. This is contrary to previous views suggesting that different conflict types either entirely generalized or could not generalize at all. In addition, the analyses revealed that the neural networks involved in resolving each conflict type were organised in a continuous manner within a region called the prefrontal cortex. This pattern resembles how spatial information is arranged in the brain, prompting Yang et al. to suggest that cognitive control also falls under a set of principles known as cognitive space representations. Overall, the methodology employed in this work could prove useful to researchers from other fields who also investigate whether various stimuli are processed via the same or different neural networks.

The efficacy of wound edge protectors in reducing the post-operative surgical site infections after abdominal surgery: a meta-analysis of randomized clinical studies.

Introduction: Following abdominal surgery, surgical site infections (SSIs) are a common complication. The effectiveness of wound edge protectors in preventing SSI remains uncertain. Aim: To determine the clinical effectiveness of a wound edge protector (WEP) in preventing surgical site infections (SSIs) after abdominal surgery. Material and methods: A systematic search of the Cochrane Library, PubMed, Embase, and Web of Science yielded all relevant articles published through October 2022. The major evidence regarding the efficacy of WEPs in minimizing SSIs in abdominal surgery patients relative to the standard of care was determined by searching the literature. The primary outcome was SSI as clinically defined by CDC. To combine qualitative factors, risk ratios (RRs) were used. Results: WEPs were related to a decreased incidence of SSI overall (RR = 0.75; 95% CI: 0.61-0.91; p = 0.004). WEPs are efficient in lowering the incidence of SSI at various abdominal surgical sites, with RR = 0.67; 95% CI: 0.47-0.96; p = 0.03 for pancreatoduodenectomy, RR = 0.52; 95% CI: 0.31-0.86; p = 0.01 for colorectal surgery, and RR = 0.39; 95% CI: 0.21-0.73; p = 0.003 for abdominal surgery. Moreover, both kinds of WEPs (single-ring and double-ring devices) were successful in lowering the risk of SSIs, with RR = 0.66; 95% CI: 0.47-0.93; p = 0.02 for double-ring devices and RR = 0.76; 95% CI: 0.58-0.98; p = 0.04 for single-ring devices. Conclusions: These findings demonstrate that double- and single-ring wound edge protection devices are effective in preventing surgical site infections following pancreatoduodenectomy, colorectal, and abdominal procedures.