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\"This book describes research on training using cognitive psychology to build a complete empirical and theoretical picture of the training process. It includes a review of relevant cognitive psychological literature, a summary of recent laboratory experiments, a presentation of original theoretical ideas, and a discussion of possible applications to real-world training settings\"--Provided by publisher.

The dual task experimental paradigm is used to probe the attentional requirements of postural control. However, findings of dual task postural studies have been inconsistent with many studies even reporting improvement in postural stability during dual tasking and thus raising questions about cognitive involvement in postural control. A U-shaped non-linear relationship has been hypothesized between cognitive task complexity and dual task postural stability suggesting that the inconsistent results might have arisen from the use of cognitive tasks of varying complexities. To systematically review experimental studies that compared the effect of simple and complex cognitive tasks on postural stability during dual tasking, we searched seven electronic databases for relevant studies published between 1980 to September 2020. 33 studies involving a total of 1068 participants met the review’s inclusion criteria, 17 of which were included in meta-analysis (healthy young adults: 15 studies, 281 participants; Stroke patients: 2 studies, 52 participants). Narrative synthesis of the findings in studies involving healthy old adults was carried out. Our result suggests that in healthy population, cognitive task complexity may not determine whether postural stability increases or decreases during dual tasking (effect of cognitive task complexity was not statistically significant; P > 0.1), and thus the U-shaped non-linear hypothesis is not supported. Rather, differential effect of dual tasking on postural stability was observed mainly based on the age of the participants and postural task challenge, implying that the involvement of cognitive resources or higher cortical functions in the control of postural stability may largely depends on these two factors.

Introduction : Dual-task studies have demonstrated that walking is attention-demanding for younger adults. However, numerous studies have attributed this to task type rather than the amount of required to accomplish the task. This study examined four tasks: two discrete (i.e., short intervals of attention) and two continuous (i.e., sustained attention) to determine whether greater attentional demands result in greater dual-task costs due to an overloaded processing capacity. Methods : Nineteen young adults (21.5 ± 3.6 years, 13 females) completed simple reaction time (SRT) and go/no-go (GNG) discrete cognitive tasks and n-back (NBK) and double number sequence (DNS) continuous cognitive tasks with or without self-paced walking. Prefrontal cerebral hemodynamics were measured using functional near-infrared spectroscopy (fNIRS) and performance was measured using response time, accuracy, and gait speed. Results : Repeated measures ANOVAs revealed decreased accuracy with increasing cognitive demands ( p = 0.001) and increased dual-task accuracy costs ( p < 0.001). Response times were faster during the single compared to dual-tasks during the SRT ( p = 0.005) and NBK ( p = 0.004). DNS gait speed was also slower in the dual compared to single task ( p < 0.001). Neural findings revealed marginally significant interactions between dual-task walking and walking alone in the DNS ( p = 0.06) and dual -task walking compared to the NBK cognitive task alone ( p = 0.05). Conclusion : Neural findings suggest a trend towards increased PFC activation during continuous tasks. Cognitive and motor measures revealed worse performance during the discrete compared to continuous tasks. Future studies should consider examining different attentional demands of motor tasks.

Cognitive‐relevant information is processed by different brain areas that cooperate to eventually produce a response. The relationship between local activity and global brain states during such processes, however, remains for the most part unexplored. To address this question, we designed a simple face‐recognition task performed in patients with drug‐resistant epilepsy and monitored with intracranial electroencephalography (EEG). Based on our observations, we developed a novel analytical framework (named “local–global” framework) to statistically correlate the brain activity in every recorded gray‐matter region with the widespread connectivity fluctuations as proxy to identify concurrent local activations and global brain phenomena that may plausibly reflect a common functional network during cognition. The application of the local–global framework to the data from three subjects showed that similar connectivity fluctuations found across patients were mainly coupled to the local activity of brain areas involved in face information processing. In particular, our findings provide preliminary evidence that the reported global measures might be a novel signature of functional brain activity reorganization when a stimulus is processed in a task context regardless of the specific recorded areas. Cognitive‐relevant information is processed by different brain areas that cooperate to eventually produce a response. Here, we develop a novel analytical framework (named “local–global” framework) to statistically correlate the signal power in every recorded gray‐matter region (intracranial electroencephalography) with the widespread connectivity fluctuations as proxy to identify concurrent local and global activity that may plausibly reflect a common functional network during cognition. The application of the local–global framework to the data from three subjects with different recorded areas showed similar connectivity fluctuations, which were mainly coupled to local activity of brain areas involved in the task.

Lack of attention to obstacles on the floor or walking path may cause trip and fall accidents. The preparatory activity in the motor cortex to the perturbation associated with obstacle avoidance movements with cognitive task is still unclear. The purpose of this study was to investigate the motor cortical activity involved in the preparation and execution of concurrent obstacle avoidance movement and cognitive task. Twenty young adults were required to step over obstacles that were projected on the floor while performing a cognitive task. The electroencephalogram was recorded, and the movement-related cortical potentials (MRCP) aligned by foot dorsiflexion were evaluated. There was no significant difference in the number of contacts between the toe and the obstacle between the obstacle avoidance task and obstacle avoidance with cognitive task; however, the distance between the toe and the obstacle just before obstacle avoidance movement was significantly extended in the latter task. The amplitude and the onset of MRCP during the dual task were decreased and delayed, respectively, compared with the simple obstacle avoidance movement task. These results suggest that the young participants changed their clearance strategy to stepping over the obstacle during the concurrent motor and cognitive dual task to reduce motor cortical activity.

Locating cognitive task states by measuring changes in electrocortical activity due to various attentional and sensory-motor changes, has been in research interest since last few decades. In this paper, different cognitive states while performing various attentional and visuo-motor coordination tasks, are classified using electroencephalogram (EEG) signal. A non-linear time-series method, multifractal detrended fluctuation analysis (MFDFA) , is applied on respective EEG signal for features. Using MFDFA based features a multinomial classification is achieved. Nine channel EEG signal was recorded for 38 young volunteers (age: 25 ± 5 years, 30 male and 8 female), during six consecutive tasks. First three tasks are related to increasing levels of selective focus vision; next three are reflex and response based computer tasks. Total of 90 features (ten features from each of nine channel) were extracted from Hurst and singularity exponents of MFDFA on EEG signals. After feature selection, a multinomial classifier of six classes using two methods: support vector machine (SVM) and decision tree classifier (DTC). An accuracy of 96.84% using SVM and 92.49% using DTC was achieved.

Motor adaptation plays an important role in the acquisition of new motor skills. It has been reported that cognitive tasks can promote motor adaptation; however, which cognitive tasks effectively promote motor adaptation remains unknown. This study aimed to examine what factors of cognitive tasks contribute to promoting motor adaptation. Forty-two healthy young adults were randomly assigned to one of three groups: incongruent Stroop task group (iSTG), congruent Stroop task group (cSTG), and control group (CG). All participants underwent 20 blocks of a mouse-tracking task on the 1st and 2nd days. Before the mouse tracking task on the 1st day, the iSTG and cSTG completed the incongruent and congruent Stroop tasks, respectively. Participants in the CG did not perform any cognitive tasks. On the 28th day, all participants underwent 3 blocks of the mouse tracking task to evaluate their retention of motor adaptation. As a result, on the 1st day, the mouse tracking task performance improved equally for both groups. However, on the 2nd and 28th days, the mouse tracking task performance in the iSTG showed greater improvements for all blocks compared to those in the CG. These results suggest that the incongruent Stroop task promotes motor adaptation, but the congruent Stroop task does not. In addition, it is suggested that factors, which are primarily involved in the incongruent Stroop task, might promote motor adaptation.

This study investigated effects of cognitive dual-task interference and task prioritization instructions on task performance and trunk control during a dynamic balance task in persons with and without recurrent low back pain (rLBP). First, we tested the hypothesis that those with rLBP rely more on cognitive resources than back-healthy controls, and therefore trunk kinematics would be altered under dual-task interference conditions. Then, we tested participants’ ability to modulate task performance in accord with prioritization instructions. Persons with and without rLBP (n = 19/group) performed the Balance-Dexterity Task, which involved single-limb balance while compressing an unstable spring with the other limb, with and without a cognitive task engaging verbal working memory. Trunk coupling was quantified with the coefficient of determination (R2) of an angle–angle plot of thorax–pelvis frontal plane motion. Task performance was quantified using variability of spring compression force and of cognitive task errors. Trunk coupling in the rLBP group was lower than that of the back-healthy control group in the single-task condition (p = 0.024) and increased in the dual-task condition (p = 0.006), abolishing the difference between groups. Significant main effects of task prioritization instruction on performance were observed with no differences between groups, indicating similar performance modulation. Cognitive task error variability decreased with a switch from a single- to dual-task condition, exposing an unexpected facilitation effect. We interpret these findings in the context of movement-specific reinvestment and action-specific perception theories as they pertain to cognitive contributions to posture and how the dual-task interference paradigm may influence those contributions.

Working memory plays an important role in cognitive science and is a basic process for learning. While working memory is limited in regard to capacity and duration, different cognitive tasks are designed to overcome these difficulties. This study investigated information flow during a novel visual working memory task in which participants respond to exaggerated and normal pictures. Ten healthy men (mean age 28.5 ± 4.57 years) participated in two stages of the encoding and retrieval tasks. The electroencephalogram (EEG) signals are recorded. Moreover, the adaptive directed transfer function (ADTF) method is used as a computational tool to investigate the dynamic process of visual working memory retrieval on the extracted event-related potentials (ERPs) from the EEG signal. Network connectivity and P300 sub-components (P3a, P3b, and LPC) are also extracted during visual working memory retrieval. Then, the nonparametric Wilcoxon test and five classifiers are applied to network properties for features selection and classification between exaggerated-old and normal-old pictures. The Z -values of Ge is more distinctive rather than other network properties. In terms of the machine learning approach, the accuracy, F1-score, and specificity of the k-nearest neighbors (KNN), classifiers are 81%, 77%, and 81%, respectively. KNN classifier ranked first compared with other classifiers. Furthermore, the results of in-degree/out-degree matrices show that the information flows continuously in the right hemisphere during the retrieval of exaggerated pictures, from P3a to P3b. During the retrieval of visual working memory, the networks associated with attentional processes show greater activation for exaggerated pictures compared to normal pictures. This suggests that the exaggerated pictures may have captured more attention and thus required greater cognitive resources for retrieval. Graphical abstract

The Emergency Department (ED) serves as a vital gateway to acute care, where timely and accurate triage decisions are essential to ensure appropriate patient prioritisation and efficient use of limited resources. Triage nurses operate in high-pressure environments and must make rapid decisions, often under conditions of uncertainty, relying on a blend of analytical reasoning and intuitive judgement. However, this complex decision-making process is susceptible to a range of challenges, including cognitive biases, communication breakdowns, procedural inconsistencies, fatigue, and stress, all of which can compromise patient safety and care quality. This study explores the multifaceted nature of triage decision-making, focusing on the influencing factors, cognitive processes, and real-world challenges experienced by nurses. By deepening our understanding of these elements, the paper lays the groundwork for the development of effective Clinical Decision Support Systems (CDSS) that can enhance clinical judgement and support nurses in delivering safe, timely, and efficient emergency care. The study used cognitive task analysis through interviews and observations to capture the cognitive strategies used by nurses during triage. This approach provided detailed insights into how nurses assess patient acuity, handle uncertainty, verify decisions, and manage challenges. This study identified 26 themes from interviews and observations, illustrating how nurses use experience and protocols such as the Emergency Severity Index to manage patient flow. Key challenges encountered in triage included overcrowding, staff shortages, high patient acuity, communication barriers, frequent interruptions, and multitasking demands. Despite these hurdles, nurses adapted through prioritization and collaboration. The findings highlight significant implications for emergency health care, mainly the need for improvements in triage decision making, resource utilization, and patient safety. Data-driven clinical decision support systems can enhance decision making, streamline assessments, reduce delays, and improve safety and equity in triage, particularly in high-stress, resource-constrained environments. This study has significant implications for clinical practice, particularly in emergency care settings where effective triage is critical for patient outcomes. By exploring the cognitive processes and challenges faced by triage nurses, the research provides valuable insights into the complexities of decision making under pressure. The findings emphasize the importance of clinical decision support systems to enhance decision accuracy, reduce cognitive load, and mitigate the risk of errors. Implementing data-driven technologies and refining triage protocols can lead to more efficient resource allocation, more streamlined workflows, reduced waiting times, and improved patient safety. By aligning clinical decision support system design with the cognitive processes of triage nurses, this study supports the development of tools that enhance decision accuracy, reduce cognitive load, and improve patient prioritization, ultimately promoting safer, faster, and more consistent triage in high-pressure emergency settings.