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When two cognitive processes contribute to a behavioral output-each process producing a specific distribution of the behavioral variable of interest-and when the mixture proportion of these two processes varies as a function of an experimental condition, a common density point should be present in the observed distributions of the data across said conditions. In principle, one can statistically test for the presence (or absence) of a fixed point in experimental data to provide evidence in favor of (or against) the presence of a mixture of processes, whose proportions are affected by an experimental manipulation. In this paper, we provide an empirical diagnostic of this test to detect a mixture of processes. We do so using resampling of real experimental data under different scenarios, which mimic variations in the experimental design suspected to affect the sensitivity and specificity of the fixed-point test (i.e., mixture proportion, time on task, and sample size). Resampling such scenarios with real data allows us to preserve important features of data which are typically observed in real experiments while maintaining tight control over the properties of the resampled scenarios. This is of particular relevance considering such stringent assumptions underlying the fixed-point test. With this paper, we ultimately aim at validating the fixed-point property of binary mixture data and at providing some performance metrics to researchers aiming at testing the fixed-point property on their experimental data., (© 2023. The Author(s).)

In this article, with the help of Heidegger and Foucault's conceptual tools, as well as the achievements of the cognitive science, we try to examine the transition from paper reading to digital reading. This discussion can be a preparation for a philosophical understanding of the digital age. We show that Heidegger's analysis of the ready-to-hand beings and technology can help to understand the reading behaviors of paper and digital readers. We have also use Heidegger's analytical framework for text ontology. On the other hand, Foucault's reflections on the embodied technologies can explain the power-knowledge relationships that is constructed around the reader. In addition, the cognitive science has provided detailed empirical findings on the effects of reading behaviors on the human cognitive biases and skills by examining the various modes of reading. Embodied and skillful cognitive science, by going beyond the computational understanding of human beings, has noticed the various behaviors and states of the body in reading processes and highlighted their effects. To integrate these areas of research, we try to place the findings of the cognitive science within the framework of Heidegger and Foucault's technological understanding of the modern power-knowledge relationships. [ABSTRACT FROM AUTHOR]

While research evidence has the potential to improve classroom practice, research-practice gaps continue to persist not least owing to the limited relevance of research findings for practice. A common approach in healthcare to address the research-practice gap is to form research priority setting partnerships (PSPs) in which stakeholders identify questions they would like research to answer. This paper presents the results from such a PSP in education with a focus on cognitive science research, which has received increased attention in the past few years owing to its potential to explain memory and learning processes and inform classroom practice. Over 400 questions from teachers were collected using an online survey. The final 15 research priorities highlight the need for research on a wider range of subjects, settings and phases as well as research designs that take the complexity of classrooms into consideration and aim to answer how different teaching strategies interact with each other as well as student motivation and agency at the micro- and macro-level. The role of teacher expertise vs. fidelity to original research designs should also be investigated further. Overall, this paper highlights the importance of taking teacher voice into account to ensure that new research in the field is both academically rigorous and practically relevant.

Abstract concepts are relevant to a wide range of disciplines, including cognitive science, linguistics, psychology, cognitive, social, and affective neuroscience, and philosophy. This consensus paper synthesizes the work and views of researchers in the field, discussing current perspectives on theoretical and methodological issues, and recommendations for future research. In this paper, we urge researchers to go beyond the traditional abstract-concrete dichotomy and consider the multiple dimensions that characterize concepts (e.g., sensorimotor experience, social interaction, conceptual metaphor), as well as the mediating influence of linguistic and cultural context on conceptual representations. We also promote the use of interactive methods to investigate both the comprehension and production of abstract concepts, while also focusing on individual differences in conceptual representations. Overall, we argue that abstract concepts should be studied in a more nuanced way that takes into account their complexity and diversity, which should permit us a fuller, more holistic understanding of abstract cognition. [ABSTRACT FROM AUTHOR]

Teams are a fundamental aspect of life-from sports to business, to defense, to science, to education. While the cognitive sciences tend to focus on information processing within individuals, others have argued that teams are also capable of demonstrating cognitive capacities similar to humans, such as skill acquisition and forgetting (cf., Cooke, Gorman, Myers, & Duran, 2013; Fiore et al., 2010). As artificially intelligent and autonomous systems improve in their ability to learn, reason, interact, and coordinate with human teammates combined with the observation that teams can express cognitive capacities typically seen in individuals, a cognitive science of teams is emerging. Consequently, new questions are being asked about teams regarding teamness, trust, the introduction and effects of autonomous systems on teams, and how best to measure team behavior and phenomena. In this topic, four facets of human-autonomy team cognition are introduced with leaders in the field providing in-depth articles associated with one or more of the facets: (1) defining teams; (2) how trust is established, maintained, and repaired when broken; (3) autonomous systems operating as teammates; and (4) metrics for evaluating team cognition across communication, coordination, and performance., (© 2024 Cognitive Science Society LLC.)

Cognitive science-informed approaches have gained considerable influence in education in the UK and internationally, but not much is known about how teachers perceive cognitive science-informed strategies or enact them within the contexts of their everyday classrooms. In this paper, we discuss the perceptions and experiences of cognitive science-informed strategies of 13 teachers in England. The paper critically explores how the teachers understood and used cognitive science-informed strategies in their teaching, their views of the benefits and challenges for different subjects and groups of learners, and their reflections on supporting factors and barriers for adopting the strategies in their schools. The teachers' accounts illustrate some of the many complexities of adopting cognitive science-informed approaches in real-life educational settings. Drawing on their narratives, the paper emphasises the importance of acknowledging different contextual dimensions and the dynamic interactions between them to understand when and how teachers enact cognitive science-informed approaches in their classrooms.

There are increasingly frequent calls for school mathematics curricula to be informed by robust research evidence. One approach to achieving this is designing evidence-informed learning and teaching resources for the classroom. In this paper, we reflect on our experiences of designing a free and fully resourced complete set of secondary mathematics curriculum materials. We explore in detail the challenges we have encountered in our attempts to apply principles from the cognitive theory of multimedia learning. We focus on tensions we have experienced when simultaneously applying multiple principles and balancing these with other educational considerations. Specifically, we consider trade-offs between redundancy versus clarity, seductive details versus richness, personalisation and emotional design versus abstraction, spatial contiguity and signalling versus parsimony, and pre-training and worked examples versus exploration. We examine the choices and dilemmas we faced, and illustrate our emerging attempts to resolve these tensions through presenting multiple examples from our design work. We conclude with recommendations about how tensions among these design principles might be navigated in curriculum design and we suggest possible avenues for further research in this area.

A central concern of the cognitive science of language since its origins has been the concept of the linguistic system. Recent approaches to the system concept in language point to the exceedingly complex relations that hold between many kinds of interdependent systems, but it can be difficult to know how to proceed when "everything is connected." This paper offers a framework for tackling that challenge by identifying *scale* as a conceptual mooring for the interdisciplinary study of language systems. The paper begins by defining the scale concept--simply, the possibility for a measure to be larger or smaller in different instances of a system, such as a phonemic inventory, a word's frequency value in a corpus, or a speaker population. We review sites of scale difference in and across linguistic subsystems, drawing on findings from linguistic typology, grammatical description, morphosyntactic theory, psycholinguistics, computational corpus work, and social network demography. We consider possible explanations for scaling differences and constraints in language. We then turn to the question of *dependencies between* sites of scale difference in language, reviewing four sample domains of scale dependency: in phonological systems, across levels of grammatical structure (Menzerath's Law), in corpora (Zipf's Law and related issues), and in speaker population size. Finally, we consider the implications of the review, including the utility of a scale framework for generating new questions and inspiring methodological innovations and interdisciplinary collaborations in cognitive-scientific research on language.

Cognitive researchers often carve cognition up into structures and processes. Cognitive processes operate on structures, like vehicles driving over a map. Language alongside semantic and episodic memory are proposed to have structure, as are perceptual systems. Over these structures, processes operate to construct memory and solve problems by retrieving and manipulating information. Network science offers an approach to representing cognitive structures and has made tremendous inroads into understanding the nature of cognitive structure and process. But is the mind a network? If so, what kind? In this article, we briefly review the main metaphors, assumptions, and pitfalls prevalent in cognitive network science (maps and vehicles; one network/process to rule them all), highlight the need for new metaphors that elaborate on the map-and-vehicle framework (wormholes, skyhooks, and generators), and present open questions in studying the mind as a network (the challenge of capturing network change, what should the edges of cognitive networks be made of, and aggregated vs. individual-based networks). One critical lesson of this exercise is that the richness of the mind as network approach makes it a powerful tool in its own right; it has helped to make our assumptions more visible, generating new and fascinating questions, and enriching the prospects for future research. A second lesson is that the mind as a network-though useful-is incomplete. The mind is not a network, but it may contain them., (© 2021 Cognitive Science Society LLC.)

An introduction is presented in which the editor discusses articles in the issue on the topics including demonstrates exciting results based on self-imitation learning within a cross-modal setting; and providing advances in controllable generation and interpolation across attributes.

This paper discusses adapting Churches' approach to large-scale teacher/researcher conceptual replications of major "science of learning" findings, to increase teachers' engagement with empirical research on, and building research networks for, gathering data on the science of learning. The project here demonstrated the feasibility of teacher-led randomised controlled trials for conceptually replicating the effects of cognitive science on learning, as specified by researchers. It also indicated high levels of interest by teachers in applying more science of learning in their practice. The approach gave freedom to teachers to design interventions, choose research methods, and measure outcomes, even though such freedom would be in tension with some scientific research which relies on constraining the sources of variation. This paper discusses how a balance can be struck between the objectives of teachers and researchers engaged in replicating cognitive science findings, and promoting teacher engagement in conceptual replication research.

This paper discusses a three-level model that synthesizes and unifies existing learning theories to model the roles of artificial intelligence (AI) in promoting learning processes. The model, drawn from developmental psychology, computational biology, instructional design, cognitive science, complexity and sociocultural theory, includes a causal learning mechanism that explains how learning occurs and works across micro, meso and macro levels. The model also explains how information gained through learning is aggregated, or brought together, as well as dissipated, or released and used within and across the levels. Fourteen roles for AI in education are proposed, aligned with the model's features: four roles at the individual or micro level, four roles at the meso level of teams and knowledge communities and six roles at the macro level of cultural historical activity. Implications for research and practice, evaluation criteria and a discussion of limitations are included. Armed with the proposed model, AI developers can focus their work with learning designers, researchers and practitioners to leverage the proposed roles to improve individual learning, team performance and building knowledge communities.

The approach that students take in their studies at university is critical not only for their academic success but is equally important in life-long learning for their career and professional development. Heutagogy is the study of self-determined learning and it is important that we appraise how students in higher education are developing their metacognitive awareness in how they learn and study effectively. Cognitive science is an interdisciplinary research area that involves the scientific study of the human mind. It helps provide new knowledge in relation to areas such as memory, problem solving, knowledge transfer and understanding of complex topics. Cognitive science has demonstrated that re-testing oneself on material when learning, enhances and promotes greater retention of knowledge compared to re-reading the material. Learning that is distributed or spaced out over multiple study sessions also allows for greater retention of knowledge in the longer-term compared to 'cramming' of information. To evaluate the use of effective study approaches and habits across three different levels of study in an undergraduate pharmacy programme, a survey study was employed. A paper-based survey was completed by first-, second- and third-year undergraduate pharmacy students (n=192) during class sessions. Although there was some evidence of metacognitive awareness such as using testing (retrieval practice) with practice problems; across all years, suboptimal study approaches such as rereading, copying notes and cramming were endorsed. A schedule of deadlines shaped the organisation of study and time management for most students. Self-testing was predominantly used to test learning rather than an approach used during learning. There was evidence of a difference between the cohorts in relation to decisions for prioritizing studying, returning to review course material and re-reading. The evidence from this study demonstrates that learners would require training on metacognitive awareness and effective study strategies to enable their self-determined learning capabilities to evolve. The linear progression through a programme of study alone will not achieve this. There is a need to embed and emphasize effective strategies for learning into curricula and for faculty to utilise metacognitive awareness in their teaching.

"What is the structure of thought?" is as central a question as any in cognitive science. A classic answer to this question has appealed to a Language of Thought (LoT). We point to emerging research from disparate branches of the field that supports the LoT hypothesis, but also uncovers diversity in LoTs across cognitive systems, stages of development, and species. Our letter formulates open research questions for cognitive science concerning the varieties of rules and representations that underwrite various LoT-based systems and how these variations can help researchers taxonomize cognitive systems., (© 2022 Cognitive Science Society LLC.)

Cognitive science has much to contribute to the general scientific body of knowledge, but it is also a field rife with possibilities for providing background research that can be leveraged by artificial intelligence (AI) developers. In this introduction, we briefly explore the history of AI. We particularly focus on the relationship between AI and cognitive science and introduce this special issue that promotes the method of inspiring AI development with the results of cognitive science research., (© 2022 Cognitive Science Society LLC.)

In recent years, a multitude of datasets of human-human conversations has been released for the main purpose of training conversational agents based on data-hungry artificial neural networks. In this paper, we argue that datasets of this sort represent a useful and underexplored source to validate, complement, and enhance cognitive studies on human behavior and language use. We present a method that leverages the recent development of powerful computational models to obtain the fine-grained annotation required to apply metrics and techniques from Cognitive Science to large datasets. Previous work in Cognitive Science has investigated the question-asking strategies of human participants by employing different variants of the so-called 20-question-game setting and proposing several evaluation methods. In our work, we focus on GuessWhat, a task proposed within the Computer Vision and Natural Language Processing communities that is similar in structure to the 20-question-game setting. Crucially, the GuessWhat dataset contains tens of thousands of dialogues based on real-world images, making it a suitable setting to investigate the question-asking strategies of human players on a large scale and in a natural setting. Our results demonstrate the effectiveness of computational tools to automatically code how the hypothesis space changes throughout the dialogue in complex visual scenes. On the one hand, we confirm findings from previous work on smaller and more controlled settings. On the other hand, our analyses allow us to highlight the presence of "uninformative" questions (in terms of Expected Information Gain) at specific rounds of the dialogue. We hypothesize that these questions fulfill pragmatic constraints that are exploited by human players to solve visual tasks in complex scenes successfully. Our work illustrates a method that brings together efforts and findings from different disciplines to gain a better understanding of human question-asking strategies on large-scale datasets, while at the same time posing new questions about the development of conversational systems.

Purpose: I take as a starting point the disparaging comments about the place of history and philosophy of education in initial teacher education (ITE) made by the chair of the Teacher Education Expert Panel established by the Australian Government in 2023, which I take to be the most recent attempt at resurrecting the tired debate over "the art versus science of teaching". I draw on an example from my own ITE classroom to tease possibilities for how historical research and research in the sciences of learning can be in dialogue and collaborate to deepen educational scholarship. Design/Methodology/Approach: The purpose of this essay is to reflect on the scholarly discussions and debates in the sciences of learning that historical researchers of education have largely been absent or excluded from. I argue that historical researchers have an important role to play as "critical friends" to the burgeoning fields of cognitive science and educational neuroscience. Findings: From my cursory and singular account, I highlight two key areas of education where historical research and the sciences of learning can be fruitfully in dialogue and collaboration: Firstly, on the cultural dimension that prefigures learners' approach to learning; and secondly, on the factors that enable or disable effective learning. Originality/Value: This essay sets an agenda for historical researchers in education to exercise "critical friendship" with the cognitive and brain sciences of learning, specifically by adding temporal considerations to the way biology, psychology, and sociality interact to produce different educational outcomes. This will be of "practical" service to ITE -- and to students in schools.

The critical question for cognitive scientists is what does cognitive science do, if anything, for people? Cognitive science is primarily concerned with human cognition but has fallen short in continuously and critically assessing the "who" in human cognition. This complacency in a world where white supremacist and patriarchal structures leave cognitive science in the unfortunate position of potentially "supporting" those structures. We take it that many cognitive scientists operate on the assumption that the study of human cognition is both interesting and important. We want to invoke that importance to note that cognitive scientists must continue to work to show how the field is useful to all of humanity and reflects a humanity that is not white by default. We wonder how much the field has done, and can do, to show that it is useful not only in the sense that we might make connections with researchers in other fields, win grants and write papers, even of the highest quality, but useful in some material way to the billions of non-cognitive scientists across the globe.

All papers will be fully peer reviewed with publication of the full issue scheduled for later in the same year. Belief in conspiracy theories has become widespread, from COVID-19 and vaccinations to climate change and QAnon. [Extracted from the article]

Artificial intelligence (AI) policy, innovation, and practice are moving ahead in rapid fashion. There is a growing mismatch between technological innovations in AI, which are advancing at a rapid rate, and normative and regulatory frameworks, which are lagging, particularly when it comes to protecting democratic values and human rights principles. National governments and multilateral institutions are attempting to catch up. At least 175 countries, firms and other organizations have produced documents listing ethical principles for AI. These efforts have proceeded in a somewhat fragmented manner, yet there are emerging signs of consolidation as the United States, Europe, and other countries begin to coalesce around shared principles. Europe, in particular, has raced ahead to draft comprehensive legislation, the Artificial Intelligence Act (AIA), to oversee these technologies and systems. What has motivated the European Union to pursue this approach? And how will this effort influence AI norms globally? This paper describes how Europe's AI norm-building process represents an effort to ensure EU priorities are reflected in the AI governance landscape. Europe's approach faces uncertainty. While it is likely that the AIA will meaningfully influence global AI norms, several factors may hinder its global diffusion and adoption. [ABSTRACT FROM AUTHOR]

Several deep learning models are presented: Ref. [[4]] designs a network intrusion detection model, DLNID, that combines an attention mechanism, a bidirectional long short-term memory (LSTM), and an adaptive synthetic sampling, and demonstrates improved performance for severely imbalanced data. In addition, there are a couple of papers on HPO: Ref. [[11]] introduces a greedy I k i -fold cross validation method that vastly reduces the average time required to find the best-performing model with or without a computational budget, and experimentally verifies its improved performance over existing methods. Since its inception as a branch of Artificial Intelligence, Machine Learning (ML) has flourished in recent years. [Extracted from the article]

Cognitive science-informed approaches have gained considerable influence in education in the UK and internationally, but not much is known about how teachers perceive cognitive science-informed strategies or enact them within the contexts of their everyday classrooms. In this paper, we discuss the perceptions and experiences of cognitive science-informed strategies of 13 teachers in England. The paper critically explores how the teachers understood and used cognitive science-informed strategies in their teaching, their views of the benefits and challenges for different subjects and groups of learners, and their reflections on supporting factors and barriers for adopting the strategies in their schools. The teachers' accounts illustrate some of the many complexities of adopting cognitive science-informed approaches in real-life educational settings. Drawing on their narratives, the paper emphasises the importance of acknowledging different contextual dimensions and the dynamic interactions between them to understand when and how teachers enact cognitive science-informed approaches in their classrooms. [ABSTRACT FROM AUTHOR]

This document is a response to a comment made by Robertas Damaševičius regarding an article on info-autopoiesis and artificial general intelligence (AGI). The response addresses specific points raised in the comment and clarifies the author's position. It emphasizes that info-autopoiesis, the self-creation of information, is fundamental only to humans and not achievable by artificial creations. The article argues for the impossibility of achieving AGI using info-autopoiesis and suggests that resources should be directed towards more achievable goals in AI. The response also discusses the relationship between biological aspects of human intelligence and AI research, highlighting the use of neural networks and considering the architecture of AGI. It concludes by stating that info-autopoiesis sets a limit to human knowledge based on fundamental principles and that all human creations are syntactic and incapable of semantic interpretation. The text discusses the concept of info-autopoiesis and its application to AI research. Info-autopoiesis is a framework that aims to explain how living organisms function, and the paper explores its potential implications for AI and AGI development. The author acknowledges the limitations of info-autopoiesis and emphasizes the need for interdisciplinary collaboration and ethical considerations in AI research. The text concludes by suggesting areas for further research and expressing gratitude for the comments and suggestions provided by Robertas Damaševičius. [Extracted from the article]

The role of artificial intelligence (AI) in the education sector has received considerable attention from scholars; however, its potency and application among tourism learners are yet to be discerned. Accordingly, this research note articulates the potential impact and limitations of the AI chatbot, ChatGPT, in tourism education practices. In particular, the paper aims to raise awareness among tourism educators regarding the disruptive potential of ChatGPT and its impact on academic integrity and ethics. This paper contributes to tourism education practices on AI and robotics by critically discussing ChatGPT from a cognitive science perspective and providing recommendations on how to minimise AI plagiarism in tourism education. This research argues for adapting novel pedagogies as technology evolves and raises the need for AI ethical use guidelines in tourism education. [ABSTRACT FROM AUTHOR]

Among those living systems, which are cognizers? Among the behaviours of, and causes of behaviour in, living systems, which are cognitive? Such questions sit at the heart of a sophisticated, ongoing debate, of which the recent papers by Corcoran et al. (2020) and Sims and Kiverstein (2021) serve as excellent examples. I argue that despite their virtues, both papers suffer from flawed conceptions of the point of the debate. This leaves their proposals ill-motivated—good answers to the wrong question. Additionally, their proposals are unfit to serve the legitimate roles for characterizations of cognition. [ABSTRACT FROM AUTHOR]

Knauff and Gazzo Castañeda (2022) object to using the term "new paradigm" to describe recent developments in the psychology of reasoning. This paper concedes that the Kuhnian term "paradigm" may be queried. What cannot is that the work subsumed under this heading is part of a new, progressive movement that spans the brain and cognitive sciences: Bayesian cognitive science. Sampling algorithms and Bayes nets used to explain biases in JDM can implement the Bayesian new paradigm approach belying any advantages of mental models theory (MMT) at the algorithmic level. Moreover, this paper argues that new versions of MMT lack a computational level theory and questions the grounds for MMTs much-vaunted generality. The paper then examines common ground on the importance of small-scale models/simulations of the world and the importance of argumentation in the social domain rather than individual reasoning. Finally, the paper concludes that although there may be prospects for moving reasoning research forward in a more collective, collaborative manner, many disagreements remain to be resolved. [ABSTRACT FROM AUTHOR]

Copyright of TEME: Casopis za Društvene Nauke is the property of TEME: Casopis za Drustvene Nauke and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In EFL learning and teaching processes, mental translation might play an essential role in the mind of the foreign language learner. Some scholarly papers investigate the types of processing in the language learners’ mind such as language perception, language acquisition and dialogue production. Other studies, based on empirical psycholinguistic research about the process of mental translation, investigate mental translation strategies as well as the neuro-linguistic aspects of this process, including cognitive and brain imaging research. There is a consensus among researchers that using neurolinguistics knowledge, one can connect neurological processes in the language learners’ brain with the foreign language learning. This paper tries to contribute to existing studies in this field and highlight the importance of neuro-linguistic research in connection to the biological capacity of the human memory to make connections between language systems in the process of communicative language learning. [ABSTRACT FROM AUTHOR]

To what extent does playing a musical instrument contribute to an individual’s construction of knowledge? This paper aims to address this question by examining music performance from an embodied perspective and offering a narrative-style review of the main literature on the topic. Drawing from both older theoretical frameworks on motor learning and more recent theories on sensorimotor coupling and integration, this paper seeks to challenge and juxtapose established ideas with contemporary views inspired by recent work on embodied cognitive science. By doing so we advocate a centripetal approach to music performance, contrasting the prevalent centrifugal perspective: the sounds produced during performance not only originate from bodily action (centrifugal), but also cyclically return to it (centripetal). This perspective suggests that playing music involves a dynamic integration of both external and internal factors, transcending mere output-oriented actions and revealing music performance as a form of knowledge acquisition based on real-time sensorimotor experience. [ABSTRACT FROM AUTHOR]

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The growing integration of renewable energy sources into grid-connected microgrids has created new challenges in power generation forecasting and energy management. This paper explores the use of advanced machine learning algorithms, specifically Support Vector Regression (SVR), to enhance the efficiency and reliability of these systems. The proposed SVR algorithm leverages comprehensive historical energy production data, detailed weather patterns, and dynamic grid conditions to accurately forecast power generation. Our model demonstrated significantly lower error metrics compared to traditional linear regression models, achieving a Mean Squared Error of 2.002 for solar PV and 3.059 for wind power forecasting. The Mean Absolute Error was reduced to 0.547 for solar PV and 0.825 for wind scenarios, and the Root Mean Squared Error (RMSE) was 1.415 for solar PV and 1.749 for wind power, showcasing the model's superior accuracy. Enhanced predictive accuracy directly contributes to optimized resource allocation, enabling more precise control of energy generation schedules and reducing the reliance on external power sources. The application of our SVR model resulted in an 8.4% reduction in overall operating costs, highlighting its effectiveness in improving energy management efficiency. Furthermore, the system's ability to predict fluctuations in energy output allowed for adaptive real-time energy management, reducing grid stress and enhancing system stability. This approach led to a 10% improvement in the balance between supply and demand, a 15% reduction in peak load demand, and a 12% increase in the utilization of renewable energy sources. Our approach enhances grid stability by better balancing supply and demand, mitigating the variability and intermittency of renewable energy sources. These advancements promote a more sustainable integration of renewable energy into the microgrid, contributing to a cleaner, more resilient, and efficient energy infrastructure. The findings of this research provide valuable insights into the development of intelligent energy systems capable of adapting to changing conditions, paving the way for future innovations in energy management. Additionally, this work underscores the potential of machine learning to revolutionize energy management practices by providing more accurate, reliable, and cost-effective solutions for integrating renewable energy into existing grid infrastructures. [ABSTRACT FROM AUTHOR]

Artificial neural network models are able to achieve great performance at numerous computationally challenging tasks like face recognition. It is of significant importance to explore the difference between neural network models and human brains in terms of computational mechanism. This issue has become an experimental focus for some researchers in recent studies, and it is believed that using human behavior to understand neural network models can address this issue. This paper compares the neural network model performance with human performance on a classic yet important task: judging the ethnicity of a given face. This study uses Caucasian and East Asian faces to train 4 neural networks including AlexNet, VGG11, VGG13, and VGG16. Then, the ethnicity judgments of the neural networks are compared with human data using classical psychophysical methods by fitting psychometric curves. The results suggest that VGG11, followed by VGG16, shows a similar response pattern as humans, while simpler AlexNet and more complex VGG13 do not resemble human performance. Thus, this paper explores a new paradigm to compare neural networks and human brains. • Neural networks are able to provide cognitive approaches from face perception. • Human perception is used as a benchmark to evaluate the performance of neural networks. • Neural networks utilizing eye regions are confirmed to be better to perceive faces. • Attentional region analysis would unveil the processing details of a neural network. [ABSTRACT FROM AUTHOR]

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As such, the workshop has always been located in Italy, with the workshop Steering Committee constantly committed to involve every major Italian research group working on agents and multi-agent systems. The first Workshop "From Objects to Agents" (WOA) was held in Parma in May 2000. The topics discussed in the papers covered some of the most debated subjects in the research on agents and multi-agent systems, and were not limited to the theme of the workshop, which was suggested in the call for papers as I multi-agent systems in the machine learning era i . [Extracted from the article]