Task Outcomes Research Articles

BigBind: Learning from Nonstructural Data for Structure-Based Virtual Screening.

Deep learning methods that predict protein-ligand binding have recently been used for structure-based virtual screening. Many such models have been trained using protein-ligand complexes with known crystal structures and activities from the PDBBind data set. However, because PDBbind only includes 20K complexes, models typically fail to generalize to new targets, and model performance is on par with models trained with only ligand information. Conversely, the ChEMBL database contains a wealth of chemical activity information but includes no information about binding poses. We introduce BigBind, a data set that maps ChEMBL activity data to proteins from the CrossDocked data set. BigBind comprises 583 K ligand activities and includes 3D structures of the protein binding pockets. Additionally, we augmented the data by adding an equal number of putative inactives for each target. Using this data, we developed Banana (basic neural network for binding affinity), a neural network-based model to classify active from inactive compounds, defined by a 10 μM cutoff. Our model achieved an AUC of 0.72 on BigBind's test set, while a ligand-only model achieved an AUC of 0.59. Furthermore, Banana achieved competitive performance on the LIT-PCBA benchmark (median EF1% 1.81) while running 16,000 times faster than molecular docking with Gnina. We suggest that Banana, as well as other models trained on this data set, will significantly improve the outcomes of prospective virtual screening tasks.

Inhibitory control and its modification in spider phobia - Study protocol for an antisaccade training trial.

Inhibitory control deficits are considered a key pathogenic factor in anxiety disorders. To assess inhibitory control, the antisaccade task is a well-established measure that assesses antisaccade performance via latencies and error rates. The present study follows three aims: (1) to investigate inhibitory control via antisaccade latencies and errors in an antisaccade task, and their associations with multiple measures of fear in patients with spider phobia (SP) versus healthy controls (HC), (2) to investigate the modifiability of antisaccade performance via a fear-specific antisaccade training in patients with SP and HC, and (3) to explore associations between putative training-induced changes in antisaccade performance in SPs and changes in diverse measures of fear. Towards aim 1, we assess antisaccade latencies (primary outcome) and error rates (secondary outcome) in an emotional antisaccade task. Further, the baseline assessment includes assessments of psychophysiological, behavioral, and psychometric indices of fear in patients with SP and HCs. To address aim 2, we compare effects of a fear-specific antisaccade training with effects of a prosaccade training as a control condition. The primary and secondary outcomes are reassessed at a post-1-assessment in both SPs and HCs. Aim 3 employs a cross-over design and is piloted in patients with SP, only. Towards this aim, primary and secondary outcomes, as well as psychophysiological, behavioral, and psychometric measures of fear are reassessed at a post-2-assessment after the second training block. This study aims to better understand inhibitory control processes and their modifiability in spider phobia. If successful, antisaccade training may assist in the treatment of specific phobia by directly targeting the putative underlying inhibitory control deficits. This study has been preregistered with ISRCTN (ID: ISRCTN12918583) on 28th February 2022.

Rethinking Gamification Failure: A Model and Investigation of Gamified System Maladaptive Behaviors

Current studies show gamification, the integrating of game design elements into target systems, enhances user engagement and instrumental task outcomes. Despite its potential for improving behavioral outcomes, gamification can also lead to maladaptive behaviors, behaviors directed at misappropriating gamified systems. We conceptualized gamified system maladaptive behaviors (GSMB), which involve technology and gamified task maladaptations. We developed a model that depicts three drivers of GSMB from design elements, how they fulfill or frustrate psychological innate needs, which in turn drive GSMB, and how GSMB affect task performance. We tested how the three drivers of design elements affect GSMB in Study 1 by empirically examining users of a gamified system, Pocket Points. The results support our conceptualization of GSMB, and design issues as its antecedents. To further unpack this relationship, we then employed a within-subject experiment and a follow-up survey in Study 2. By manipulating the design issues, we found that GSMB adversely affect task performance, because these users may focus too intently on winning the game, at the expense of task performance. By assessing the fulfillment of psychological needs, our findings suggest that design in gamified systems may not uniformly fulfill the satisfaction of psychological needs and consequently triggers GSMB.

Advanced workstations and collaborative robots: exploiting eye-tracking and cardiac activity indices to unveil senior workers' mental workload in assembly tasks.

Introduction: As a result of Industry 5.0's technological advancements, collaborative robots (cobots) have emerged as pivotal enablers for refining manufacturing processes while re-focusing on humans. However, the successful integration of these cutting-edge tools hinges on a better understanding of human factors when interacting with such new technologies, eventually fostering workers' trust and acceptance and promoting low-fatigue work. This study thus delves into the intricate dynamics of human-cobot interactions by adopting a human-centric view. Methods: With this intent, we targeted senior workers, who often contend with diminishing work capabilities, and we explored the nexus between various human factors and task outcomes during a joint assembly operation with a cobot on an ergonomic workstation. Exploiting a dual-task manipulation to increase the task demand, we measured performance, subjective perceptions, eye-tracking indices and cardiac activity during the task. Firstly, we provided an overview of the senior workers' perceptions regarding their shared work with the cobot, by measuring technology acceptance, perceived wellbeing, work experience, and the estimated social impact of this technology in the industrial sector. Secondly, we asked whether the considered human factors varied significantly under dual-tasking, thus responding to a higher mental load while working alongside the cobot. Finally, we explored the predictive power of the collected measurements over the number of errors committed at the work task and the participants' perceived workload. Results: The present findings demonstrated how senior workers exhibited strong acceptance and positive experiences with our advanced workstation and the cobot, even under higher mental strain. Besides, their task performance suffered increased errors and duration during dual-tasking, while the eye behavior partially reflected the increased mental demand. Some interesting outcomes were also gained about the predictive power of some of the collected indices over the number of errors committed at the assembly task, even though the same did not apply to predicting perceived workload levels. Discussion: Overall, the paper discusses possible applications of these results in the 5.0 manufacturing sector, emphasizing the importance of adopting a holistic human-centered approach to understand the human-cobot complex better.

Deep learning-based natural language processing for detecting medical symptoms and histories in emergency patient triage

ObjectiveThe manual recording of electronic health records (EHRs) by clinicians in the emergency department (ED) is time-consuming and challenging. In light of recent advancements in large language models (LLMs) such as GPT and BERT, this study aimed to design and validate LLMs for automatic clinical diagnoses. The models were designed to identify 12 medical symptoms and 2 patient histories from simulated clinician–patient conversations within 6 primary symptom scenarios in emergency triage rooms. Materials and methodWe developed classification models by fine-tuning BERT, a transformer-based pre-trained model. We subsequently analyzed these models using eXplainable artificial intelligence (XAI) and the Shapley additive explanation (SHAP) method. A Turing test was conducted to ascertain the reliability of the XAI results by comparing them to the outcomes of tasks performed and explained by medical workers. An emergency medicine specialist assessed the results of both XAI and the medical workers. ResultsWe fine-tuned four pre-trained LLMs and compared their classification performance. The KLUE-RoBERTa-based model demonstrated the highest performance (F1-score: 0.965, AUROC: 0.893) on human-transcribed script data. The XAI results using SHAP showed an average Jaccard similarity of 0.722 when compared with explanations of medical workers for 15 samples. The Turing test results revealed a small 6% gap, with XAI and medical workers receiving the mean scores of 3.327 and 3.52, respectively. ConclusionThis paper highlights the potential of LLMs for automatic EHR recording in Korean EDs. The KLUE-RoBERTa-based model demonstrated superior classification performance. Furthermore, XAI using SHAP provided reliable explanations for model outputs. The reliability of these explanations was confirmed by a Turing test.

Negative emotion reduces the discriminability ofreward outcomes inthe ventromedial prefrontal cortex.

Reward and emotion are tightly intertwined, so there is a growing interest in mapping their interactions. However, our knowledge of these interactions in the human brain, especially during the consummatory phase of reward is limited. To address this critical gap, we conducted a functional magnetic resonance imaging study to investigate the effects of negative emotion on reward outcome processing. We employed a novel design where emotional valence (negative or neutral) indicated the type of outcome (reward or no-reward) in a choice task. We focused our functional magnetic resonance imaging analysis on the ventro-medial prefrontal cortex (vmPFC), ventral striatum and amygdala, which were frequently implicated in reward outcome processing. In these regions of interest, we performed multi-voxel pattern analysis to specifically probe how negative emotion modulates reward outcome processing. In vmPFC, using decoding analysis, we found evidence consistent with the reduced discriminability of multi-variate activity patterns of reward vs no-reward outcomes when signaled by a negative relative to a neutral image, suggesting an emotional modulation of reward processing along the plausible common value/valence dimension. These findings advance our limited understanding of the basic brain mechanisms underlying the influence of negative emotion on consummatory reward processing, with potential implications for mental disorders, particularly anxiety and depression.

When “good enough” is not good enough: How maximizing benefits financial well‐being

AbstractA maximizing decision‐making style is generally associated with lower individual well‐being. That is, even though maximizers invest more time and resources in finding the best option and achieve better outcomes than satisficers, they are still more dissatisfied with those outcomes. Contrary to this general consensus that maximizing is negatively associated with overall well‐being, across two studies we show that this decision‐making style is actually positively associated with individuals' financial well‐being. We find that measured dispositional maximizing is positively associated with financial well‐being, regardless of whether maximizing is operationalized as having high standards or the tendency to engage in alternative search (Study 1) and replicate this relationship with experimentally induced situational maximizing (Study 2). We identify financial self‐control (both measured as a trait and as the behavioral outcome of an experimental choice task) as a mediator of the aforementioned relationship. Our findings offer guidance to financial service providers and policymakers on how to improve consumers' financial well‐being, such as encouraging consumers to engage in a more meticulous search while evaluating financial products and services (e.g., home loans, retirement plans, investments) to identify the best possible option.

Overconfidence: the roles of gender, public observability and incentives

AbstractIn this project, we manipulate the public observability of forecasts and outcomes of a physical task. We explore how these manipulations affect overconfidence (OC). Participants in the experiment are asked to hold a weight after predicting how long they think they could do it for. Comparing the prediction and outcome times (in seconds) yields a measure of OC. We independently vary two dimensions of public observability (of the outcome and of the prediction). Additionally, we manipulate incentives to come up with an accurate prediction. This design allows us to shed light on the mechanism behind male and female OC. Following the existing literature, we formulate several hypotheses regarding the differences in predictions and outcomes for males and females in the presence of the public observability of predictions and outcomes. Our experimental data do not provide support to most of the hypotheses: in particular, there is no evidence of a gender gap in overconfidence. The most robust finding that emerges from our results is that incentives on making correct predictions increase participants’ forecasts on their own performance (by about 24%) and their actual performance as well, but to a lower extent (by about 8%); in addition, incentives to predict correctly in fact increase error for females (by about 33%).

Different Faces of Medial Beta-Band Activity Reflect Distinct Visuomotor Feedback Signals.

Beta-band (13-35 Hz) modulations following reward, task outcome feedback, and error have been described in cognitive and/or motor adaptation tasks. Observations from different studies are, however, difficult to conciliate. Among the studies that used cognitive response selection tasks, several reported an increase in beta-band activity following reward, whereas others observed increased beta power after negative feedback. Moreover, in motor adaptation tasks, an attenuation of the postmovement beta rebound follows a movement execution error induced by visual or mechanical perturbations. Given that kinematic error typically leads to negative task-outcome feedback (e.g., target missed), one may wonder how contradictory modulations, beta power decrease with movement error versus beta power increase with negative feedback, may coexist. We designed a motor adaptation task in which female and male participants experience varied feedbacks-binary success/failure feedback, kinematic error, and sensory-prediction error-and demonstrate that beta-band modulations in opposite directions coexist at different spatial locations, time windows, and frequency ranges. First, high beta power in the medial frontal cortex showed opposite modulations well separated in time when compared in success and failure trials; that is, power was higher in success trials just after the binary success feedback, whereas it was lower in the postmovement period compared with failure trials. Second, although medial frontal high-beta activity was sensitive to task outcome, low-beta power in the medial parietal cortex was strongly attenuated following movement execution error but was not affected by either the outcome of the task or sensory-prediction error. These findings suggest that medial beta activity in different spatio-temporal-spectral configurations play a multifaceted role in encoding qualitatively distinct feedback signals.SIGNIFICANCE STATEMENT Beta-band activity reflects neural processes well beyond sensorimotor functions, including cognition and motivation. By disentangling alternative spatio-temporal-spectral patterns of possible beta-oscillatory activity, we reconcile a seemingly discrepant literature. First, high-beta power in the medial frontal cortex showed opposite modulations separated in time in success and failure trials; power was higher in success trials just after success feedback and lower in the postmovement period compared with failure trials. Second, although medial frontal high-beta activity was sensitive to task outcome, low-beta power in the medial parietal cortex was strongly attenuated following movement execution error but was not affected by the task outcome or the sensory-prediction error. We propose that medial beta activity reflects distinct feedback signals depending on its anatomic location, time window, and frequency range.

A Review of Emotions in Human-Conversational Agent Interaction

Conversational agents (CA), powered by natural language processing, have become increasingly popular across multiple domains. However, these agents often fail to communicate effectively with users, leading to poor adoption and task outcomes. Emotions are a fundamental aspect of such interactions, influencing use and adoption of digital artifacts. Despite their salience, understanding of the role of emotions in human-CA collaboration remains fragmented. Motivated thus, we review empirical studies on emotions in human-CA interactions. We synthesize the findings from the reviewed studies in terms of antecedents, emotion-related outcomes, and their relationships in the form of a descriptive model. Based on the synthesis, we identify knowledge gaps and propose directions for future research. Our analysis provides insights into the role of emotions in human-CA interactions and contributes to research in this area.

Change in food choice during acute treatment and the effect on longer-term outcome in patients with anorexia nervosa.

Restriction of food intake is a central pathological feature of anorexia nervosa (AN). Maladaptive eating behavior and, specifically, limited intake of calorie-dense foods are resistant to change and contribute to poor long-term outcomes. This study is a preliminary examination of whether change in food choices during inpatient treatment is related to longer-term clinical course. Individuals with AN completed a computerized Food Choice Task at the beginning and end of inpatient treatment to determine changes in high-fat and self-controlled food choices. Linear regression and longitudinal analyses tested whether change in task behavior predicted short-term outcome (body mass index [BMI] at discharge) and longer-term outcome (BMI and eating disorder psychopathology). Among 88 patients with AN, BMI improved significantly with hospital treatment (p < 0.001), but Food Choice Task outcomes did not change significantly. Change in high-fat and self-controlled choices was not associated with BMI at discharge (r = 0.13, p = 0.22 and r = 0.10, p = 0.39, respectively). An increase in the proportion of high-fat foods selected (β = 0.91, p = 0.02) and a decrease in the use of self-control (β = -1.50, p = 0.001) predicted less decline in BMI over 3 years after discharge. Short-term treatment is associated with improvement in BMI but with no significant change, on average, in choices made in a task known to predict actual eating. However, the degree to which individuals increased high-fat choices during treatment and decreased the use of self-control over food choice were associated with reduced weight loss over the following 3 years, underscoring the need to focus on changing eating behavior in treatment of AN.

The electronic nicotine delivery system (ENDS) purchase task: Are results sensitive to price framing?

Extension of the cigarette purchase task (CPT) to electronic nicotine delivery systems (ENDS) is complicated by the heterogeneous nature of this product class, as ambiguity exists regarding the appropriate price-frame (i.e., unit of the product being purchased). We explored correlations between ENDS purchase task (E-CPTs) outcomes featuring two common price-frames: 10 puffs and 1 mL of liquid. Adult exclusive ENDS users (N = 19) and dual users of ENDS and cigarettes (N = 16) completed two own-brand E-CPTs. One E-CPT used "10 puffs" as its price-frame; the other used "1 mL of liquid." Five outcomes were generated for each E-CPT: breakpoint, intensity, Omax, Pmax, and α. Exploratory Factor Analyses (EFA) considered how these outcomes captured latent structures of demand for ENDS. Spearman correlations in E-CPT outcomes assessed within-person variation between price-frames. Analyses also considered whether correlations differed by user group. E-CPT outcomes were highly correlated across price-frames (ρs > 0.57, ps < .001), and EFA revealed little difference in how outcomes from the tasks loaded onto two latent structures of demand ("Persistence" and "Amplitude") reported in the previous literature. The magnitude of correlations for E-CPT outcomes tended to be higher for exclusive ENDS users than for dual users. Participant responses to purchase task outcomes were similar across two E-CPT price-frames. Using "10 puffs" as a price-frame may be a generalizable approach among heterogenous groups of ENDS users, but researchers should consider their target population and structure the E-CPT to reflect participants' knowledge and purchasing behaviors. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations

AI explanations are often mentioned as a way to improve human-AI decision-making, but empirical studies have not found consistent evidence of explanations' effectiveness and, on the contrary, suggest that they can increase overreliance when the AI system is wrong. While many factors may affect reliance on AI support, one important factor is how decision-makers reconcile their own intuition---beliefs or heuristics, based on prior knowledge, experience, or pattern recognition, used to make judgments---with the information provided by the AI system to determine when to override AI predictions. We conduct a think-aloud, mixed-methods study with two explanation types (feature- and example-based) for two prediction tasks to explore how decision-makers' intuition affects their use of AI predictions and explanations, and ultimately their choice of when to rely on AI. Our results identify three types of intuition involved in reasoning about AI predictions and explanations: intuition about the task outcome, features, and AI limitations. Building on these, we summarize three observed pathways for decision-makers to apply their own intuition and override AI predictions. We use these pathways to explain why (1) the feature-based explanations we used did not improve participants' decision outcomes and increased their overreliance on AI, and (2) the example-based explanations we used improved decision-makers' performance over feature-based explanations and helped achieve complementary human-AI performance. Overall, our work identifies directions for further development of AI decision-support systems and explanation methods that help decision-makers effectively apply their intuition to achieve appropriate reliance on AI.

Neurosurveillance in the workplace: do employers have the right to monitor employees' minds?

The use of neurotechnologies for surveillance in the workplace have the potential to impact the entire working population of the world. Currently, with the help of neurodevices, employers could analyze the brain data from employees to assess their cognitive functions (such as mental capacity and efficiency), cognitive patterns (such as response to stress), and even detect neuropathologies. The workers brain data obtained with wearable neurodevices could serve employers for purposes such as promotion, hiring, or dismissal. Neurodevices could also be used as new micromanagement tools, aimed at monitoring employees' attention at work. Additionally, they can be implemented as tools for self-control for workers, as the feedback provided about their current cognitive state can help improve the outcomes of ongoing tasks and ensure safety. Recent studies have shown that while employees may recognize the potential benefits of using such technology for self-monitoring purposes, they have a negative perception toward its implementation in the workplace. Surprisingly, only a few scientific papers specifically address the issues of neurosurveillance in the workplace, while international frameworks have not yet provided precise responses to these new intrusive methods of monitoring workers. The overall goal of this paper is to discuss whether employers should be allowed to use neurosurveillance technologies in the workplace to monitor their employees' minds and, if so, under what circumstances. The authors take a hypothetical scenario of neurosurveillance in the workplace using EEG-based devices as a starting point for their analysis. On this basis, three key ethical issues are identified: an increasing power imbalance in the employment relationship; a new threat to employees' privacy, and a risk of neurodiscrimination.

A Novel Discriminative Enhancement Method for Few-Shot Remote Sensing Image Scene Classification

Remote sensing image scene classification (RSISC) has garnered significant attention in recent years. Numerous methods have been put forward in an attempt to tackle this issue, particularly leveraging deep learning methods that have shown promising performance in classifying remote sensing image (RSI). However, it is widely recognized that deep learning methods typically require a substantial amount of labeled data to effectively converge. Acquiring a sufficient quantity of labeled data often necessitates significant human and material resources. Hence, few-shot RSISC has become highly meaningful. Fortunately, the recently proposed deep nearest neighbor neural network based on the attention mechanism (DN4AM) model incorporates episodic training and class-related attention mechanisms, effectively reducing the impact of background noise regions on classification results. Nevertheless, the DN4AM model does not address the problem of significant intra-class variability and substantial inter-class similarities observed in RSI scenes. Therefore, the discriminative enhanced attention-based deep nearest neighbor neural network (DEADN4) is proposed to address the few-shot RSISC task. Our method makes three contributions. Firstly, we introduce center loss to enhance the intra-class feature compactness. Secondly, we utilize the deep local-global descriptor (DLGD) to increase inter-class feature differentiation. Lastly, we modify the Softmax loss by incorporating cosine margin to amplify the inter-class feature dissimilarity. Experiments are conducted on three diverse RSI datasets to gauge the efficacy of our approach. Through comparative analysis with various cutting-edge methods including MatchingNet, RelationNet, MAML, Meta-SGD, DN4, and DN4AM, our approach showcases promising outcomes in the few-shot RSISC task.

The Effect of the Linguistic Status of Text Previewing in Arabic on the Reading Comprehension Outcomes Among Second and Sixth Grade Native Arabs Readers: A Cross-Sectional View.

The current study examined the effect of the linguistic status of the verbal previewing strategy on the outcomes of reading comprehension tasks among second (N = 25, age 7.08 ± .3), and sixth-grade students (N = 25, age 11.75 ± .25), with typical reading development. The texts for each group were carefully matched and were divided into three conditions of verbal previewing: (a) Standard Arabic previewing (hereafter: StA previewing); (b) spoken Arabic previewing (hereafter: SpA previewing); (c) without previewing. The results showed that for the second-grade readers, SpA previewing had a significant contribution to the reading comprehension outcomes compared to the other conditions of previewing while for the sixth-grade readers; StA previewing had a significant contribution to the outcomes of reading comprehension. The findings were explained according to the assumption that relatively native Arab speaking students develop a progressive change toward activation of StA representations for verbal learning. Such representations become more efficient as a result of the dominant exposure to StA during performing reading and writing tasks.

An at-home evaluation of a light intervention to mitigate sleep inertia symptoms

ObjectivesUnder laboratory settings, light exposure upon waking at night improves sleep inertia symptoms. We investigated whether a field-deployable light source would mitigate sleep inertia in a real-world setting. MethodsThirty-six participants (18 female; 26.6 years ± 6.1) completed an at-home, within-subject, randomized crossover study. Participants were awoken 45 minutes after bedtime and wore light-emitting glasses with the light either on (light condition) or off (control). A visual 5-minute psychomotor vigilance task, Karolinska sleepiness scale, alertness and mood scales, and a 3-minute auditory/verbal descending subtraction task were performed at 2, 12, 22, and 32 minutes after awakening. Participants then went back to sleep and were awoken after 45 minutes for the opposite condition. A series of mixed-effect models were performed with fixed effects of test bout, condition, test bout × condition, a random effect of the participant, and relevant covariates. ResultsParticipants rated themselves as more alert (p = .01) and energetic (p = .001) in the light condition compared to the control condition. There was no effect of condition for descending subtraction task outcomes when including all participants, but there was a significant improvement in descending subtraction task total responses in the light condition in the subset of participants waking from N3 (p = .03). There was a significant effect of condition for psychomotor vigilance task outcomes, with faster responses (p < .001) and fewer lapses (p < .001) in the control condition. ConclusionsOur findings suggest that light modestly improves self-rated alertness and energy after waking at home regardless of sleep stage, with lower aggression and improvements to working memory only after waking from N3. Contrary to laboratory studies, we did not observe improved performance on the psychomotor vigilance task. Future studies should include measures of visual acuity and comfort to assess the feasibility of interventions in real-world settings.

Fostering a virtual knowledge sharing community in a Malaysian financial institution

The purpose of this research was to study the set-up process of cultivating a knowledge sharing community in a financial institution comprising of 500 over employees. The areas that were researched were – what major issues can be identified during the set-up phase, the critical success factors, and way-forward plans to sustain this program. This paper proposes in adopting an IS model in implementing a virtual knowledge sharing community. Conforming to a case study approach, the study investigates the outcomes of a 14-month on-going implementation program, encompassing of strategic discussions and multiple workshops in assessing critical knowledge assets and developing techniques in how best to manage explicit and tacit knowledge, amongst knowledge workers. The valuable insights were focused, in particular on KM training for the newly designated ‘KM Stewards’ who were responsible of driving the organisational KM strategic initiatives and the implementation of a KM capability assessment technique that can be adopted in a financial sector. Special emphasis was made in building the skills in intra-organisational cooperation and management engagement for meeting the challenge of ‘capturing’ and codifying tacit knowledge and ultimately the generation of lessons learned and fostering of virtual communities of practice in promoting a new form of ‘collective intelligence’ in the organisation. With practitioners’ experience in adopting an IS model, this paper will highlight the implementation road map for assessing the usage of knowledge management systems, challenges faced by the KM Task Force, action plans and outcomes with the user community. Finally, this article will be able to contribute to the theoretical knowledge on knowledge sharing in the financial sector of domain. The research methodology is discussed, followed by results and a discussion of the results. Finally, conclusion and future research are presented.

Distractor-Specific Single Neuron Activity Predicts Visual Working Memory Task Outcomes

&#x0D; &#x0D; &#x0D; &#x0D; This paper explores the relationship between neural activity and behavioral performance in the form of visual working memory (VWM) task outcomes, by answering the question: Are there any significant differences in the firing rates of individual neurons during the distractor presentation period of a VWM task between success and error trials that can predict the outcome of a trial? Distractor-specific single neuron firing rates during a VWM task were analyzed to answer this question. A logistic regression was used to identify the predictive capability of neural firing rate on trial outcome with the neural activity of 51 cells from the lateral prefrontal cortex (LPFC) of a primate. This study found that a best-fit logistic model could predict the behavioral performance of the primate (success or error of the VWM task) with 63.01% accuracy, with additional machine learning techniques producing scores upwards of 68% accuracy. Moreover, greater firing rates in response to the distractor, indicating less efficient distractor suppression, accompanied the error trials of the VWM task. This suggests that stronger neural responses to task-specific distractors can hinder the attentional filtering required for efficient working memory, supporting previous research that found that distractor suppression is a mechanism that heavily influences WM efficiency. These findings indicate that people, particularly children, with disorders that affect WM capacity such as ADHD may experience stronger neural responses to distractors, and therefore inefficient distractor suppression, at the single neuron level when engaging in goal-oriented behaviors, which can significantly impact learning and other developmental processes.&#x0D; &#x0D; &#x0D; &#x0D;

Rhodiola rosea as an adaptogen to enhance exercise performance: a review of the literature.

Rhodiola rosea (RR) is a plant whose bioactive components may function as adaptogens, thereby increasing resistance to stress and improving overall resilience. Some of these effects may influence exercise performance and adaptations. Based on studies of rodents, potential mechanisms for the ergogenic effects of RR include modulation of energy substrate stores and use, reductions in fatigue and muscle damage and altered antioxidant activity. At least sixteen investigations in humans have explored the potential ergogenicity of RR. These studies indicate acute RR supplementation (∼200 mg RR containing ∼1 % salidroside and ∼3 % rosavin, provided 60 min before exercise) may prolong time-to-exhaustion and improve time trial performance in recreationally active males and females, with limited documented benefits of chronic supplementation. Recent trials providing higher doses (∼1500 to 2400 mg RR/d for 4–30 d) have demonstrated ergogenic effects during sprints on bicycle ergometers and resistance training in trained and untrained adults. The effects of RR on muscle damage, inflammation, energy system modulation, antioxidant activity and perceived exertion are presently equivocal. Collectively, it appears that adequately dosed RR enhances dimensions of exercise performance and related outcomes for select tasks. However, the current literature does not unanimously show that RR is ergogenic. Variability in supplementation dose and duration, concentration of bioactive compounds, participant characteristics, exercise tests and statistical considerations may help explain these disparate findings. Future research should build on the longstanding use of RR and contemporary clinical trials to establish the conditions in which supplementation facilitates exercise performance and adaptations.