Complex Actions Research Articles

Optimization method of task uninstallation in mobile edge computing environment combining improved deep Q-learning and transmission learning

Traditional task uninstallation methods are difficult to cope with the continuous changes in network environment and device status, therefore a more intelligent solution is urgently needed. This article studied the optimization method of task uninstallation in mobile edge computing (MEC) environment, and studied the optimization method of task uninstallation in combination with improved deep Q-learning and transmission learning (TL). Firstly, it provided an overview of the basic concepts and optimization problems of task uninstallation in MEC environments, and then delved into the application of improved deep Q-learning in task uninstallation optimization, namely the application of deep Q-network (DQN) in task uninstallation optimization. Next, this article designed the TL-DQN method by combining DQN and TL. This method utilizes the DQN algorithm to construct a model that can handle complex states and action spaces. Meanwhile, by introducing TL, the model can quickly transfer knowledge and achieve adaptive optimization of tasks in new environments, thereby improving the generalization ability and efficiency of the decision-making process. Experiments have shown that TL-DQN performs better in convergence speed compared to other algorithms. For delay sensitive users, the delay growth of TL-DQN algorithm is relatively slow. When the task volume reached 10, the delay of TL-DQN was 7.97 s, which was 1.99 s lower than reinforcement learning. When the number of unloading tasks increased to 500, the energy consumption of TL-DQN was 554 J, which was 282 J lower than the energy consumption required by the task uninstallation method constructed by reinforcement learning algorithms. The task uninstallation method studied in this article has achieved significant results in reducing latency, improving energy efficiency, and adapting to network dynamic changes, bringing users a more stable and reliable service experience.

Process tomography of structured optical gates with convolutional neural networks

Abstract Efficient and accurate characterization of an experimental setup is a critical requirement in any physical setting. In the quantum realm, the characterization of an unknown operator is experimentally accomplished via Quantum Process Tomography. This technique combines the outcomes of different projective measurements to reconstruct the underlying process matrix, typically extracted from maximum-likelihood estimation. Here, we exploit the logical correspondence between optical polarization and two-level quantum systems to retrieve the complex action of structured metasurfaces within a Quantum Process Tomography-inspired context. In particular, we investigate a deep-learning approach that allows for fast and accurate reconstructions of space-dependent SU(2) operators by only processing a minimal set of measurements. We train a convolutional neural network based on a scalable U-Net architecture to process entire experimental images in parallel. Synthetic processes are reconstructed with average fidelity above 90%. The performance of our routine is experimentally validated in the case of space-dependent polarization transformations acting on a classical laser beam. Our approach further expands the toolbox of data-driven approaches to Quantum Process Tomography and shows promise in the real-time characterization of complex optical gates.

Magnetochrome-catalyzed oxidation of ferrous iron by MamP enables magnetite crystal growth in the magnetotactic bacterium AMB-1

Magnetotactic bacteria have evolved the remarkable capacity to biomineralize chains of magnetite [Fe(II)Fe(III)2O4] nanoparticles that align along the geomagnetic field and optimize their navigation in the environment. Mechanisms enabling magnetite formation require the complex action of numerous proteins for iron acquisition, sequestration in dedicated magnetosome organelles, and precipitation into magnetite. The MamP protein contains c-type cytochromes called magnetochrome domains that are found exclusively in magnetotactic bacteria. Ablation of magnetochromes in MamP prevents bacteria from aligning with external magnetic fields, showing their importance to maintain this biological function. MamP has been proposed, mostly from in vitro experimentations, to regulate iron redox state and maintain an Fe(II)/Fe(III) balance compatible with magnetite formation via the iron oxidase activity of magnetochromes. To test the proposed function for MamP in vivo in the magnetotactic strain Magnetospirillum magneticum (AMB)-1, we characterized the iron species in chemical MamP-mediated magnetite syntheses as well as in bacteria unable to produce MamP using a combination of physicochemical methodologies. We show that MamP has no apparent control on the speciation and oxidation state of intracellular iron or on the Fe(II)/Fe(III) balance in magnetite. We propose that MamP promotes magnetite growth by incorporating Fe(III) into preexisting magnetite seeds and that magnetite structure and stoichiometry is maintained by further equilibration with dissolved Fe(II) in magnetosome organelles.

Enhancing human behavior recognition with dynamic graph convolutional networks and multi-scale position attention

PurposeHuman behavior recognition poses a pivotal challenge in intelligent computing and cybernetics, significantly impacting engineering and management systems. With the rapid advancement of autonomous systems and intelligent manufacturing, there is an increasing demand for precise and efficient human behavior recognition technologies. However, traditional methods often suffer from insufficient accuracy and limited generalization ability when dealing with complex and diverse human actions. Therefore, this study aims to enhance the precision of human behavior recognition by proposing an innovative framework, dynamic graph convolutional networks with multi-scale position attention (DGCN-MPA) to sup.Design/methodology/approachThe primary applications are in autonomous systems and intelligent manufacturing. The main objective of this study is to develop an efficient human behavior recognition framework that leverages advanced techniques to improve the prediction and interpretation of human actions. This framework aims to address the shortcomings of existing methods in handling the complexity and variability of human actions, providing more reliable and precise solutions for practical applications. The proposed DGCN-MPA framework integrates the strengths of convolutional neural networks and graph-based models. It innovatively incorporates wavelet packet transform to extract time-frequency characteristics and a MPA module to enhance the representation of skeletal node positions. The core innovation lies in the fusion of dynamic graph convolution with hierarchical attention mechanisms, which selectively attend to relevant features and spatial relationships, adjusting their importance across scales to address the variability in human actions.FindingsTo validate the effectiveness of the DGCN-MPA framework, rigorous evaluations were conducted on benchmark datasets such as NTU-RGB + D and Kinetics-Skeleton. The results demonstrate that the framework achieves an F1 score of 62.18% and an accuracy of 75.93% on NTU-RGB + D and an F1 score of 69.34% and an accuracy of 76.86% on Kinetics-Skeleton, outperforming existing models. These findings underscore the framework’s capability to capture complex behavior patterns with high precision.Originality/valueBy introducing a dynamic graph convolutional approach combined with multi-scale position attention mechanisms, this study represents a significant advancement in human behavior recognition technologies. The innovative design and superior performance of the DGCN-MPA framework contribute to its potential for real-world applications, particularly in integrating behavior recognition into engineering and autonomous systems. In the future, this framework has the potential to further propel the development of intelligent computing, cybernetics and related fields.

Design of an epidemic prevention and control bracelet system integrated with convolutional neural networks: Promote real-time physiological feedback and adaptive training in remote physical education

This study aims to design an epidemic prevention and control bracelet system that integrates convolutional neural network (CNN). This system can collect and process the user’s physiological index data in real time, especially in the remote physical education scene, and provide learners with immediate physiological index feedback and personalized adaptive training suggestions through accurate human action recognition (HAR) technology. One-dimensional acceleration signal is converted into two-dimensional image, and CNN’s powerful feature extraction and classification ability is used to effectively solve the problem that manual feature extraction is complex and nonlinear features are difficult to capture. By considering the joint action trajectory in the time window, a dynamic Recurrence Plot (RP) is constructed to capture the dynamic changes among joints. To input recursive graph data into CNN, it needs to be converted into image form. In the task of HAR, CNN can automatically learn useful features from images without manually designing features. It can not only effectively extract features from images, but also be directly used in classification tasks. Experimental results show that compared with other algorithms, the proposed RP + CNN model has the best performance in action recognition, with an accuracy of 96.89% and a F1 value of 86.76%. RP captures the dynamic patterns and periodic behaviors in time series by visualizing the repeated appearance of system states over time. The RP + CNN model is used to extract and classify human action features, which significantly improves the accuracy and efficiency of HAR. This innovative method not only simplifies the complex process of traditional manual feature extraction, but also enhances the system’s ability to identify nonlinear and complex action patterns, which provides strong technical support for remote physical education.

Electrophysiological indexes of the cognitive-motor trade-off associated with motor response complexity in a cognitive task

Complex actions require more cognitive and motor control than simple ones. Literature shows that to face complexity, the brain must make a compromise between available resources usually giving priority to motor control. However, literature has minimally explored the effect of the motor response complexity on brain processing associated with cognitive tasks. Consequently, it is unknown whether carrying out a cognitive task requiring motor responses of increasing complexity could reduce cognitive processing keeping stable motor control. Therefore, this study aims to investigate possible modulations exerted by increasing motor response complexity in a cognitive task on brain processing. To this aim, we analyzed the event-related potentials and behavioral responses during a cognitive task with increasing complexity of the required motor response (keypress, reaching and stepping). Results showed the increasing motor complexity enhances early visual and attentional processing (P1 and N1 components) but reduces the late post-perceptual cognitive control (P3 component). Additionally, we found a component following the P3 which was specific for stimuli requiring a response. This component, labeled N750, increased amplitude along with the response motor complexity. Behaviorally, response accuracy was not affected by complexity. Results indicated that in cognitive tasks stimulus processing is affected by the complexity of the motor response. Complex responses require a greater investment of early perceptual and attentional resources, but at late phases of processing, cognitive resources are less available in favor of motor resources. This confirms the idea of the motor-priority cognitive-motor trade-off of the brain.

SERCA Modulators Reveal Distinct Signaling and Functional Roles of T Lymphocyte Ca2+ Stores.

The allosteric SERCA (Sarcoplasmic/Endoplasmic Reticulum Ca2+-ATPase) activator CDN1163 has been recently added to the group of pharmacological tools for probing SERCA function. We chose to investigate the effects of the compound on T lymphocyte Ca2+ stores, using the well-described Jurkat T lymphocyte as a reliable cell system for Ca2+ signaling pathways. Our study identified the lowest concentrations of the SERCA inhibitors thapsigargin (TG) and 2,5-di-(tert butyl)-1,4-benzohydroquinone (tBHQ) capable of releasing Ca2+, permitting the differentiation of the TG-sensitive SERCA 2b Ca2+ store from the tBHQ-sensitive SERCA 3 Ca2+ store. We proceeded to test the effects of CDN1163 on Ca2+ stores, examining specific actions on the SERCA 2b and SERCA 3 Ca2+ pools using our low-dose SERCA blocker regimen. In contrast to previous work, we find CDN1163 exerts complex time-sensitive and SERCA isoform-specific actions on Ca2+ stores. Surprisingly, short-term exposure (0-30 min) to CDN1163 perturbs T cell Ca2+ stores by suppressing Ca2+ uptake with diminished Ca2+ release from the SERCA 2b-controlled store. Concomitantly, we find evidence for a SERCA-activating effect of CDN1163 on the SERCA-3 regulated store, given the observation of increased Ca2+ release inducible by low-dose tBHQ. Intriguingly, longer-term (>12 h) CDN1163 exposure reversed this pattern, with increased Ca2+ release from SERCA 2b-regulated pools yet decreased Ca2+ release responses from the tBHQ-sensitive SERCA 3 pool. Indeed, this remodeling of SERCA 2b Ca2+ stores with longer-term CDN1163 exposure also translated into the compound's ability to protect Jurkat T lymphocytes from TG but not tBHQ-induced growth suppression.

Incremental and Radical Innovations that Contribute to the Survival of Small Businesses in Environments of Crisis

This paper examines the influence of incremental and radical innovations on the survival capacity of micro and small companies (MSE) in contexts of economic crisis.State of the art on innovation points to a lack of a specific methodology to segregate incremental and radical innovation in MSEs. Identifying such a method allows for action to promote innovation in such more assertive companies, contributing to the survival of companies in the most adverse moments, such as crises.In this sense, a methodology was developed to identify the type and degree of innovation based on the level of innovation in the sector of activity and was applied to a sample of 60 companies in the textile retail sector. The dependent variable was business survival, measured by the continuity or cessation of activity in 2020. The estimate of the probability of survival was carried out using a binary logistic regression model.The dimensions of incremental innovation, which are the most common in the business environment, did not significantly affect survival. The dimensions of radical innovation, which imply deeper and more disruptive changes in the business model, had a positive impact. significant impact on survival in all cases. The most complex actions in the Solutions dimension increased the chances of survival by 42%; improving Processes by 39%; and innovation in the Square by 38%. These findings suggest that small businesses that adopt radical innovations have greater adaptability and resilience to adverse situations.

Iron-molybdenum cofactor synthesis by a thermophilic nitrogenase devoid of the scaffold NifEN

The maturation and installation of the active site metal cluster (FeMo-co, Fe7S9CMo-R-homocitrate) in Mo-dependent nitrogenase requires the protein product of the nifB gene for production of the FeS cluster precursor (NifB-co, [Fe8S9C]) and the action of the maturase complex composed of the protein products from the nifE and nifN genes. However, some putative diazotrophic bacteria, like Roseiflexus sp. RS-1, lack the nifEN genes, suggesting an alternative pathway for maturation of FeMo-co that does not require NifEN. In this study, the Roseiflexus NifH, NifB, and apo-NifDK proteins produced in Escherichia coli are shown to be sufficient for FeMo-co maturation and insertion into the NifDK protein to achieve active nitrogenase. The E. coli expressed NifDKRS contained P-clusters but was devoid of FeMo-co (referred to as apo-NifDKRS). Apo-NifDKRS could be activated for N2 reduction by addition of preformed FeMo-co. Further, it was found that apo-NifDKRS plus E. coli produced NifBRS and NifHRS were sufficient to yield active NifDKRS when incubated with the necessary substrates (homocitrate, molybdate, and S-adenosylmethionine [SAM]), demonstrating that these proteins can replace the need for NifEN in maturation of Mo-nitrogenase. The E. coli produced NifHRS and NifBRS proteins were independently shown to be functional. The reconstituted NifDKRS demonstrated reduction of N2, protons, and acetylene in ratios observed for Azotobacter vinelandii NifDK. These findings reveal a distinct NifEN-independent pathway for nitrogenase activation involving NifHRS, NifBRS, and apo-NifDKRS.

Practical skills taught in Scandinavian nursing education simulation centres: a cross-sectional survey

ABSTRACT Practical nursing skills are complex actions that must be taught in bachelor nursing education. There is a lack of comparative research about what simulation-based skills are taught in Scandinavian countries, and this study aims to fill this gap. In 2019, a cross-sectional survey of nursing educators was conducted, and data were analysed using descriptive and non-parametric tests. The study revealed that eight categories of simulation-based practical skills (respiration, activity, elimination, nutrition, skin care, circulation, hygiene, and medication) were taught in all Scandinavian countries, but there were differences in what skills were prioritised within each category. Moreover, the study revealed that the amount of time spent on teaching simulation-based practical skills varied across countries and faculties, with Norway and Sweden spending more time teaching simulation-based practical skills than Denmark. The authors recommend that nursing education prioritise and standardise the practical skills taught in simulation centres in all Scandinavian countries.

Adults and Children Engage in Subtle and Fine-Grained Action Interpretation and Evaluation in Moral Dilemmas.

Understanding the actions of others is fundamental for human social life. It builds on a grasp of the subjective intentionality behind behavior: one action comprises different things simultaneously (e.g., moving their arm, turning on the light) but which of these constitute intentional actions, in contrast to merely foreseen side-effects (e.g., increasing the electricity bill), depends on the description under which the agent represents the acts. She may be acting intentionally only under the description "turning on the light," but did not turn on the light in order to increase the electricity bill. In preregistered studies (N = 620), we asked how adults and children engage in such complex subjective action interpretation and evaluation in moral dilemmas. To capture the deep structure of subjects' representations of the intentional structures of actions, we derived "act trees" from their response patterns to questions about the acts. Results suggest that people systematically distinguish between intended main and merely foreseen side-effects in their moral and intentionality judgments, even when main and side-effects were closely related and the latter were harmful. Additional experimental conditions suggest that, when given ambiguous information, the majority of subjects assume that agents act with beneficial main intentions. This "good intention prior" was so strong that participants attributed good intentions even when the harmful action was no longer necessary to resolve the dilemma (Study 2). These methods provide promising new ways to investigate in more subtle and fine-grained ways how reasoners parse, interpret, and evaluate complex actions.

Introduction to latent variable energy-based models: a path toward autonomous machine intelligence

Abstract Current automated systems have crucial limitations that need to be addressed before artificial intelligence can reach human-like levels and bring new technological revolutions. Among others, our societies still lack level-5 self-driving cars, domestic robots, and virtual assistants that learn reliable world models, reason, and plan complex action sequences. In these notes, we summarize the main ideas behind the architecture of autonomous intelligence of the future proposed by Yann LeCun. In particular, we introduce energy-based and latent variable models and combine their advantages in the building block of LeCun’s proposal, that is, in the hierarchical joint-embedding predictive architecture.

Novel plasmon-coupled Ag/Ag2O nano-particles to downregulate β-catenin pathway in triple negative breast cancer

We prepared novel plasmon-coupled silver (Ag) and silver oxide (Ag2O) nanoparticles using green synthesis and a magnetic stirrer device from lemon juice. Firstly, we prepared six Ag samples from green tea and lemon juice, characterized by XRD, FTIR, UV, and HRTEM. We selected just two samples to apply to MDA-MB-231 cells. The samples entered the cell through endocytosis, showed moderate cytotoxicity and ROS levels, caused cell growth arrest at the G2M phase, exhibited higher inhibition of Cyclin D1, and induced early apoptosis. β-Catenin is an abundant protein in triple-negative breast cancer TNBC. Both samples showed inhibition of the β-catenin proteins pathway. Plasmon-coupled nanoparticles effectively inhibited β-catenin, physically capturing β-catenin and its pathway proteins, mimicking the action of a degradation protein complex due to their geometric properties. The prepared materials could be considered a promising treatment for TNBC that has not responded to chemotherapy and radiotherapy. As they have low toxicity to normal cells; this paves the way for new material designs without strict size limitations.

Endosomal membrane budding patterns in plants

Multivesicular endosomes (MVEs) sequester membrane proteins destined for degradation within intralumenal vesicles (ILVs), a process mediated by the membrane-remodeling action of Endosomal Sorting Complex Required for Transport (ESCRT) proteins. In Arabidopsis, endosomal membrane constriction and scission are uncoupled, resulting in the formation of extensive concatenated ILV networks and enhancing cargo sequestration efficiency. Here, we used a combination of electron tomography, computer simulations, and mathematical modeling to address the questions of when concatenated ILV networks evolved in plants and what drives their formation. Through morphometric analyses of tomographic reconstructions of endosomes across yeast, algae, and various land plants, we have found that ILV concatenation is widespread within plant species, but only prevalent in seed plants, especially in flowering plants. Multiple budding sites that require the formation of pores in the limiting membrane were only identified in hornworts and seed plants, suggesting that this mechanism has evolved independently in both plant lineages. To identify the conditions under which these multiple budding sites can arise, we used particle-based molecular dynamics simulations and found that changes in ESCRT filament properties, such as filament curvature and membrane binding energy, can generate the membrane shapes observed in multiple budding sites. To understand the relationship between membrane budding activity and ILV network topology, we performed computational simulations and identified a set of membrane remodeling parameters that can recapitulate our tomographic datasets.

Anti-fatigue effect of an enzymatically derived deer velvet extract through muscle damage recovery and improvement of antioxidant levels

Ethnopharmacological relevanceDeer velvet (DV) has been extensively used in traditional Oriental medicine to treat various diseases. Its pharmacological spectrum encompasses tonicity, longitudinal bone growth of adolescence, blood retention, hemopoiesis facilitation, enhancement of organ function, physical function improvement, and augmentation of physical vitality. Among its myriad effects, DV notably exhibits anti-fatigue properties; however, its specific mode of action is yet to be fully elucidated. Aim of the studyThis study was undertaken to investigate the anti-fatigue and exercise performance-enhancing effects of YC-1101(HENKIV®), an enzymatically derived DV extract, in C2C12 cell lines and forced swimming mouse models. Materials and methodsThe effect of YC-1101 on increasing cell growth and lowering lactate dehydrogenase (LDH) activity was assessed in C2C12. The antioxidative effects of YC-1101 and its mechanistic underpinnings were also evaluated in C2C12 cells. Moreover, mice were subjected to an exhaustive swimming test subsequent to 3 weeks of YC-1101 extract administration. Fatigue-associated biochemical parameters, such as LDH activity and lactate, superoxide dismutase, glutathione peroxidase, and malondialdehyde levels, were measured in serum and muscle tissues. ResultsYC-1101 significantly promoted myoblast growth and reduced LDH activity, indicative of a cell-proliferative effect. Notably, free radical scavenging assays and analysis of reactive oxygen species production and antioxidant-related mRNA expression corroborated the significant involvement of YC-1101 in antioxidation, an important mechanism in anti-fatigue processes. Furthermore, animal experiments demonstrated prolonged swimming endurance and inhibition of muscle LDH accumulation in forced swimming mouse models. Serum biochemical analysis further revealed significant modulation of the expression of anti-fatigue-related biomarkers. Various bioactive low-molecular-weight DV peptides were enriched in YC-1101 compared to YHC-BE-2038 (a DV extract without enzymatic digestion). The anti-fatigue effect of YC-1101 was significantly stronger than that of YHC-BE-2038. ConclusionsThese findings suggest the potential of YC-1101 as a nutraceutical adjunct in ameliorating fatigue, concurrently facilitating muscle damage recovery, and exerting antioxidant effects via the nuclear factor E2-related factor 2-Kelch-like ECH-associated protein-1 pathway. It can be assumed that the complex action of low-molecular-weight DV peptides produced during the enzymatic degradation process was effective, and the further research is needed.

The Nash problem for torus actions of complexity one

Abstract We solve the equivariant generalized Nash problem for any non-rational normal variety with torus action of complexity one. Namely, we give an explicit combinatorial description of the Nash order on the set of equivariant divisorial valuations on any such variety. Using this description, we positively solve the classical Nash problem in this setting, showing that every essential valuation is a Nash valuation. We also describe terminal valuations and use our results to answer negatively a question of de Fernex and Docampo by constructing examples of Nash valuations which are neither minimal nor terminal, thus illustrating a striking new feature of the class of singularities under consideration.

No need to execute: Omitted responses still yield response-response binding effects.

In the literature on human action control, the binding and retrieval of responses are assumed to shape the coordination of more complex actions. Specifically, the consecutive execution of two responses is assumed to result in their integration into cognitive representations (so-called event files) and can be retrieved from that upon later response repetition, thereby influencing behavior. Against the background of ideomotor theory and more recent theorizing in the binding and retrieval in action control framework (Frings et al., 2020), we investigated whether response execution is necessary for binding and retrieval of responses. We manipulated whether the retrieving response (Experiment 1), as well as the to-be-bound response (Experiment 2), is executed or omitted. The results showed that responses do not need to be executed to retrieve other responses or to be bound to other responses. Apparently, activating the cognitive representation of a response sufficed for this response to trigger event file binding and retrieval. Our results are the first to show that response-response binding is not dependent on executing responses. Together, the results support the core assumptions of ideomotor theory and the binding and retrieval in action control framework, namely a common coding of action and perception. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Optimal iterative learning PI controller for SISO and MIMO processes with machine learning validation for performance prediction

The multivariable process plays a significant role in industrial applications, and designing a controller for the Multi-Input Multi-Output process is challenging due to dynamic process changes and interactions between system variables. Traditionally, the PI family of controllers has been used for its simple design, easy tuning, and quick deployment. However, these processes require complex control actions due to multiple loops in process plants. Thus, this paper proposes an Iterative Learning Controller Dead-time compensating PI, which utilizes the newly developed hybrid Simulated Annealing-Ant Lion Optimization algorithm for Single-Input Single-Output process simulation and real-time experimentation on the Quadruple Tank System. To validate the effectiveness of the developed controller, Machine Learning techniques such as regression and ensemble trees are used to accurately predict the actual system response using error values from respective processes. The simulation and experimental results demonstrate that the proposed controller achieved better performance. The regression and ensemble tree algorithm models effectively predicted the actual response. The obtained data shows that the proposed controller improved system stability and robustness by minimizing nearly half of the overshoot and improving settling time, with an average of 29.9596% faster than the other controller in the SISO process and 14.6116% in the MIMO process.

Construction, evidence of validity and reliability of a Teacher Socioemotional Competencies Scale - ECSED

Social-emotional competencies are complex actions that include skills, knowledge and attitudes to maintain effective and satisfactory social interactions. As well as, managing one's own and other people's emotions in different environments. They are vital for healthy coexistence in higher education. The purpose of this research was to establish the evidence of validity and reliability for the scale of socioemotional competencies of university teachers. Scale constructed under the pentagonal model approach of Bizquerra and Pérez (2007). The study is cross-sectional and instrumental. A non-probabilistic convenience sample of 237 teachers from public and private universities in Lima (Peru), from different professional careers, aged between 24 and 70 years, male and female, was used. Satisfactory evidence of content-based validity was found for the criteria of clarity, pertinence, and relevance for each of the items using Aiken's V technique. For validity based on the internal structure of the construct, a Penta dimensional model and a bi-factor model were contrasted by confirmatory factor analysis, with better fit values for the second model (CFI and TLI > 0.95, RMSEA and SRMR < 0.07, = ω h = 0.90, ECV and PUC > 0.95), and favorable evidence of convergent and discriminant validity was also found. For both the overall score and the scores for each factor, there were coefficients denoting high reliability (ordinal α and ω > 0.90).

Differences in biological motion perception associated with hearing status and age of signed language exposure.

This study investigates how American Sign Language (ASL) fluency and hearing status influence the perception of biological motion, using three point-light display (PLD) tasks. Prior research indicates that early exposure to ASL among deaf signers results in more rapid and effortless recognition of biological motion than hearing nonsigners, potentially due to the expertise in deciphering complex human movements or possibly due to neuroplasticity in deaf brains. However, it remains uncertain whether this advantage stems from signed language proficiency or the experience of being deaf. To explore this, we designed three PLD tasks involving viewing randomly moving dots, identifying a person from biological motion PLDs, and determining whether right-side up and inverted PLDs depict actions involving a ball. A diverse cohort of participants (N = 224) with varying ASL fluencies and hearing statuses completed the tasks online, providing us with reaction time and accuracy data. Our results demonstrate that earlier ASL exposure is associated with accuracy, especially on complex action identification tasks. Furthermore, we discovered robust evidence for a speed-accuracy trade-off in deaf participants, in which they performed more quickly but less accurately. The speed-accuracy trade-off was evident in the most difficult task, the action identification task. Further analysis of this deaf group revealed that earlier signed language acquisition led to higher accuracy in action identification task. We conclude that age of ASL exposure and hearing status both significantly contribute to variations in biological motion perception, with implications for understanding visual expertise and cognitive processing in both deaf and signing populations. (PsycInfo Database Record (c) 2024 APA, all rights reserved).