Daily Life Scenarios Research Articles

The effects of deception on memory: a comparative study of actors and eyewitnesses accounts

Deception is a common occurrence in daily life and has been shown to impair memory. This study investigated the memory-undermining effects of deception in a simulated daily life scenario, focusing on the potential moderating effect of the liars’ role (i.e., actor vs. eyewitness). In a 2 × 2 between-subjects design, 128 participants were randomly assigned to one of four conditions: actors who told the truth, actors who lied, eyewitnesses who told the truth, and eyewitnesses who lied. Participants engaged in a simulated shopping task, followed by an interview where they either provided truthful or deceptive responses about the items they bought (actors) or observed (eyewitnesses). Two days later, participants completed a series of memory tests assessing item memory, source memory, destination memory, and non-believed memories. Results showed that deception impaired source memory, with a greater impairment observed for actors than eyewitnesses. Deception also led to more non-believed memories and impaired item and destination memory, regardless of the liars’ role. These findings suggest that the role of liars moderates the effects of deception on memory, with self-related deception (actors) leading to greater memory impairments than other-related deception (eyewitnesses).

Estimation of electrical muscle activity during gait using inertial measurement units with convolution attention neural network and small-scale dataset

In general, muscle activity can be directly measured using Electromyography (EMG) or calculated with musculoskeletal models. However, both methods are not suitable for non-technical users and unstructured environments. It is desired to establish more portable and easy-to-use muscle activity estimation methods. Deep learning (DL) models combined with inertial measurement units (IMUs) have shown great potential to estimate muscle activity. However, it frequently occurs in clinical scenarios that a very small amount of data is available and leads to limited performance of the DL models, while the augmentation techniques to efficiently expand a small sample size for DL model training are rarely used. The primary aim of the present study was to develop a novel DL model to estimate the EMG envelope during gait using IMUs with high accuracy. A secondary aim was to develop a novel model-based data augmentation method to improve the performance of the estimation model with small-scale dataset. Therefore, in the present study, a time convolutional network-based generative adversarial network, namely MuscleGAN, was proposed for data augmentation. Moreover, a subject-independent regression DL model was developed to estimate EMG envelope. Results suggested that the proposed two-stage method has better generalization and estimation performance than the commonly used existing methods. Pearson correlation coefficient and normalized root-mean-square errors derived from the proposed method reached up to 0.72 and 0.13, respectively. It was indicated that the MuscleGAN indeed improved the estimation accuracy of lower limb EMG envelope from 70% to 72%. Thus, even using only two IMUs and a very small-scale dataset, the proposed model is still capable of accurately estimating lower limb EMG envelope, demonstrating considerable potential for its application in clinical and daily life scenarios.

Application of a mobile health data platform for public health surveillance: A case study in stress monitoring and prediction.

Public health surveillance involves the collection, analysis and dissemination of data to improve population health. The main sources of data for public health decision-making are surveys, typically comprised of self-report which may be subject to biases, costs and delays. To complement subjective data, objective measures from sensors could potentially be used. Specifically, advancements in personal mobile and wearable technologies enable the collection of real-time and continuous health data. In this context, the goal of this work is to apply a mobile health platform (MHP) that extracts health data from the Apple Health repository to collect data in daily-life scenarios and use it for the prediction of stress, a major public health issue. A pilot study was conducted with 45 participants over 2 weeks, using the MHP to collect stress-related data from Apple Health and perceived stress self-reports. Apple, Withings and Empatica devices were distributed to participants and collected a wide range of data, including heart rate, sleep, blood pressure, temperature, and weight. These were used to train random forests and support vector machines. The SMOTE technique was used to handle imbalanced datasets. Accuracy and f1-macro scores were in line with state-of-the-art models for stress prediction above 60% for the majority of analyses and samples analysed. Apple Watch sleep features were particularly good predictors, with most models with these data achieving results around 70%. A system such as the MHP could be used for public health data collection, complementing traditional self-reporting methods when possible. The data collected with the system was promising for monitoring and predicting stress in a population.

Color-Based Object Tracking Using Forward and Inverse Kinematics

Nowadays, the current surge in diverse applications of robotic arms makes comprehending these concepts of paramount importance. Various studies have been conducted on the topic of robotic arms. However, little attention was paid to the coupling relationship between object tracking and kinematics. Therefore, this research paper delves into the intricacies of robotic arm dynamics, with a specific focus on the topics of Forward and Inverse Kinematics, alongside object tracking facilitated by color-based methodologies to deepen foundational insights into robotic arm functionality. Controlling the motion and movement of robotic arms is the initial step. Leveraging the capabilities of cutting-edge technologies such as OpenCV, Armpi, and Hiwonder, it becomes feasible to precisely rotate the servos to user-input degrees and accurately position the arm based on three-dimensional Cartesian coordinates. Additionally, cameras equipped in the system enable photographing and recognizing target objects. By analyzing various positions of the target object, the robotic arm can dynamically adjust multiple servos to maintain the object's relative position. This tracking mechanism relies on the Proportional-Integral-Derivative algorithm, incorporating a minimum valid area parameter to mitigate interferences. Harnessing these algorithms empowers the robotic arm to discern objects based on camera-detected colors, and is affected slightly by different angles or distances, meaning the system's error margin consistently resides within a favorable interval. The approach exhibits practical utility in daily life scenarios, such as drug classification and recognition.

Smartphones Helping Memory-Impaired Individuals Overcome Inconveniences During Daily Living.

The external compensatory means recommended for rehabilitation of memory disorder must consist of a means for storing information externally and a clue for accessing the externally stored information. Writing down the information on a paper pocketbook is usually used as an external compensatory means, but smartphones are overwhelmingly more useful in terms of the functions as compared to paper pocketbooks. The author, who has a memory disorder and works as an occupational therapist, has recently devised a method for utilizing a smartphone for overcoming the inconveniences during daily living and herein describes how to use it in daily life scenarios.

Estimation of Lower Limb Joint Angles and Joint Moments during Different Locomotive Activities Using the Inertial Measurement Units and a Hybrid Deep Learning Model.

Using inertial measurement units (IMUs) to estimate lower limb joint kinematics and kinetics can provide valuable information for disease diagnosis and rehabilitation assessment. To estimate gait parameters using IMUs, model-based filtering approaches have been proposed, such as the Kalman filter and complementary filter. However, these methods require special calibration and alignment of IMUs. The development of deep learning algorithms has facilitated the application of IMUs in biomechanics as it does not require particular calibration and alignment procedures of IMUs in use. To estimate hip/knee/ankle joint angles and moments in the sagittal plane, a subject-independent temporal convolutional neural network-bidirectional long short-term memory network (TCN-BiLSTM) model was proposed using three IMUs. A public benchmark dataset containing the most representative locomotive activities in daily life was used to train and evaluate the TCN-BiLSTM model. The mean Pearson correlation coefficient of joint angles and moments estimated by the proposed model reached 0.92 and 0.87, respectively. This indicates that the TCN-BiLSTM model can effectively estimate joint angles and moments in multiple scenarios, demonstrating its potential for application in clinical and daily life scenarios.

Construction of Multi-Label Personality Trait Recognition Model in Chinese Text Based on Emotional Features

The key to improve the service effect of virtual digital human is the personality trait recognition technology of users. However, the complexity of Chinese and the sparsity of short texts limit the accuracy of personality trait recognition. Therefore, we applied emotional features to the research of personality trait recognition and constructed a deep learning model for Chinese corpus personality trait recognition to improve the accuracy of personality trait recognition. The model ([Formula: see text] extracted semantic features and embedded emotional features through RoBERTa-wwm-ext Chinese pre-training model, BiLSTM neural network, and attention mechanism. Some experiments were carried out using two Chinese datasets with different scenes. The comparative experiments and ablation experiments showed that the overall effect of the personality trait recognition model ([Formula: see text] is the best, and the Macro-F1 value reaches 75.3%, which is significantly better than the existing models. The results showed that emotional features can significantly improve the accuracy of multi-label personality trait classification in both Chinese daily life scenarios and online scenarios. The proposed model can integrate emotional features and Chinese semantic features to improve the accuracy of personality traits recognition. These findings support the development of personality trait recognition and the application of virtual digital human.

Radar-Beat: Contactless beat-by-beat heart rate monitoring for life scenes

Background and objective:Heartbeat is a crucial vital sign, and radio-frequency technology can measure the micro-vibration of the body surface from a distance and extract the heartbeat signal, which can solve the compliance problem caused by wearable sensors. However, most existing works verify short-term controlled experiments and seldom consider the challenges posed by random body movements and posture changes during long-term monitoring in daily life scenarios. In this article, we propose a systematic solution called Radar-Beat, which enables accurate heartbeat monitoring using an mmWave FMCW radar device. Our goal is to promote the integration of contactless heartbeat monitoring into life scenarios. Methods:We proposes a sensitive body motion detection algorithm and an optimal Range-bin selection algorithm, which can automatically identify body motion and update the best heartbeat signal channel. Then, we construct a personalized heartbeat template for each signal segment and propose a global optimization model to improve the accuracy of heartbeat length estimation. Results:We use the synchronized ECG signal as the ground truth, results show strong agreement between Radar-Beat and synchrony ECG devices for heart rate and inter-beat interval (IBI) measurements in healthy subjects. In the heartbeat monitoring experiment for a total of 72 h and 56 min involving 11 participants, under a time coverage of 91.2%, the median error of IBI estimation was 12 ms. Conclusion:Radar-Beat has strong robustness to different individuals in different postures and positions. It can be deployed in a hospital bed or home to enable continuous heartbeat monitoring in an unobtrusive way.

Cognitive Training With Head-Mounted Display Virtual Reality in Neurorehabilitation: Pilot Randomized Controlled Trial.

Neurological rehabilitation is technologically evolving rapidly, resulting in new treatments for patients. Stroke, one of the most prevalent conditions in neurorehabilitation, has been a particular focus in recent years. However, patients often need help with physical and cognitive constraints, whereby the cognitive domain in neurorehabilitation does not technologically exploit existing potential. Usually, cognitive rehabilitation is performed with pen and paper or on a computer, which leads to limitations in preparation for activities of daily living. Technologies such as virtual reality (VR) can bridge this gap. This pilot study investigated the use of immersive VR in cognitive rehabilitation for patients undergoing inpatient neurorehabilitation. The goal was to determine the difference in rehabilitation effectiveness between a VR serious game that combines everyday activities with cognitive paradigms and conventional computerized cognitive training. We hypothesized the superiority of the VR serious game regarding cognitive abilities and patient-reported outcomes as well as transfer to daily life. We recruited 42 patients with acute brain affection from a German neurorehabilitation clinic in inpatient care with a Mini Mental Status Test score >20 to participate in this randomized controlled trial. Participants were randomly assigned to 2 groups, with 1 receiving the experimental VR treatment (n=21). VR training consisted of daily life scenarios, for example, in a kitchen, focusing on treating executive functions such as planning and problem-solving. The control group (n=21) received conventional computerized cognitive training. Each participant received a minimum of 18 treatment sessions in their respective group. Patients were tested for cognitive status, subjective health, and quality of life before and after the intervention (Alters-Konzentrations-Test, Wechsler Memory Scale-Revised, Trail Making Test A and B, Tower of London-German version, Short Form 36, European Quality of Life 5 Dimensions visual analog scale, and Fragebogen zur Erfassung der Performance in VR). Repeated-measures ANOVA revealed several significant main effects in the cognitive tests: Tower of London-German version (P=.046), Trail Making Test A (P=.01), and Wechsler Memory Scale-Revised (P=.006). However, post hoc tests revealed that the VR group showed significant improvement in the planning, executive control, and problem-solving domains (P=.046, Bonferroni P=.02). In contrast, no significant improvement in the control group between t0 and t1 was detected (all P>.05). Furthermore, a nonsignificant trend was observed in visual speed in the VR group (P=.09, Bonferroni P=.02). The results of this pilot randomized controlled trial showed that immersive VR training in cognitive rehabilitation had greater effectiveness than the standard of care in treating patients experiencing stroke in some cognitive domains . These findings support the further use and study of VR training incorporating activities of daily living in other neurological disorders involving cognitive dysfunction. Federal Registry of Clinical Trials of Germany (DRKS) DRKS00023605; https://drks.de/search/de/trial/DRKS00023605.

Machine learning models for stroke detection by observing the eye-movement features under five-color visual stimuli in traditional Chinese medicine

To develop a novel diagnostic modality to identify and diagnose stroke in daily life scenarios for improving the therapeutic effects and prognoses of patients. In this study, 16 stroke patients and 24 age-matched healthy participants as controls were recruited for comparative analysis. Leveraging a portable eye-tracking device and integrating traditional Chinese medicine theory with modern color psychology principles, we recorded the eye movement signals and calculated eye movement features. Meanwhile, the stroke recognition models based on eye movement features were further trained by using random forest (RF), k-nearest neighbors (KNN), decision tree (DT), gradient boosting classifier (GBC), XGBoost, and CatBoost. The stroke group and the healthy group showed significant differences in some eye movement features (P< .05). The models trained based on eye movement characteristics had good performances in recognizing stroke individuals, with accuracies ranging from 77.40% to 88.45%. Under the red stimulus, the eye movement model trained by RF became the best machine learning model with a recall of 84.65%, a precision of 86.48%, a F1 score of 85.47%. Among the six algorithms, RF and CatBoost performed better in classification. This study pioneers the application of traditional Chinese medicine’s five-color stimuli to visual observation tasks. On the basis of the combined design, the eye-movement models can accurately identify stroke, and the developed high-performance models may be used in daily life scenarios.

How does VR differ from renderings in human perception of office layout design? A quantitative evaluation of a full-scale immersive VR

Virtual reality (VR) technology provides immersive and interactive user experiences in office layout design. However, the mechanism of how VR impacts human perception in layout selections and how such mechanism differs from the traditional rendering-based exhibition remain unclear. Hence, this research proposes an experience-based full-scale immersive VR-based approach and investigates its quantitative effectiveness when conducting user-centered office layout design and evaluation. Comparative experiments (N=57) with three office designs are conducted, where subjective surveys and objective Stroop tests are utilized to statistically analyze (1) the quantitative difference in user layout satisfaction between using VR and traditional approaches in terms of psychological perceptions and environmental design features, and (2) the consistency between the layout perceptions using VR and the underlying design principles. The results reveal that the influence of VR varies from different design indicators. The significant difference in the satisfaction variation does not exist in the psychological perceptions (i.e., privacy, communication, and concentration) but does exist in the environmental design features (i.e., workspace enclosure, storage, rearrangement), probably because VR can help the end-users visualize the distinct characteristics of office layouts and further rectify their cognition when experiencing daily-life scenarios. In addition, the effectiveness of the VR approach is verified, where the layout perceptions are mostly in line with the design principles. The findings of this research are expected to extend the existing knowledge of VR-based office design and evaluation by providing quantitative evidence and analytical insight into human behaviors in user-centered layout evaluation.

Development of Piezoelectric Elastomers and Their Applications in Soft Devices

AbstractPiezoelectric materials have wide‐ranging applications owing to their capacity to convert mechanical energy into electrical energy in daily life scenarios. With rapid development in fields such as wearable electronics, intelligent electronic products, and medical equipment, the requirement for the elasticity of piezoelectric materials has become increasingly stringent. However, combining high piezoelectric performance with high elasticity in most conventional piezoelectric materials is challenging, limiting the application of piezoelectric elastomers in complex scenarios. Recently, several piezoelectric elastomer materials with good flexibility, high elasticity, and easy processing ability have been reported, and their applications in soft devices have rapidly developed. In this review, the current state of piezoelectric elastomers and their applications are systematically reviewed. Additionally, an overview of trends in the development of piezoelectric elastomer materials is given, providing a reference for future research in this field.

大學生在效益論和正義思維兩難情境中客觀立場與置身其中的比較

&lt;p&gt;本研究的目的在於檢視「效益論與正義思維兩難情境量表」之心理計量特徵,瞭解台灣的大學生在該量表上之表現情形,並探討個人在不同情境下,處在不同的立場觀點所做的道德判斷之差異組型,以及探究重要的背景變項與效益和正義取向的關聯性。根據 230 位受試者的實徵資料發現,台灣的大學生在分量表 1 較傾向於效益論的取向,而在分量表 2 和 3 則較傾向於正義思維的判斷;15 題兩難情境中,有 13 題在客觀立場和置身其中兩個不同觀點所做出的效益判斷得分的平均數和百分比有顯著差異。性別、教育水準、支持的黨派、宗教信仰以及工作經驗等是影響受試者的表現之重要背景變項。&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;&lt;p&gt;This study evaluated the psychometric characteristics of a utilitarianism versus justice scale and assessed the moral reasoning of students faced with utilitarianism versus justice dilemmas based on daily life scenarios. The consistency of their moral judgments made from objective and situated standpoints was also evaluated. The effect of background variables on their utilitarianism versus justice scale scores was explored. Empirical data from 230 participants revealed that the college students were generally more utilitarianism oriented on the first subscale and more justice oriented on the second and third subscales. Among the 15 items, 13 items differed significantly with respect to the means and proportions of the moral judgment scores the students assigned from objective and situated standpoints. Sex, education level, partisan orientation, religious beliefs, and working experience were significantly correlated with the utilitarianism versus justice scale scores.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

Analysis of the Investment Value of Pony.ai Based on SWOT Analysis

With the rapid development of autonomous driving technology in daily life scenarios and logistics, some potential start-ups have emerged on such tracks. Therefore, research on this industry in the field of business research cannot be ignored. This article analyzes the investment value of pony.ai, one of the startups in the autonomous driving industry. Firstly, the macro market where pony.ai is located is analyzed, concluding that this industry is expected to develop rapidly and flourish in the next few years. Following the background information and projected financial position of pony.ai, which provides a basic understanding of the company. Additionally, showing the investment value of pony.ai by presenting its current situation through four dimensions, also known as SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis. The results show that pony.ai can be a worthwhile investment choice, whereas specific improvements are needed. The results also have certain reference significance for analyzing startups of the same type.

Cognitive control in creative discovery: The gap between ideal and reality

Creative discovery involves discovering the additional values of existing things in the environment by identifying the novel associations between seemingly unrelated things; the judgment made in this process is expected to be accurate but not entirely correct. From the perspective of cognitive processing, what is the difference between the ideal and real states of creative discovery? This is largely unknown. In this study, a daily life scenario was presented, and a great number of seemingly unrelated tools were presented for participants to discover valuable tools. Electrophysiological activity was recorded when participants identified tools, and we then retrospectively analyzed the differences between responses. Compared with usual tools, unusual tools evoked greater N2, N400 and late sustained potential (LSP) amplitudes, which was likely associated with the monitoring and resolution of cognitive conflicts. Moreover, unusual tools evoked smaller N400 and greater LSP amplitudes when correctly identified as usable than when identified as unusable; this result suggested that creative discovery in the ideal state should depend on the cognitive control involved in resolving conflicts. However, in the comparison between subjectively rated usable and unusable tools, smaller N400 and greater LSP amplitudes were observed only when unusual tools could be identified by expanding the application scope but not by releasing functional fixedness; this outcome suggested that creative discovery in the real state was not always influenced by the cognitive control involved in resolving conflicts. The difference in cognitive control that should be exerted and that was actually exerted to identify novel associations was discussed.

A Deep Learning-Based Indoor Odor Compass

Mobile robot-based odor source localization (OSL) has broad applications in various industrial and daily-life scenarios. To this end, a deep learning-based odor compass is designed in this work. Functionally, the designed odor compass is divided into three primary modules, which are the sensing module (i.e., a sensor array composed of four metal-oxide-semiconductor (MOS) gas sensors), the communication module, and the remote data processing module (i.e., a deep learning-based algorithm). In particular, a deep learning-based odor attention (DL-OA) model is proposed to realize an end-to-end odor source direction estimation (OSDE) based on the responses of gas sensor array. Moreover, the proposed DL-OA model adopts a separated spatial-temporal attention-based encoder-decoder structure. Furthermore, the average validation error in estimating the OSD in an indoor environment is 4.98°, essentially demonstrating the effectiveness of designed odor compass.

Pattern Matching for Real-Time Extraction of Fast and Slow Spectral Components from sEMG Signals.

Previous studies have demonstrated the potential of surface electromyography (sEMG) spectral decomposition in evaluating muscle performance, motor learning, and early diagnosis of muscle conditions. However, decomposition techniques require large data sets and are computationally demanding, making their implementation in real-life scenarios challenging. Based on the hypothesis that spectral components will present low inter-subject variability, the present paper proposes the foundational principles for developing a real-time system for their extraction by utilizing a pre-defined library of components derived from an extensive data set to match new measurements. The model library was tailored to fulfill specific requirements for real-time system application and the challenges encountered during implementation are discussed in the paper. For system validation, four distinct data sets comprising isotonic and isometric muscle activations were utilized. The extracted during validation showed low inter-subject variability, suggesting that a wide range of physiological variations can be described with them. The adoption of the proposed system for muscle analysis could provide a deeper understanding of the underlying mechanisms governing different motor conditions and neuromuscular disorders, as it allows for the measurement of these components in various daily-life scenarios.

A Unifying View on Blind Source Separation of Convolutive Mixtures Based on Independent Component Analysis

In many daily-life scenarios, acoustic sources recorded in an enclosure can only be observed with other interfering sources. Hence, convolutive Blind Source Separation (BSS) is a central problem in audio signal processing. Methods based on Independent Component Analysis (ICA) are especially important in this field as they require only few and weak assumptions and allow for blindness regarding the original source signals and the acoustic propagation path. Most of the currently used algorithms belong to one of the following three families: Frequency Domain ICA (FD-ICA), Independent Vector Analysis (IVA), and TRIple- N Independent component analysis for CONvolutive mixtures (TRINICON). While the relation between ICA, FD-ICA and IVA becomes apparent due to their construction, the relation to TRINICON is not well established yet. This paper fills this gap by providing an in-depth treatment of the common building blocks of these algorithms and their differences, and thus provides a common framework for all considered algorithms.

When and why will creative discovery succeed or fail? The role of reactive control in resolving conflicts.

The process of creative discovery refers to discovering additional values of existing objects by identifying novel associations between seemingly unrelated things; however, this process is not always successful. To reveal the dynamic process of creative discovery, particularly when and why people made right or wrong judgments, the daily life scenario was described, and a possible tool was presented for judging whether it is usable to solve problems. Electrophysiological activity was recorded when people identified novel tools and then retrospectively analyzed the differences among three responses: correctly identified novel and usable (C-NU) options, falsely identified novel and usable (F-NU) options, and correctly identified novel but unusable (C-NUU) options. The results showed that, compared with ordinary tools, novel tools evoked greater N2, N400 and LSP amplitudes; the differences in these components were likely associated with the monitoring and resolution of cognitive conflicts in the process of discovering novel associations. Regarding novel tools, smaller N400 and greater LSP amplitudes were evoked by the C-NU option than by the F-NU option, and no differences in these components were found between the F-NU and C-NUU options. The findings revealed that the success or failure of discovering novel associations depended on reactive control in resolving conflicts. Only when sufficient cognitive effort is used to resolve a conflict to a great degree can the appropriately novel association be successfully discovered; otherwise, the novel and appropriate association is lost.

Associations between emotional and non-emotional cognition and subsequent mood episodes in recently diagnosed patients with bipolar disorder: A 16-month follow-up study

BackgroundBipolar disorder (BD) is associated with impairments in both emotional and non-emotional cognition. Recently, cognitive impairments have attracted increasing research interest as markers of prognosis and possible treatment targets in patients with BD. However, there is a paucity of studies investigating cognitive predictors of prognosis in BD. MethodsWe assessed 148 recently diagnosed, symptomatically stable patients with BD with a battery of emotional and non-emotional cognitive tests and followed them up over 16 months as part of an ongoing cohort study. Multiple linear regression analyses were conducted to examine the associations between cognitive performance at baseline and the recurrence and duration of (hypo)manic and depressive episodes, respectively, with adjustment for age, sex, subsyndromal symptoms and time between assessments. ResultsPoorer recognition of negative facial expressions and more negative emotions in neutral daily life scenarios were associated with greater frequency (ps ≤ .04) and longer duration (ps ≤ .03) of subsequent (hypo)manic episodes over the 16-month follow-up period. In addition, poorer global cognition, attention and psychomotor speed, and verbal fluency were associated with more (hypo)manic episodes (ps ≤ .04). Finally, more difficulty down-regulating emotion in negative social scenarios was associated with depressive relapse (p = .007).It was a limitation that patients had a delayed diagnosis of seven years from their first mood episode despite being recently diagnosed. ConclusionTrait-related cognitive impairments influence the early course in recently diagnosed patients with BD, particularly (hypo)manic relapse. Early prophylactic strategies targeting cognitive impairments may increase resilience and the course of illness in recently diagnosed patients with BD.