Psychometric Properties of the Spanish Clance Impostor Scale (S-CIPS) / Propiedades psicométricas de la adaptación española de la Clance Impostor Phenomenon Scale (S-CIPS)

El objetivo del presente estudio fue el de examinar la fiabilidad, validez y estructura factorial de la adaptación española de la Clance Impostor Phenomenon Scale (CIPS). Para ello, un total de 271 estudiantes españoles completaron una versión traducida de la escala original de 20 ítems. En nuestra muestra, el instrumento mostró una alta fiabilidad, medida como consistencia interna, (ωTotal =.90) y correlaciones moderadas-altas con medidas de depresión (r =.633), autoestima (r = -.754) y miedo a las evaluaciones negativas (r = .666), lo cual sugiere tanto una validez nomológica como discriminante. Aunque en la validación original se propuso una estructura de tres factores, otros estudios han encontrado ajuste a estructuras de uno y dos factores. Aquí, utilizamos un análisis factorial confirmatorio (AFC) para probar el ajuste de estos tres modelos. Nuestros resultados muestran que, en la adaptación a español, el modelo con dos factores es el preferido. Esta adaptación al español de la CIPS provee a los profesionales clínicos una de una nueva herramienta para poder investigar los mecanismos que subyacen al síndrome del impostor, así como futuros tratamientos.(AU)

The aim of this study was to examine the reliability, validity, and factorial structure of the Spanish version of the Clance Impostor Phenom-enon Scale (CIPS). A sample of 271 Spanish students was recruited to complete a translated version of the original 20-item CIPS. In our sample, the instrument showed high internal consistency reliability (ωTotal=.90) and a moderate-to-strong correlation with measures of depression (r= .633), self-esteem (r= -.754) and fear of negative evaluation (r= .666), suggesting both nomological and discriminant validity. Althoughthe original valida-tion of the CIPS proposed a factorial structure with three factors, subse-quent validations also revealed adjustment to two-and one-factor struc-tures. Here, we used confirmatory factor analysis (CFA) to test the three different models. The results showed that in our adaptation, a 2-factor structure might be preferred. This adaptation of the CIPS to Spanish pro-vides clinicians with a new method to gain insight into the psychological mechanisms behind the Impostor phenomenon and suitable treatments.(AU)

[Research practice and development direction of intelligent manufacturing of presonalized traditional Chinese medicine preparations].

In recent years, as people's living standards continue to improve, and the pace of life accelerates dramatically, the demand and quality of traditional Chinese medicine(TCM) services from patients continue to rise. As an essential supplement to the existing forms of TCM application, such as Chinese patent medicine, decoction, and formulated granules, presonalized TCM preparations is facing an increasing market demand. Currently, manual and semi-mechanized production are the primary production ways in presonalized TCM preparations. However, the production process control level is low, and digitalization and informatization need to be improved, which restricts the automated and intelligent development of presonalized TCM preparations. Presonalized TCM preparations faces a significant opportunity and challenge in integrating with intelligent manufacturing through research and development of intelligent equipment and core technology. This paper overviews the connotation and characteristics of intelligent manufacturing and summarizes the application of intelligent manufacturing technologies such as "Internet of things" "big data", and "artificial intelligence" in the TCM industry. Based on the innovative research and development model of "intelligent classification of TCM materials, intelligent decision making of prescription and process, and online control and intelligent production" of presonalized TCM preparations, the research practice and achievements from our research group in the field of intelligent manufacturing of presonalized TCM preparations are introduced. Ultimately, the paper proposes the direction for developing intelligent manufacturing of presonalized TCM preparations, which will provide a reference for the research and application of automation and intelligence of presonalized TCM preparations.

Recent Progress in Wearable Near-Sensor and In-Sensor Intelligent Perception Systems.

As the Internet of Things (IoT) becomes more widespread, wearable smart systems will begin to be used in a variety of applications in people's daily lives, not only requiring the devices to have excellent flexibility and biocompatibility, but also taking into account redundant data and communication delays due to the use of a large number of sensors. Fortunately, the emerging paradigms of near-sensor and in-sensor computing, together with the proposal of flexible neuromorphic devices, provides a viable solution for the application of intelligent low-power wearable devices. Therefore, wearable smart systems based on new computing paradigms are of great research value. This review discusses the research status of a flexible five-sense sensing system based on near-sensor and in-sensor architectures, considering material design, structural design and circuit design. Furthermore, we summarize challenging problems that need to be solved and provide an outlook on the potential applications of intelligent wearable devices.

Research on the Vision-Based Dairy Cow Ear Tag Recognition Method.

With the increase in the scale of breeding at modern pastures, the management of dairy cows has become much more challenging, and individual recognition is the key to the implementation of precision farming. Based on the need for low-cost and accurate herd management and for non-stressful and non-invasive individual recognition, we propose a vision-based automatic recognition method for dairy cow ear tags. Firstly, for the detection of cow ear tags, the lightweight Small-YOLOV5s is proposed, and then a differentiable binarization network (DBNet) combined with a convolutional recurrent neural network (CRNN) is used to achieve the recognition of the numbers on ear tags. The experimental results demonstrated notable improvements: Compared to those of YOLOV5s, Small-YOLOV5s enhanced recall by 1.5%, increased the mean average precision by 0.9%, reduced the number of model parameters by 5,447,802, and enhanced the average prediction speed for a single image by 0.5 ms. The final accuracy of the ear tag number recognition was an impressive 92.1%. Moreover, this study introduces two standardized experimental datasets specifically designed for the ear tag detection and recognition of dairy cows. These datasets will be made freely available to researchers in the global dairy cattle community with the intention of fostering intelligent advancements in the breeding industry.

Towards secure and efficient integration of blockchain and 6G networks.

The future of communication systems is undergoing a transformative shift towards intelligence, efficiency, and flexibility. Presently, the amalgamation of blockchain technology and the sixth-generation mobile communication network (6G) has garnered significant attention, as their fusion is poised to profoundly impact the digital economy and society at large. However, the convergence of blockchain and 6G networks poses challenges pertaining to security and performance. In this article, we propose an approach based on the design of secure mechanisms and performance optimization to delve into the key issues surrounding the integration of blockchain and 6G networks from both security and performance perspectives. Specifically, we first introduce the application scenarios of 6G networks and blockchain's empowerment of them to highlight the necessity of combining blockchain technology with 6G. Subsequently, in order to ensure the security of communication and data transmission between blockchain and 6G networks, we have investigated the design requirements for security mechanisms. Furthermore, we discuss the efficient realization of the amalgamation between blockchain and 6G networks by proposing a solution based on Directed Acyclic Graph (DAG) for blockchain's asynchronous consensus protocol, alongside optimization strategies for storage and communication to meet the desired characteristics and requirements of 6G networks. Lastly, we provide valuable research directions that serve as references and guidance for the future development of the integration between blockchain and 6G networks.

Epidemic intelligence in Europe: a user needs perspective to foster innovation in digital health surveillance.

BACKGROUND: European epidemic intelligence (EI) systems receive vast amounts of information and data on disease outbreaks and potential health threats. The quantity and variety of available data sources for EI, as well as the available methods to manage and analyse these data sources, are constantly increasing. Our aim was to identify the difficulties encountered in this context and which innovations, according to EI practitioners, could improve the detection, monitoring and analysis of disease outbreaks and the emergence of new pathogens. METHODS: We conducted a qualitative study to identify the need for innovation expressed by 33 EI practitioners of national public health and animal health agencies in five European countries and at the European Centre for Disease Prevention and Control (ECDC). We adopted a stepwise approach to identify the EI stakeholders, to understand the problems they faced concerning their EI activities, and to validate and further define with practitioners the problems to address and the most adapted solutions to their work conditions. We characterized their EI activities, professional logics, and desired changes in their activities using NvivoⓇ software. RESULTS: Our analysis highlights that EI practitioners wished to collectively review their EI strategy to enhance their preparedness for emerging infectious diseases, adapt their routines to manage an increasing amount of data and have methodological support for cross-sectoral analysis. Practitioners were in demand of timely, validated and standardized data acquisition processes by text mining of various sources; better validated dataflows respecting the data protection rules; and more interoperable data with homogeneous quality levels and standardized covariate sets for epidemiological assessments of national EI. The set of solutions identified to facilitate risk detection and risk assessment included visualization, text mining, and predefined analytical tools combined with methodological guidance. Practitioners also highlighted their preference for partial rather than full automation of analyses to maintain control over the data and inputs and to adapt parameters to versatile objectives and characteristics. CONCLUSIONS: The study showed that the set of solutions needed by practitioners had to be based on holistic and integrated approaches for monitoring zoonosis and antimicrobial resistance and on harmonization between agencies and sectors while maintaining flexibility in the choice of tools and methods. The technical requirements should be defined in detail by iterative exchanges with EI practitioners and decision-makers.

Relationship between cultural intelligence and career and work adaptability in nursing students.

Objectives: To investigate the relationship between cultural intelligence and career and work adaptability among nursing students. METHODS: The descriptive, cross-sectional study was conducted at Kilis 7 Aralik University Nursing Department in Turkey from April to May 2019, and comprised nursing students of either gender. Data was gathered using Cultural Intelligence Scale and Career and Work Adaptability Questionnaire. Data was analysed using SPSS24. RESULTS: Of the 277 subjects, 162(58.5%) were females and 115(41.5%) were males. The overall mean age was 21.21±1.81 years. The mean Cultural Intelligence Scale score was 95.17±18.16. The mean Career and Work Adaptability Questionnaire score was 115.69±19.38. There was a positive correlation between the total scores and subscale scores of both the scales (r=598, p<0.001). The student's father's occupation, desire to work overseas, feeling like a good fit for nursing, and feeling prepared for professional life significantly affected cultural intelligence (p<0.05). The student's father's occupation significantly affected career and work adaptability (p=0.001). Conclusion: There was a positive correlation between the total scores and subscale scores of Cultural Intelligence Scale and Career and Work Adaptability Questionnaire.

Irrigation intelligence-enabling a cloud-based Internet of Things approach for enhanced water management in agriculture.

Advanced sensor technology, especially those that incorporate artificial intelligence (AI), has been recognized as increasingly important in various contemporary applications, including navigation, automation, water under imaging, environmental monitoring, and robotics. Data-driven decision-making and higher efficiency have enabled more excellent infrastructure thanks to integrating AI with sensors. The agricultural sector is one such area that has seen significant promise from this technology using the Internet of Things (IoT) capabilities. This paper describes an intelligent system for monitoring and analyzing agricultural environmental conditions, including weather, soil, and crop health, that uses internet-connected sensors and equipment. This work makes two significant contributions. It first makes it possible to use sensors linked to the IoT to accurately monitor the environment remotely. Gathering and analyzing data over time may give us valuable insights into daily fluctuations and long-term patterns. The second benefit of AI integration is the remote control; it provides for essential activities like irrigation, pest management, and disease detection. The technology can optimize water usage by tracking plant development and health and adjusting watering schedules accordingly. Intelligent Control Systems (Matlab/Simulink Ver. 2022b) use a hybrid controller that combines fuzzy logic with standard PID control to get high-efficiency performance from water pumps. In addition to monitoring crops, smart cameras allow farmers to make real-time adjustments based on soil moisture and plant needs. Potentially revolutionizing contemporary agriculture, this revolutionary approach might boost production, sustainability, and efficiency.

AttGRU-HMSI: enhancing heart disease diagnosis using hybrid deep learning approach.

Heart disease is a major global cause of mortality and a major public health problem for a large number of individuals. A major issue raised by regular clinical data analysis is the recognition of cardiovascular illnesses, including heart attacks and coronary artery disease, even though early identification of heart disease can save many lives. Accurate forecasting and decision assistance may be achieved in an effective manner with machine learning (ML). Big Data, or the vast amounts of data generated by the health sector, may assist models used to make diagnostic choices by revealing hidden information or intricate patterns. This paper uses a hybrid deep learning algorithm to describe a large data analysis and visualization approach for heart disease detection. The proposed approach is intended for use with big data systems, such as Apache Hadoop. An extensive medical data collection is first subjected to an improved k-means clustering (IKC) method to remove outliers, and the remaining class distribution is then balanced using the synthetic minority over-sampling technique (SMOTE). The next step is to forecast the disease using a bio-inspired hybrid mutation-based swarm intelligence (HMSI) with an attention-based gated recurrent unit network (AttGRU) model after recursive feature elimination (RFE) has determined which features are most important. In our implementation, we compare four machine learning algorithms: SAE + ANN (sparse autoencoder + artificial neural network), LR (logistic regression), KNN (K-nearest neighbour), and naïve Bayes. The experiment results indicate that a 95.42% accuracy rate for the hybrid model's suggested heart disease prediction is attained, which effectively outperforms and overcomes the prescribed research gap in mentioned related work.

Influencing factors and countermeasures on intelligent transformation and upgrading of logistics firms: A case study in China.

This study explores the influencing factors on intelligent transformation and upgrading of China's logistics firms under smart logistics, and designs the corresponding framework to guide the practice of firms. By analyzing the characteristics of smart logistics and the transformation and upgrading needs of traditional logistics, from the micro perspective of logistics firms, this paper constructs influencing factor index system of smart transformation and development from four dimensions: logistics technology innovation, logistics big data sharing, logistics management upgrading and logistics decision-making transformation. Logistics firms are divided into firms with medium scale and above and small and medium-sized firms according to their scale. Then EWIF-AHP model is proposed to measure the weight of index system and score the decision-making, so as to evaluate the impact of various influencing factors on transformation and development of logistics firms. The results show that, for logistics firms above medium scale, logistics technology innovation and logistics big data sharing have the most significant impact on transformation and development, followed by logistics management upgrading and logistics decision-making transformation. For small and medium-sized logistics firms, the biggest factor is the upgrading of logistics management, followed by the upgrading of logistics technology, which is almost as important as the influencing factors of the upgrading of logistics management, and followed by the sharing of logistics big data and the transformation of logistics decision-making. Therefore, corresponding countermeasures and suggestions for intelligent transformation of logistics firms have been put forward.

SCB-YOLOv5: a lightweight intelligent detection model for athletes' normative movements.

Intelligent detection of athlete behavior is beneficial for guiding sports instruction. Existing mature target detection algorithms provide significant support for this task. However, large-scale target detection algorithms often encounter more challenges in practical application scenarios. We propose SCB-YOLOv5, to detect standardized movements of gymnasts. First, the movements of aerobics athletes were captured, labeled using the labelImg software, and utilized to establish the athlete normative behavior dataset, which was then enhanced by the dataset augmentation using Mosaic9. Then, we improved the YOLOv5 by (1) incorporating the structures of ShuffleNet V2 and convolutional block attention module to reconstruct the Backbone, effectively reducing the parameter size while maintaining network feature extraction capability; (2) adding a weighted bidirectional feature pyramid network into the multiscale feature fusion, to acquire precise channel and positional information through the global receptive field of feature maps. Finally, SCB-YOLOv5 was lighter by 56.9% than YOLOv5. The detection precision is 93.7%, with a recall of 99% and mAP value of 94.23%. This represents a 3.53% improvement compared to the original algorithm. Extensive experiments have verified that our method. SCB-YOLOv5 can meet the requirements for on-site athlete action detection. Our code and models are available at https://github.com/qingDu1/SCB-YOLOv5 .

Understanding the limits to animal cognition.

The thriving field of comparative cognition examines the behaviour of diverse animals in cognitive terms. Comparative cognition research has primarily focused on the abilities of animals - what tasks they can do - rather than on the limits of their cognition - tasks that exceed an animal's cognitive abilities. We propose that understanding and identifying cognitive limits is as important as demonstrating the capacities of animal minds. Here, we identify challenges that have deterred the study of cognitive limits related to epistemic, practical and publication problems. The epistemic problem is concerned with how we can confidently infer a cognitive limit from null or negative results. The practical problem is how can we be certain our research has identified a cognitive limit rather than failures in tasks due to methodological or experimental design issues. The publication problem outlines the publication bias toward positive and exciting results over negative or null results in animal cognition. We propose solutions to these three challenges and examples of how to conduct research to confidently identify and confirm cognitive limits in animals. We believe a refocus on the cognitive limits of animals is the next step in the field of comparative cognition. Knowing the limits to the intelligence of different animals will aid us in appreciating the diversity of animal intelligence, and will resolve outstanding questions of how cognition evolves.

Design of a multi-category drug information integration platform for intelligent pharmacy management: A needs analysis study.

A drug store was never just an area to fill personal solution. Patients considered drug specialists to be counsels, somebody who could help them pick an over-the-counter treatment or understanding the portion and directions for a solution. Drug stores, similar to the remainder of the medical services business, are going through changes. Nowadays, one of the main highlights of any structure is the board. The executives give the refinement needed to wrap up any responsibility in a particular way. The executive framework of a drug store can be utilized to deal with most drug store related errands. This report has provided data on the best way to fabricate and execute a Pharmacy Management System. The primary objective of this system is to expand exactness, just as security and proficiency, in the drug shop. This undertaking is focused on the drug store area, determined to offer engaging and reasonable programming answers to assist them with modernizing to rival shops (helping out other equal modules in a similar examination program). This study will clarify the system's thoughts concerning the board issues and arrangements of a drug store. Likewise, this study covers the main parts of the Pharmacy application's investigation, execution, and look.

Predictability of intelligence and age from structural connectomes.

In this study, structural images of 1048 healthy subjects from the Human Connectome Project Young Adult study and 94 from ADNI-3 study were processed by an in-house tractography pipeline and analyzed together with pre-processed data of the same subjects from braingraph.org. Whole brain structural connectome features were used to build a simple correlation-based regression machine learning model to predict intelligence and age of healthy subjects. Our results showed that different forms of intelligence as well as age are predictable to a certain degree from diffusion tensor imaging detecting anatomical fiber tracts in the living human brain. Though we did not identify significant differences in the prediction capability for the investigated features depending on the imaging feature extraction method, we did find that crystallized intelligence was consistently better predictable than fluid intelligence from structural connectivity data through all datasets. Our findings suggest a practical and scalable processing and analysis framework to explore broader research topics employing brain MR imaging.

Gene-environment pathways to cognitive intelligence and psychotic-like experiences in children.

In children, psychotic-like experiences (PLEs) are related to risk of psychosis, schizophrenia, and other mental disorders. Maladaptive cognitive functioning, influenced by genetic and environmental factors, is hypothesized to mediate the relationship between these factors and childhood PLEs. Using large-scale longitudinal data, we tested the relationships of genetic and environmental factors (such as familial and neighborhood environment) with cognitive intelligence and their relationships with current and future PLEs in children. We leveraged large-scale multimodal data of 6,602 children from the Adolescent Brain and Cognitive Development Study. Linear mixed model and a novel structural equation modeling (SEM) method that allows estimation of both components and factors were used to estimate the joint effects of cognitive phenotypes polygenic scores (PGSs), familial and neighborhood socioeconomic status (SES), and supportive environment on NIH Toolbox cognitive intelligence and PLEs. We adjusted for ethnicity (genetically defined), schizophrenia PGS, and additionally unobserved confounders (using computational confound modeling). Our findings indicate that lower cognitive intelligence and higher PLEs are significantly associated with lower PGSs for cognitive phenotypes, lower familial SES, lower neighborhood SES, and less supportive environments. Specifically, cognitive intelligence mediates the effects of these factors on PLEs, with supportive parenting and positive school environments showing the strongest impact on reducing PLEs. This study underscores the influence of genetic and environmental factors on PLEs through their effects on cognitive intelligence. Our findings have policy implications in that improving school and family environments and promoting local economic development may enhance cognitive and mental health in children.

Childhood is a critical period for brain development. Difficult experiences during this developmental phase may contribute to reduced intelligence and poorer mental health later in life. Genetics and environmental factors also play roles. For example, having family support or a higher family income has been linked to better brain health outcomes for children. Delusions or hallucinations, or other psychotic-like experiences during childhood, are linked with poor mental health later in life. Children who experience psychotic-like episodes between the ages of nine and eleven have a higher risk of developing schizophrenia or related conditions. Environmental circumstances during childhood also appear to play a crucial role in shaping the risk of schizophrenia or related conditions. Park, Lee et al. show that positive parenting and supportive school and neighborhood environments boost child intelligence and mental health. In the experiments, Park, Lee et al. analyzed data on 6,602 children to determine how genetics and environmental factors shaped their intelligence and mental health. The models show that children with higher intelligence have a lower risk of psychosis. Both genetics and supportive environments contribute to higher intelligence. Complex interactions between biology and social factors shape children's intelligence and mental health. Beneficial genetics and coming from a family with more financial resources are helpful. Yet, social environments, such as having parents who use positive child-rearing practices, or having supportive schools or neighborhoods, have protective effects that can offset other disadvantages. Policies that help parents, encourage supportive school environments, and strengthen neighborhoods may boost children's intelligence and mental health later in life.

The functional aspects of selective exposure for collective decision-making under social influence.

Opinion diversity is crucial for collective decision-making, but maintaining it becomes challenging in the face of social influence. We propose selective exposure as an endogenous mechanism that preserves opinion diversity by forming exclusive subgroups of like-minded individuals, or echo chambers, which have been often perceived as an obstacle to achieving collective intelligence. We consider situations where a group of agents collectively make decisions about the true state of nature with the assumption that agents update their opinions by adopting the aggregated opinions of their information sources (i.e., naïve learning), or alternatively, replace incongruent sources with more like-minded others without adjusting their opinions (i.e., selective exposure). Individual opinions at steady states reached under these dynamics are then aggregated to form collective decisions, and their quality is assessed. The results suggest that the diversity-reducing effects of social influence are effectively confined within subgroups formed by selective exposure. More importantly, strong propensities for selective exposure maintain the quality of collective decisions at a level as high as that achieved in the absence of social influence. In contrast, naïve learning allows groups to reach consensuses, which are more accurate than initial individual opinions, but significantly undermines the quality of collective decisions.

Complexity synchronization in emergent intelligence.

In this work, we use a simple multi-agent-based-model (MABM) of a social network, implementing selfish algorithm (SA) agents, to create an adaptive environment and show, using a modified diffusion entropy analysis (DEA), that the mutual-adaptive interaction between the parts of such a network manifests complexity synchronization (CS). CS has been shown to exist by processing simultaneously measured time series from among organ-networks (ONs) of the brain (neurophysiology), lungs (respiration), and heart (cardiovascular reactivity) and to be explained theoretically as a synchronization of the multifractal dimension (MFD) scaling parameters characterizing each time series. Herein, we find the same kind of CS in the emergent intelligence of groups formed in a self-organized social interaction without macroscopic control but with biased self-interest between two groups of agents playing an anti-coordination game. This computational result strongly suggests the existence of the same CS in real-world social phenomena and in human-machine interactions as that found empirically in ONs.

Comparison of approaches to control for intracranial volume in research on the association of brain volumes with cognitive outcomes.

Most neuroimaging studies linking regional brain volumes with cognition correct for total intracranial volume (ICV), but methods used for this correction differ across studies. It is unknown whether different ICV correction methods yield consistent results. Using a brain-wide association approach in the MRI substudy of UK Biobank (N = 41,964; mean age = 64.5 years), we used regression models to estimate the associations of 58 regional brain volumetric measures with eight cognitive outcomes, comparing no correction and four ICV correction approaches. Approaches evaluated included: no correction; dividing regional volumes by ICV (proportional approach); including ICV as a covariate in the regression (adjustment approach); and regressing the regional volumes against ICV in different normative samples and using calculated residuals to determine associations (residual approach). We used Spearman-rank correlations and two consistency measures to quantify the extent to which associations were inconsistent across ICV correction approaches for each possible brain region and cognitive outcome pair across 2320 regression models. When the association between brain volume and cognitive performance was close to null, all approaches produced similar estimates close to the null. When associations between a regional volume and cognitive test were not null, the adjustment and residual approaches typically produced similar estimates, but these estimates were inconsistent with results from the crude and proportional approaches. For example, when using the crude approach, an increase of 0.114 (95% confidence interval [CI]: 0.103-0.125) in fluid intelligence was associated with each unit increase in hippocampal volume. However, when using the adjustment approach, the increase was 0.055 (95% CI: 0.043-0.068), while the proportional approach showed a decrease of -0.025 (95% CI: -0.035 to -0.014). Different commonly used methods to correct for ICV yielded inconsistent results. The proportional method diverges notably from other methods and results were sometimes biologically implausible. A simple regression adjustment for ICV produced biologically plausible associations.

Neural responses to syllable-induced P1m and social impairment in children with autism spectrum disorder and typically developing Peers.

In previous magnetoencephalography (MEG) studies, children with autism spectrum disorder (ASD) have been shown to respond differently to speech stimuli than typically developing (TD) children. Quantitative evaluation of this difference in responsiveness may support early diagnosis and intervention for ASD. The objective of this research is to investigate the relationship between syllable-induced P1m and social impairment in children with ASD and TD children. We analyzed 49 children with ASD aged 40-92 months and age-matched 26 TD children. We evaluated their social impairment by means of the Social Responsiveness Scale (SRS) and their intelligence ability using the Kaufman Assessment Battery for Children (K-ABC). Multiple regression analysis with SRS score as the dependent variable and syllable-induced P1m latency or intensity and intelligence ability as explanatory variables revealed that SRS score was associated with syllable-induced P1m latency in the left hemisphere only in the TD group and not in the ASD group. A second finding was that increased leftward-lateralization of intensity was correlated with higher SRS scores only in the ASD group. These results provide valuable insights but also highlight the intricate nature of neural mechanisms and their relationship with autistic traits.

Radical Collective Intelligence and the Reimagining of Cognitive Science.

To introduce our special issue How Minds Work: The Collective in the Individual, we propose "radical CI," a form of collective intelligence, as a new paradigm for cognitive science. Radical CI posits that the representations and processes necessary to perform the cognitive functions that humans perform are collective entities, not encapsulated by any individual. To explain cognitive performance, it appeals to the distribution of cognitive labor on the assumption that the human project runs on countless interactions between locally acting individuals with specialized skills that each retain a small part of the relevant information. Some of the papers in the special issue appeal to radical CI to account for a variety of cognitive phenomena including memory performance, metacognition, belief updating, reasoning, and problem-solving. Other papers focus on the cultural and institutional practices that make radical CI possible.