Perceptual Abilities Research Articles

A Multi-Mechanism Fusion Method for Robust State of Charge Estimation via Bidirectional Long Short-Term Memory Model with Mixture Kernel Mean p-Power Error Loss Optimized by Golden Jackal Optimization Algorithm

Abstract Accurate state of charge (SOC) estimation is crucial for effective battery management in various applications. The bidirectional long short-term memory (BiLSTM) as an outstanding nonlinear regression model can be used for SOC estimation. This work develops a novel multi-mechanism fusion method based on BiLSTM to further enhance its estimation performance for SOC, in which the convolutional neural network (CNN), attention mechanism, and mixture kernel mean p-power error (MKMPE) loss are introduced into the BiLSTM framework for addressing different issues. First, the introduction of CNN components aims to extract essential features from battery data, enhancing the model's comprehension of complex information. Then, the attention mechanism is used to further refine the model's perceptual ability and a robust MKMPE loss is introduced into the BiLSTM framework to replace its original mean squared error loss, and a novel robust model is developed to suppress non-Gaussian noise interference. Finally, some key hyperparameters of the proposed model are fine-tuned using the golden jackal optimization algorithm, resulting in improved estimation performance. Comparative numerical experiments are meticulously conducted in various cases to evaluate the performance of the proposed method, and the experiment results demonstrate that it can perform outstanding effectiveness in handling non-Gaussian noise scenarios.

Four Cases of Children With Phonological Impairment and Precocious Vocabulary: Making Sense of a Clinical Conundrum.

The purpose of this study was to describe the speech production, speech perception, vocabulary, and word learning abilities of lexically precocious 4-year-old children with phonological impairment, in an effort to better understand the underlying nature of phonological impairment in children. Using a case series approach, we identified four children with phonological impairment and precocious vocabulary abilities. Each child completed routine speech production and vocabulary assessments, as well as experimental speech perception and word learning tasks. The results from these tasks were then used to create profiles of each child's individual strengths and needs across the abilities assessed. Although all four children presented with phonological impairment and lexically precocious receptive and expressive vocabulary, they differed in their specific speech errors. One child presented with phonological speech errors only, while the other three children presented with an interdental lisp alongside their phonological errors. Three children presented with average speech perception abilities, and one child presented with poorer speech perception. The same three children also showed some learning of novel nonwords 1 week post-initial exposure, while the other child showed no evidence of word learning 1 week post-initial exposure. The clinical profiles of lexically precocious children with phonological impairment offered different insights into the nature of the problem. Although one child appeared to present with underspecified underlying representations of words, the other three children appeared to present with well-specified underlying representations. Of the three children with well-specified underlying representations, two appeared to have difficulty abstracting particular rules of the ambient phonological system. Further research is needed to improve our understanding of the underlying nature of phonological impairment. https://doi.org/10.23641/asha.26307640.

Minimal perception: enabling autonomy in resource-constrained robots.

The rapidly increasing capabilities of autonomous mobile robots promise to make them ubiquitous in the coming decade. These robots will continue to enhance efficiency and safety in novel applications such as disaster management, environmental monitoring, bridge inspection, and agricultural inspection. To operate autonomously without constant human intervention, even in remote or hazardous areas, robots must sense, process, and interpret environmental data using only onboard sensing and computation. This capability is made possible by advancements in perception algorithms, allowing these robots to rely primarily on their perception capabilities for navigation tasks. However, tiny robot autonomy is hindered mainly by sensors, memory, and computing due to size, area, weight, and power constraints. The bottleneck in these robots lies in the real-time perception in resource-constrained robots. To enable autonomy in robots of sizes that are less than 100 mm in body length, we draw inspiration from tiny organisms such as insects and hummingbirds, known for their sophisticated perception, navigation, and survival abilities despite their minimal sensor and neural system. This work aims to provide insights into designing a compact and efficient minimal perception framework for tiny autonomous robots from higher cognitive to lower sensor levels.

Frontoparietal network topology as a neural marker of musical perceptual abilities

Why are some individuals more musical than others? Neither cognitive testing nor classical localizationist neuroscience alone can provide a complete answer. Here, we test how the interplay of brain network organization and cognitive function delivers graded perceptual abilities in a distinctively human capacity. We analyze multimodal magnetic resonance imaging, cognitive, and behavioral data from 200+ participants, focusing on a canonical working memory network encompassing prefrontal and posterior parietal regions. Using graph theory, we examine structural and functional frontoparietal network organization in relation to assessments of musical aptitude and experience. Results reveal a positive correlation between perceptual abilities and the integration efficiency of key frontoparietal regions. The linkage between functional networks and musical abilities is mediated by working memory processes, whereas structural networks influence these abilities through sensory integration. Our work lays the foundation for future investigations into the neurobiological roots of individual differences in musicality.

A Nanoparticle-Based Artificial Ear for Personalized Classification of Emotions in the Human Voice Using Deep Learning.

Artificial intelligence and human-computer interaction advances demand bioinspired sensing modalities capable of comprehending human affective states and speech. However, endowing skin-like interfaces with such intricate perception abilities remains challenging. Here, we have developed a flexible piezoresistive artificial ear (AE) sensor based on gold nanoparticles, which can convert sound signals into electrical signals through changes in resistance. By testing the sensor's performance at both frequency and sound pressure level (SPL), the AE has a frequency response range of 20 Hz to 12 kHz and can sense sound signals from up to 5 m away at a frequency of 1 kHz and an SPL of 126 dB. Furthermore, through deep learning, the device achieves up to 96.9% and 95.0% accuracy in classification and recognition applications for seven emotional and eight urban environmental noises, respectively. Hence, on one hand, our device can monitor the patient's emotional state by their speech, such as sudden yelling and screaming, which can help healthcare workers understand patients' condition in time. On the other hand, the device could also be used for real-time monitoring of noise levels in aircraft, ships, factories, and other high-decibel equipment and environments.

Improving Pedestrian Safety with Head-Up Display Warning in a Connected Environment

In this paper, the potential of using a head-up display (HUD) in the connected environment to improve a vehicle’s running comfort and pedestrian safety is tested, by providing warning information to drivers in advance. To achieve this objective, driving simulation technology is used to construct the connected environment and develop the HUD, and the effectiveness of the system is then tested. Specifically, thirty-four participants were recruited to conduct driving simulation experiments in six scenarios: three warning display types (Baseline/Head-down display/Head-up display) combined with two weather conditions (clear weather/foggy weather). The effects of the three different warning display types on braking risk-avoidance strategy were studied by comparing the drivers’ performance during the perception and decision stage (position of accelerator-pedal release, position of first braking), the risk-avoidance manipulation stage (maximum deceleration, braking distance) and the risk-avoidance result stage (minimum collision distance, position of minimum speed). The influences of weather conditions and driver attributes were also considered. When the HUD warnings were activated, drivers started to decelerate further away from pedestrians, with a more stable and moderate deceleration process and a greater safety margin between the vehicle and the pedestrians. Using HUD warnings in foggy conditions improved drivers’ perception and decision abilities, this study confirmed the great benefits that HUD warnings in the connected environment can bring to traffic safety, especially under risky situations and inclement weathers. The research results provide a reference for the more humanized and rationalized optimization design of these warning systems.

An online replication of the association between face perception abilities and the amount of visual information required to identify a face

An online replication of the association between face perception abilities and the amount of visual information required to identify a face

Tonotopic and Default Frequency Fitting for Music Perception in Cochlear Implant Recipients

Cochlear implants are an effective technique for enhancing speech perception abilities in quiet environments for people with severe to profound deafness. Nevertheless, complex sound signals perception, such as music perception, remains challenging for cochlear implant users. To assess the benefit of a tonotopic map on music perception in new cochlear implant users. A prospective, randomized, double-blind, 2-period crossover study including 26 new cochlear implant users was performed over a 6-month period (June 2021 to November 2021). An anatomical tonotopic map was created using postoperative flat-panel computed tomography and a reconstruction software based on Greenwood function. New cochlear implant users older than 18 years with bilateral severe to profound sensorineural hearing loss or complete hearing loss for less than 5 years were selected in the University Hospital Centre of Rennes in France. The trial was conducted from June to November 2021 (inclusion) and to February 2022 (end of the assessment procedure at 12 weeks postactivation), and the analysis itself was completed in December 2022. Each participant was randomized to receive a conventional map followed by a tonotopic map or vice versa. Participants performed pitch-scaling tasks (multidimensional qualitative assessment, melodic contour identification, melodic recognition test) after 6 weeks of each setting. Thirteen participants were randomized to each sequence. Two of the 26 participants recruited (1 in each sequence) had to be excluded due to the COVID-19 pandemic. The multidimensional qualitative assessment (Gabrielsson test), melodic contour identification, and melodic recognition scores were significantly higher with the tonotopic setting than the conventional one (mean effect [ME], 7.8; 95% CI, 5.0-10.5; ME, 12.1%; 95% CI, 5.7%-18.4%; ME, 14.4%, 95% CI, 8.5%-20.2%; and ME, 2.1, 95% CI, 1.7-2.5, respectively). Among the different dimensions evaluated by the Gabrielsson test, the mean scores for clarity, spaciousness, fullness, nearness, and total impression were significantly higher with tonotopic fitting. Ninety-two percent of the participants kept the tonotopy-based map after the study period. In this randomized clinical trial of patients with new cochlear implants, a tonotopic-based fitting was associated with better results in perception of complex sound signals such as music listening experience. ClinicalTrials.gov Identifier: NCT04922619.

Novel Clinical Manifestation and Favorable Treatment Outcome of Cochlear Implant in a Chinese Family With Likely Pathogenic Variant of the P2RX2 Gene.

The rapid development and clinical application of sequencing technologies enable the genetic diagnosis of inherited deafness. P2RX2, as the gene responsible for autosomal dominant non-syndromic deafness-41 (DFNA41), has been proven to be essential for life-long normal hearing and for the protection of noise-induced hearing loss (NIHL). Our present study reports a missense variant in the P2RX2 gene (c.178G > T (p.V60L)), for the second time worldwide, in a five-generation kindred living in Henan, China. Despite carrying the same variant, the affected members in this family appear to present with earlier-onset hearing loss and poorer hearing compared to the original DFNA41 families. In addition, this study supplements some content that was not covered in previous reports. We quantitatively evaluated the pain perception ability of some members using the Pain Vision PS-2100 system, and further found an interesting clinical manifestation, that is, hyperalgesia, in heterozygotes for P2RX2 p.V60L. The cochlear implant (CI) was also provided for the proband of profound deafness, resulting in satisfactory clinical outcomes. Finally, we carried out a systematic review of recently published articles on the P2RX2 gene, which is beneficial for better understanding the role of the P2RX2 gene in the auditory system and the pathogenic mechanisms in sensorineural hearing loss (SNHL).

Vestibular perceptual learning improves self-motion perception, posture, and gait in older adults

Vestibular motion perception declines with age, increasing the risk of falling substantially. We performed a two-week perceptual learning intervention using a self-motion direction discrimination task (2800 training trials per person) on a 6 degrees of freedom motion platform in healthy older adults (n = 40, aged 70–88 yr). Linear inter-aural and angular roll tilt vestibular thresholds improved with training (95% credible interval for pre/post difference), suggesting altered sensitivity post-training. Moreover, improved perceptual abilities transfer to actual posture (reduced sway) and gait parameters. Passive self-motion discrimination training provides a new and promising way to counteract age-related sensory decline. It can reduce the risk of falling, and thereby maintain individual autonomy and quality of life.

VIPASSANA- NAVIGATING TOWARDS MENTAL PEACE

Our saints and sages have consistently stressed the importance of attaining mental peace, believing in the innate self-healing capabilities of the human mind. Often characterized as the "Monkey Mind" due to its tendency to wander, the mind can be effectively managed by heightening internal awareness through observation and acceptance of things as they are. Vipassana, an ancient mindfulness meditation rooted in yoga, is gaining recognition in the 21st century as a comprehensive approach to mental health and well-being. It integrates fundamental metaphysical aspects of the mind and spirit with the body. Numerous studies have demonstrated that Vipassana practice leads to emotional stability, increased life satisfaction, sharper cognitive and perceptual abilities, indicative of positive psychological well-being and an improved quality of life (Anderson, 2000; Fleishman, 1999; Srinivasan, 1992; Inwongsakul et al., 2015). Various psychological researches have shown that Vipassana reduces instances of violence and crime, both towards oneself and others, while also promoting mental tranquility, resilience, and fostering qualities such as love, compassion, and forgiveness (Shapiro et al., 2005; Hart, 1987; Kochargaonkar, 2005). Hence, the objective of this study is to underscore Vipassana as a pathway to achieving mental peace.

Ecosystem Services and Public Perception of Green Infrastructure from the Perspective of Urban Parks: A Case Study of Luoyang City, China

As one form of urban green infrastructure, city parks provide essential ecosystem services and serve as a vital link connecting citizens with natural ecosystems. The public, as the primary beneficiaries of urban park ecosystem services, plays a crucial role in guiding the improvement and development trends of urban park planning. Most existing studies assess the comprehensive value of green spaces from a macro scale, with few focusing on the cultural and regulatory ecosystem services of urban parks and public perception of these services at the park level. This study targets 11 typical urban parks in Luoyang City, collecting 771 samples through field mapping and surveys. It analyzes users’ socio-demographic characteristics, perception abilities, usage preferences, and service satisfaction, linking these subjective factors with objective elements of the park environment, such as fitness and recreational facilities. The results indicate that respondents with knowledge of ecosystem services are better able to evaluate and understand services such as air pollution reduction and noise reduction in parks, while those without such knowledge tend to focus more on services that they can intuitively perceive, such as biodiversity conservation and microclimate regulation. Additionally, the middle-income group exhibits a higher level of awareness of park ecosystem services, which depends on their active social participation. The findings of this study suggest that enhancing public understanding of ecosystem services through environmental education is key to improving the quality of urban ecosystems.

Improved region proposal network for enhanced few-shot object detection

Despite significant success of deep learning in object detection tasks, the standard training of deep neural networks requires access to a substantial quantity of annotated images across all classes. Data annotation is an arduous and time-consuming endeavor, particularly when dealing with infrequent objects. Few-shot object detection (FSOD) methods have emerged as a solution to the limitations of classic object detection approaches based on deep learning. FSOD methods demonstrate remarkable performance by achieving robust object detection using a significantly smaller amount of training data. A challenge for FSOD is that instances from novel classes that do not belong to the fixed set of training classes appear in the background and the base model may pick them up as potential objects. These objects behave similarly to label noise because they are classified as one of the training dataset classes, leading to FSOD performance degradation. We develop a semi-supervised algorithm to detect and then utilize these unlabeled novel objects as positive samples during the FSOD training stage to improve FSOD performance. Specifically, we develop a hierarchical ternary classification region proposal network (HTRPN) to localize the potential unlabeled novel objects and assign them new objectness labels to distinguish these objects from the base training dataset classes. Our improved hierarchical sampling strategy for the region proposal network (RPN) also boosts the perception ability of the object detection model for large objects. We test our approach and COCO and PASCAL VOC baselines that are commonly used in FSOD literature. Our experimental results indicate that our method is effective and outperforms the existing state-of-the-art (SOTA) FSOD methods. Our implementation is provided as a supplement to support reproducibility of the results https://github.com/zshanggu/HTRPN.11Early partial results of this work is presented in the 2023 ICCV Workshop on Visual Continual Learning (Shangguan and Rostami, 2023).

Validation of the Pediatric Spatial Hearing Questionnaire.

Spatial hearing is necessary for adequate sound awareness and speech perception abilities; however, research indicates that children have difficulties on these spatial hearing tasks that affect functioning in their daily environment. The purpose of this study was to validate a pediatric version of the Spatial Hearing Questionnaire (P-SHQ) for determining binaural hearing benefits and spatial hearing ability in children. We recruited parents and guardians of 68 children ages kindergarten through eighth grade to participate. Parents completed the P-SHQ, the Speech, Spatial and Qualities of Hearing Scale-Parent version, and a demographic questionnaire. To determine the factor structure of the P-SHQ, we conducted an exploratory factor analysis and reliability was assessed by calculating correlation coefficients. Three factors emerged during factor analysis: Factor 1 = sound localization, Factor 2 = speech-in-noise perception, and Factor 3 = speech perception in quiet. The P-SHQ has good internal consistency reliability (α = .97), and high item-total correlations were found. The correlation between scores from the P-SHQ questionnaire and the SSQ-Spatial subscale questionnaire provides evidence for the construct validity of the P-SHQ. The P-SHQ is a reliable and valid questionnaire to assess spatial hearing ability in children. This quick-to-administer tool can be incorporated into audiological care to determine the spatial hearing skills of a child and assist in counseling, making it a valuable assessment for hearing health care professionals.

Fault diagnosis method for oil-immersed transformers integrated digital twin model

To address the problems of low accuracy in fault diagnosis of oil-immersed transformers, poor state perception ability and real-time collaboration during diagnosis feedback, a fault diagnosis method for transformers based on the integration of digital twins is proposed. Firstly, fault sample balance is achieved through Iterative Nearest Neighbor Oversampling (INNOS), Secondly, nine-dimensional ratio features are extracted, and the correlation between dissolved gases in oil and fault types is established. Then, sparse principal component analysis (SPCA) is used for feature fusion and dimensionality reduction. Finally, the Aquila Optimizer (AO) is introduced to optimize the parameters of the Kernel Extreme Learning Machine (KELM), establishing the optimal AO-KELM diagnosis model. The final fault diagnosis accuracy reaches 98.1013%. Combining transformer digital twin models, real-time interaction mapping between physical entities and virtual space is achieved, enabling online diagnosis of transformer faults. Experimental results show that the method proposed in this paper has high diagnostic accuracy and strong stability, providing reference for the intelligent operation and maintenance of transformers.

Don’t try to make me laugh, let me do: persuading employees to action

ABSTRACT As Computer-Mediated-Communication (CMC) gains prevalence in managerial practices, this study investigates the effect of managerial humor on employees’ perceptions of ability, motivation, and tendency to act. To date, the effect of humor on communication effectiveness remains unclear. Utilizing an experimental design with 923 participants, we compare humorous and non-humorous microcopy requests in CMC through a diff-in-diff regression analysis. Results show that non-humorous microcopy fosters higher motivation, action propensity, and perceived task execution ability compared to humorous microcopy. Perceived ability interacts with the other variables, yielding diverse contextual outcomes. This study reveals both the negative and positive effects of humor in CMC, providing insights for managers. The findings emphasize the importance of considering situational context and employees’ perceived task execution ability when choosing a persuasive approach in CMC requests, offering practical guidance for managers aiming to enhance communication with employees.

Effects of hazard type on the hazard perceptions of violation-involved and violation-free drivers: An ERP study

ObjectiveHazard perception, which is closely related to crash risk, is the ability to anticipate potential dangers on the road. This study aimed to investigate the effects of drivers’ previous traffic violation history on their neural processing of hazard perception under different hazard types. MethodsA mixed experimental design was employed, involving two groups of drivers (driver group: violation-involved, violation-free) and three types of images (no-hazard image, covert hazard image, overt hazard image). Twenty covert hazard images, 20 overt hazard images and 40 no-hazard images were randomly presented to 19 drivers with previous violation histories and 28 drivers without violations. The participants were asked to detect whether there was a hazardous situation in the images and press the keyboard quickly after detecting one. Response time, response accuracy and changes in the N1 (100–140 ms), N2 (250–300 ms) and P300 (300–400 ms) components of event-related potentials (ERP) were obtained and analysed. ResultsThe response time to the covert hazard images was significantly greater than that to the no-hazard and overt hazard images, and the lower accuracy also revealed that the covert hazard images were harder to detect than the no-hazard and overt hazard images. Under both overt and covert hazard images, the response time of violation-involved drivers was longer, and their accuracy was worse than that of violation-free drivers. The N1 latency of violation-involved drivers was significantly longer than that of violation-free drivers. The amplitude of N2 was significantly larger in the no-hazard image than in the covert and overt hazard images. The P300 amplitude was significantly greater in the overt hazard images than in the covert and no-hazard images. ConclusionViolation-involved drivers have poorer hazard perception abilities than violation-free drivers regardless of hazard type. N1 latency can differ from that of drivers with and without previous violations. These findings provide support for the development of hazard perception training systems for drivers who are prone to traffic violations.

Individual differences in the consistency of neural and behavioural responses to speech sounds

There are documented individual differences among adults in the consistency of speech sound processing, both at neural and behavioural levels. Some adults show more consistent neural responses to speech sounds than others, as measured by an event-related potential called the frequency-following response (FFR); similarly, some adults show more consistent behavioural responses to native speech sounds than others, as measured by two-alternative forced choice (2AFC) and visual analog scaling (VAS) tasks. Adults also differ in how successfully they can perceive non-native speech sounds. Interestingly, it remains unclear whether these differences are related within individuals. In the current study, native English-speaking adults completed native phonetic perception tasks (2AFC and VAS), a non-native (German) phonetic perception task, and an FFR recording session. From these tasks, we derived measures of the consistency of participants’ neural and behavioural responses to native speech as well as their non-native perception ability. We then examined the relationships among individual differences in these measures. Analysis of the behavioural measures revealed that more consistent responses to native sounds predicted more successful perception of unfamiliar German sounds. Analysis of neural and behavioural data did not reveal clear relationships between FFR consistency and our phonetic perception measures. This multimodal work furthers our understanding of individual differences in speech processing among adults, and may eventually lead to individualized approaches for enhancing non-native language acquisition in adulthood.

Voicing discrimination as a diagnostic marker of developmental language disorder

This study examines how children with developmental language disorder (DLD) discriminate voiced and voiceless consonants and their processing speed. It also explores the contribution of factors like age, nonverbal intelligence, vocabulary, morphosyntactic skills, and sentence repetition in explaining speech perception abilities. Fourteen Cypriot Greek children with DLD and 14 peers with typical development (TD) aged 7; 10–10; 4 were recruited. Children were divided into four groups based on age and condition: young-DLD, young-TD, old-DLD, and old-TD. All children participated in an AX task, which measured their ability to discriminate sounds and their processing speed. They also completed a nonverbal intelligence test and a DVIQ test, which provided measures of various language abilities. The results demonstrated that the young-DLD group exhibited lower performance in discriminating consonants compared to the young-TD group, while such differences were not observed between the old-DLD and old-TD groups. Furthermore, while no significant differences in processing time were found between the DLD and TD groups, both young DLD and TD groups displayed longer processing times compared to their older counterparts. Age was the best-contributing factor to speech perception abilities in children with DLD in contrast to morphosyntax and vocabulary for children with TD. These findings highlight the role of voicing discrimination as a diagnostic marker of DLD as opposed to reaction time. Moreover, they underscore the crucial role of age in detecting DLD. The language developmental trajectories of children with TD appear distinct from those with DLD, as evidenced by variations in contributing factors between the two groups. These disparities can be attributed to the diverse nature of the DLD population, the therapies they receive, the compensatory strategies they employ, and the potential impact of other contributing factors.

ECG arrhythmia classification based on the fast ant colony clustering algorithm with improved spatiotemporal feature perception ability

Electrocardiograph (ECG) is one of the most critical physiological signals used for arrhythmia diagnosis. In recent years, ECG arrhythmia classification devices consisting of multi-module sensors, clustering algorithms and neural networks play an important role in monitoring and diagnosing cardiovascular diseases. However, the commonly used ECG arrhythmia classification methods are still facing some problems such as the complex model structure and long running time. To address the above problems, this paper proposes an ECG arrhythmia classification method based on the fast ant colony clustering algorithm with improved spatiotemporal feature perception ability (SFP-FACC), which uses LSTM to fit the cluster centers and avoids the time consumption of updating the cluster centers during the classification process. The spatiotemporal feature perception ability of this model with the dynamic time warping (DTW) algorithm is improved. The classification is achieved by applying the combination of Euclidean distance and DTW. The convergence speed of the model is improved by using dynamic pheromone volatility coefficient; and finally the optimal solution of the model is determined by using radix sort. Based on the MIT-BIH arrhythmia dataset, the overall accuracy of the proposed classification method in this paper achieves 99.04 %, and even the accuracy of certain types of classification achieves 100 %, and the running time is about 3.5 times faster than that of the basic models. The experiments show that the method proposed in this paper has certain advantages.