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How do you know that you don’t know? Cogn. Syst. Res. (IF 3.9) Pub Date : 2024-03-19 Quentin F. Gronau, Mark Steyvers, Scott D. Brown
Whenever someone in a team tries to help others, it is crucial that they have some understanding of other team members’ goals. In modern teams, this applies equally to human and artificial (“bot”) assistants. Understanding when one does not know something is crucial for stopping the execution of inappropriate behavior and, ideally, attempting to learn more appropriate actions. From a statistical point
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An adaptive network model for AI-assisted monitoring and management of neonatal respiratory distress Cogn. Syst. Res. (IF 3.9) Pub Date : 2024-03-16 Nisrine Mokadem, Fakhra Jabeen, Jan Treur, H. Rob Taal, Peter H.M.P. Roelofsma
This article presents the use of second-order adaptive network models of hospital teams consisting of doctors and nurses, interacting together. A variety of scenarios are modelled and simulated, in relation with respiratory distress of a neonate, along with the integration of an AI-Coach for monitoring and support of such teams and of organizational learning. The research highlights the benefits of
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Faulty control system Cogn. Syst. Res. (IF 3.9) Pub Date : 2024-03-15 Atef Gharbi
The integration of robotics into everyday life is increasing and these complex systems are exposed to complex faults that require rapid identification for seamless repair and continuous operation. These faults have a complex impact on cognitive aspects such as perception, decision-making and behavioral execution in robots. Robotic fault detection and diagnosis research (FDD) focuses primarily on individual
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Retraction notice to “IoT individual privacy features analysis based on convolutional neural network” [Cogn. Syst. Res. 57 (2019) 126–130] Cogn. Syst. Res. (IF 3.9) Pub Date : 2024-03-01 Meng Xi, Nie Lingyu, Song Jiapeng
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Retraction notice to “Prediction methods of ecological civilization outlook based on distributed algorithm of factor graph” [Cogn. Syst. Res. 56 (2019) 7–12] Cogn. Syst. Res. (IF 3.9) Pub Date : 2024-03-01 Z, h, u, , L, i
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Retraction notice to “Online contour extraction and texture analysis – A IoT based case study” [Cogn. Syst. Res. 52 (2018) 1029–1035] Cogn. Syst. Res. (IF 3.9) Pub Date : 2024-03-01 Y, u, , L, a, n
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Retraction notice to “Real time regulation of micro-grid communication network state” [Cogn. Syst. Res. 52 (2018) 1013–1019] Cogn. Syst. Res. (IF 3.9) Pub Date : 2024-03-01 Xiaoyi Huang, Weiguo Li
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The emergence of compositionality in a brain-inspired cognitive architecture Cogn. Syst. Res. (IF 3.9) Pub Date : 2024-02-29 Howard Schneider
Compositionality can be considered as finding (or creating) the correct meaning of the constituents of a non-simple language expression or visual image. The Causal Cognitive Architecture is a brain-inspired cognitive architecture (BICA). It is not a traditional artificial neural network architecture, nor a traditional symbolic AI system but instead uses spatial navigation maps as its fundamental circuits
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Vector database management systems: Fundamental concepts, use-cases, and current challenges Cogn. Syst. Res. (IF 3.9) Pub Date : 2024-02-15 T, o, n, i, , T, a, i, p, a, l, u, s
Vector database management systems have emerged as an important component in modern data management, driven by the growing importance for the need to computationally describe rich data such as texts, images and video in various domains such as recommender systems, similarity search, and chatbots. These data descriptions are captured as numerical vectors that are computationally inexpensive to store
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Auditory event-related potential differentiates girls with Rett syndrome from their typically-developing peers with high accuracy: Machine learning study Cogn. Syst. Res. (IF 3.9) Pub Date : 2024-02-10 Maxim Sharaev, Maxim Nekrashevich, Daria Kostanian, Victoria Voinova, Olga Sysoeva
Rett Syndrome (RTT) is a rare neurodevelopmental disorder caused by mutation in the gene. No cures are still available, but several clinical trials are ongoing. Here we examine neurophysiological correlates of auditory processing for ability to differentiate patients with RTT from typically developing (TD) peers applying standard machine learning (ML) methods and pipelines. Capitalized on the available
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The mode of computing Cogn. Syst. Res. (IF 3.9) Pub Date : 2024-01-03 Luis A. Pineda
The Turing Machine is the paradigmatic case of computing machines, but there are others such as analogical, connectionist, quantum and diverse forms of unconventional computing, each based on a particular intuition of the phenomenon of computing. This variety can be captured in terms of system levels, re-interpreting and generalizing Newell’s hierarchy, which includes the knowledge level at the top
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Extended X: Extending the reach of active externalism Cogn. Syst. Res. (IF 3.9) Pub Date : 2024-01-02 Paul R. Smart
The terms “extended cognition” and the “extended mind” identify two strands of philosophical argument that are commonly subsumed under the general heading of active externalism. The present paper describes an integrated approach to understanding extended cognition and the extended mind—one that papers over the differences between these two, ostensibly distinct, forms of cognitive extension. As an added
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Modelling conversational agent with empathy mechanism Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-12-30 Xuyao Dai, Zhen Liu, Tingting Liu, Guokun Zuo, Jialin Xu, Changcheng Shi, Yuanyi Wang
Empathy mechanism in communication is the cornerstone for effective and meaningful interaction. Establishing an empathy mechanism in conversation agent (CA) requires accurate recognition of users’ emotions to facilitate generates appropriate empathetic responses. Therefore, we proposed a Multimodal Emotion Recognition Model (MERM) to recognizes a user’s emotional state from multimodal data (audio,
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Suppression of negative tweets using reinforcement learning systems Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-12-29 Kazuteru Miyazaki, Hitomi Miyazaki
In recent years, damage caused by negative tweets has become a social problem. In this paper, we consider a method of suppressing negative tweets by using reinforcement learning. In particular, we consider the case where tweet writing is modeled as a multi-agent environment. Numerical experiments verify the effects of suppression using various reinforcement learning methods. We will also verify robustness
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A formal understanding of computational empathy in interactive agents Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-12-29 Andreas Brännström, Joel Wester, Juan Carlos Nieves
Interactive software agents, such as chatbots, are progressively being used in the area of health and well-being. In such applications, where agents engage with users in interpersonal conversations for, e.g., coaching, comfort or behavior-change interventions, there is an increased need for understanding agents’ empathic capabilities. In the current state-of-the-art, there are no tools to do that.
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Explanatory models in neuroscience, Part 2: Functional intelligibility and the contravariance principle Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-12-28 Rosa Cao, Daniel Yamins
Computational modeling plays an increasingly important role in neuroscience, highlighting the philosophical question of how computational models explain. In the particular case of neural network models, concerns have been raised about their intelligibility, and how these models relate (if at all) to what is found in the brain. We claim that what makes a system intelligible is an understanding of the
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The embeddings world and Artificial General Intelligence Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-12-22 Mostafa Haghir Chehreghani
From early days, a key and controversial question inside the artificial intelligence community was whether Artificial General Intelligence (AGI) is achievable. AGI is the ability of machines and computer programs to achieve human-level intelligence and do all tasks that a human being can. While there exist a number of systems in the literature claiming they realize AGI, several other researchers argue
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Preventive mental health care: A complex systems framework for ambient smart environments Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-12-17 Ben White, Inês Hipólito
We offer a framework for the design and use of Ambient Smart Environments (ASEs) for preventive mental health care support. Drawing from Complex Systems Theory (CST) and ‘E’ Cognitive Science (ECS), we claim that ASEs have the potential to act in a preventive capacity in support of good mental health, i.e. supporting dynamics that avoid so-called “struck states” (which are, according to CST, thought
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The place of language in multimodal communication in humans and other primates Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-12-16 Michael Sharwood Smith
Much attention has been paid to ways in which different categories of individual combine different modalities to communicate meanings to others. One major challenge that remains is to gain a deeper understanding of the cognitive processing responsible for the simultaneous deployment and integration of various different resources during multimodal communication. In response to this challenge, a Modular
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Designing a wheel-based assessment tool to measure visual aesthetic emotions Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-12-07 Nouf Abukhodair, Meehae Song, Serkan Pekçetin, Steve DiPaola
Measuring emotions in a comprehensive and meaningful way has been a constant challenge for emotion researchers in behavioral sciences. There is much debate surrounding affect and emotion conveyed in artwork as these elements are subjective higher-level semantics that are difficult to measure objectively. This paper introduces the Visual Aesthetic Wheel of Emotion (VAWE), a domain-specific device for
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Improving deep learning with prior knowledge and cognitive models: A survey on enhancing explainability, adversarial robustness and zero-shot learning Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-11-30 Fuseini Mumuni, Alhassan Mumuni
We review current and emerging knowledge-informed and brain-inspired cognitive systems for realizing adversarial defenses, eXplainable Artificial Intelligence (XAI), and zero-shot or few-shot learning. Data-driven machine learning models have achieved remarkable performance and demonstrated capabilities surpassing humans in many applications. Yet, their inability to exploit domain knowledge leads to
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A multi-adaptive network model for human Hebbian learning, synchronization and social bonding based on adaptive homophily Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-11-29 Yelyzaveta Mukeriia, Jan Treur, Sophie Hendrikse
This paper present a multi-adaptive network model integrating multiple adaptation mechanisms, specifically focusing on five types of such adaptation mechanisms. Two of them address first-order adaptation by learning of responding on others and first-order adaptation by bonding with others based on homophily. Three other adaptation mechanisms addressed are second-order adaptation of the speed of both
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XAI Transformer based Approach for Interpreting Depressed and Suicidal User Behavior on Online Social Networks Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-11-18 Anshu Malhotra, Rajni Jindal
Online social networks can be used for mental healthcare monitoring using Artificial Intelligence and Machine Learning techniques for detecting various mental health disorders and corresponding risk assessment. Recent research in this domain has primarily been focused on leveraging deep neural networks and various Transformer based Large Language Models, which have now become state-of-the-art for most
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Relationship-specific and relationship-independent behavioural adaptivity in affiliation and bonding: A multi-adaptive dynamical systems approach Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-11-14 Sophie C.F. Hendrikse, Jan Treur, Sander L. Koole
Humans often adapt their behaviour toward each other when they interact. From a neuroscientific perspective, such adaptivity can involve mechanisms based on adaptive connections (synaptic plasticity) and adaptive excitability thresholds (nonsynaptic plasticity) within the mental or neural network concerned. It is, however, often left unaddressed which of the types of adaptation are specific for the
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Transformability, generalizability, but limited diffusibility: Comparing global vs. task-specific language representations in deep neural networks Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-11-07 Yanru Jiang, Rick Dale, Hongjing Lu
This study investigates the integration of two prominent neural network representations into a hybrid cognitive model for solving a natural language task, where pre-trained large-language models serve as global learners and recurrent neural networks offer more “local” task-specific representations in the neural network. To explore the fusion of these two types of representations, we employ an autoencoder
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Building a cognitive system based on process interaction Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-11-07 Viacheslav E. Wolfengagen, Larisa Ismailova, Sergey Kosikov
According to modern notions, computing is not separable from cognitive modeling and activity. This paper continues the tradition of the uniform approach and proposes a small number of general mechanisms that cope with the main known effects of computing as a science — the interaction of objects-as-processes, the interaction of processes with the environment, generalized interaction. As shown, the applicative
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Semantic configuration model with natural transformations Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-11-10 Viacheslav Wolfengagen, Larisa Ismailova, Sergey Kosikov, Igor Slieptsov, Sebastian Dohrn, Alexander Marenkov, Vladislav Zaytsev
In the present work, efforts have been made to create a configuration-based approach to knowledge extraction. The notion of granularity is developed, which allows fine-tuning the expressive possibilities of the semantic network. As known, the central issues for knowledge-based systems are what’s-in-a-node and what’s-in-a-link. As shown, the answer can be obtained from the functor-as-object representation
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Mind surfing: Attention in musical absorption Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-11-07 Simon Høffding, Nanette Nielsen, Bruno Laeng
Literature in the psychology of music and in cognitive psychology claims – paradoxically – that musical absorption includes processes of both focused attention and mind wandering. We examine this paradox and aim to resolve it by integrating accounts from cognitive psychology on attention and mind wandering with qualitative phenomenological research on some of the world’s most skilled musicians. We
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Human-inspired goal reasoning implementations: A survey Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-11-04 Ursula Addison
Goal reasoning is the ability of an artificial system to reason over its goals; it can identify, manage, plan, and execute its goals. In complex environments where requirements could change often, goal reasoning functionality is essential. Goal reasoning agents may rely on a motivation system to guide the goal reasoning process; we refer to such agents as motivated agents. Motivated agents can be explicitly
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WordIllusion: An adversarial text generation algorithm based on human cognitive system Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-10-30 Haoran Fu, Chundong Wang, Jiaqi Sun, Yumeng Zhao, Hao Lin, Junqing Sun, Baixue Zhang
Although natural language processing technology has shown strong performance in many tasks, it is very vulnerable to adversarial examples, i.e., sentences with some small perturbations can fool AI models. Current adversarial texts for English are usually generated by finding substitute words in adjacent spaces of keyword vectors. Unlike English, Chinese is more discrete and has a more complex font
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Detection of auditory hallucinations from electroencephalographic brain–computer interface signals Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-10-13 Beatriz García-Martínez, Patricia Fernández-Sotos, Jorge J. Ricarte, Eva M. Sánchez-Morla, Roberto Sánchez-Reolid, Roberto Rodriguez-Jimenez, Antonio Fernández-Caballero
Schizophrenia is a chronic psychiatric disorder that is highly debilitating. One of the most frequent symptoms is the presence of auditory hallucinations (AH), which could be related to alterations in brain electrical activity measurable with electroencephalography (EEG). Although many previous works have recorded EEG signals of schizophrenia patients with medical EEG devices, the study of AH has never
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Higher-order adaptive dynamical system modeling of the role of epigenetics in anxiety disorders Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-10-11 Shivant Kathusing, Natalie Samhan, Jan Treur
In this paper, a fifth-order adaptive self-modelling network model is introduced to describe epigenetic involvement in the development of anxiety disorders and its regulation by a possible epigenetics-based therapeutic method. Multiple orders of adaptivity are used in the model to depict the development process, where a higher pathway of any order of adaptivity adapts characteristics of pathways in
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The role of the cerebellum in fluid intelligence: An fMRI study Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-10-04 Leibovici Anat, Raizman Reut, Itzhaki Nofar, Tik Niv, Sapir Maayan, Tsarfaty Galia, Livny Abigail
Traditionally, neuroimaging studies of fluid intelligence have focused on brain activation in frontal-parietal regions. In the past decade there has been accumulating evidence regarding the involvement of the cerebellum in higher cognitive function. In the current study we aimed to further investigate the role of the cerebellum in processing of fluid intelligence. We therefore scanned thirty-nine healthy
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Computationally inspired cognitive modeling Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-09-21 Viacheslav Wolfengagen, Larisa Ismailova, Sergey Kosikov
A computational approach to cognitive modeling is proposed. The computational model is a parametric construction that takes into account cognitive stages and transitions between them. The cognitive model enables the idea of information processes, from their birth and appearance in a scope, evolution and canceling out their existence and disappearing from the scope. Process habitats are Lawvere’s variable
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Does ChatGPT have semantic understanding? A problem with the statistics-of-occurrence strategy Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-09-20 Lisa Miracchi Titus
Over the last decade, AI models of language and word meaning have been dominated by what we might call a statistics-of-occurrence, strategy: these models are deep neural net structures that have been trained on a large amount of unlabeled text with the aim of producing a model that exploits statistical information about word and phrase co-occurrence in order to generate behavior that is similar to
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EmoBot: Artificial emotion generation through an emotional chatbot during general-purpose conversations Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-09-09 Md Ehtesham-Ul-Haque, Jacob D’Rozario, Rudaiba Adnin, Farhan Tanvir Utshaw, Fabiha Tasneem, Israt Jahan Shefa, A.B.M. Alim Al Islam
Emotion modeling has always been intriguing to researchers, where detecting emotion is highly focused and generating emotion is much less focused to date. Therefore, in this paper, we aim to exploring emotion generation, particularly for general-purpose conversations. Based on the Cognitive Appraisal Theory and focusing on audio and textual inputs, we propose a novel method to calculate informative
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Piagetian experiments to DevRobotics Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-09-07 Letícia Berto, Leonardo Rossi, Eric Rohmer, Paula Costa, Ricardo Gudwin, Alexandre Simões, Esther Colombini
Integrating robots into our daily lives, once a distant dream, is gradually becoming a reality, surpassing our initial expectations. Today, we aspire for these robots to not only perform rudimentary tasks but to emulate human behavior, and in some aspects, even exceed it. The realm of research dedicated to achieving human-like competencies in robots has given rise to the fields of Developmental and
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Human-inspired autonomous driving: A survey Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-09-07 Alice Plebe, Henrik Svensson, Sara Mahmoud, Mauro Da Lio
Autonomous vehicles promise to revolutionize society and improve the daily life of many, making them a coveted aim for a vast research community. To enable complex reasoning in autonomous vehicles, researchers are exploring new methods beyond traditional engineering approaches, in particular the idea of drawing inspiration from the only existing being able to drive: the human. The mental processes
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Interpersonal trust modelling through multi-agent Reinforcement Learning Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-09-02 Vincent Frey, Julian Martinez
Many existing approaches to model and compute trust in a quantitative way rely on ranking, rating or assessments of agents by other agents. Even though reputation is related with trust, it does not capture all its characteristics. In parallel, many works in neuroscience shows evidence about interpersonal trust being an associative learning process encoded in the human brain. Inspired by other subjects
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Predictive event segmentation and representation with neural networks: A self-supervised model assessed by psychological experiments Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-08-30 Hamit Basgol, Inci Ayhan, Emre Ugur
People segment complex, ever-changing, and continuous experience into basic, stable, and discrete spatio-temporal experience units, called events. The literature on event segmentation investigates the mechanisms behind this ability. Event segmentation theory points out that people predict ongoing activities and observe prediction error signals to find event boundaries. In this study, we investigated
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On the importance of severely testing deep learning models of cognition Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-08-22 Jeffrey S. Bowers, Gaurav Malhotra, Federico Adolfi, Marin Dujmović, Milton L. Montero, Valerio Biscione, Guillermo Puebla, John H. Hummel, Rachel F. Heaton
Researchers studying the correspondences between Deep Neural Networks (DNNs) and humans often give little consideration to severe testing when drawing conclusions from empirical findings, and this is impeding progress in building better models of minds. We first detail what we mean by severe testing and highlight how this is especially important when working with opaque models with many free parameters
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Fundamental concepts of cognitive mimetics Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-08-11 Antero Karvonen, Tuomo Kujala, Tommi Kärkkäinen, Pertti Saariluoma
The rapid development and widespread adoption of Artificial Intelligence (AI) technologies have made the development of AI-specific design methods an important topic to advance. In recent decades, the centre of gravity in AI has shifted away from cognitive science and related fields like psychology. However, there is a clear need and potential for added value in returning to stronger interaction. One
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Inductive reasoning in humans and large language models Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-08-09 Simon Jerome Han, Keith J. Ransom, Andrew Perfors, Charles Kemp
The impressive recent performance of large language models has led many to wonder to what extent they can serve as models of general intelligence or are similar to human cognition. We address this issue by applying GPT-3.5 and GPT-4 to a classic problem in human inductive reasoning known as property induction. Over two experiments, we elicit human judgments on a range of property induction tasks spanning
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Testing methods of neural systems understanding Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-08-09 Grace W. Lindsay, David Bau
Neuroscientists apply a range of analysis tools to recorded neural activity in order to glean insights into how neural circuits drive behavior in organisms. Despite the fact that these tools shape the progress of the field as a whole, we have little empirical proof that they are effective at identifying the mechanisms of interest. At the same time, deep learning systems are trained to produce intelligent
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DDG: Dependency-difference gait based on emotional information attention for perceiving emotions from gait Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-08-02 Xiao Chen, Zhen Liu, Jiangjian Xiao, Tingting Liu, Yumeng Zhao
Perceiving human emotions is crucial in the realm of affective computing. As a nonverbal biological feature, gait plays a significant role in this field, owing to its resistance to manipulation or replication. In this paper, we propose a gait-based emotion perception framework called Dependency-Difference Gait (DDG), which can extract emotional features from gait patterns comprehensively and efficiently
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Enhancing interaction representation for joint entity and relation extraction Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-07-24 Ruixue Tang, Yanping Chen, Ruizhang Huang, Yongbin Qin
Jointly extracting entities and their relations from texts is an important task in information extraction. Despite the great success, traditional models suffer from two problems. First, the same token embeddings are shared in two subtasks. It ignores the difference between semantic granularities, in which named entities are more dependent on local features and relations are semantic expressions relevant
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Quantum projections on conceptual subspaces Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-07-27 Alejandro Martínez-Mingo, Guillermo Jorge-Botana, José Ángel Martinez-Huertas, Ricardo Olmos Albacete
One of the main challenges of cognitive science is to explain the representation of conceptual knowledge and the mechanisms involved in evaluating the similarities between these representations. Theories that attempt to explain this phenomenon should account for the fact that conceptual knowledge is not static. In line with this thinking, many studies suggest that the representation of a concept changes
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A systematic literature review of emotion recognition using EEG signals Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-07-27 Dwi Wahyu Prabowo, Hanung Adi Nugroho, Noor Akhmad Setiawan, Johan Debayle
In this study, we conducted a systematic literature review of 107 primary studies conducted between 2017 and 2023 to discern trends in datasets, classifiers, and contributions to human emotion recognition using EEG signals. We identified DEAP (43%), SEED (29%), DREAMER (8%), and SEED-IV (5%) as the most commonly used EEG signal datasets. Deep learning techniques, especially transformer neural architecture
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Retraction notice to “TOPSIS time variant decision fusion model evaluation for internet of public service things” [Cogn. Syst. Res. 52 (2018) 489–496] Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-07-21 Zhang Tao, Wang Feng
Abstract not available
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Retraction notice to “Multilevel factor analysis based online financial credit system” [Cogn. Syst. Res. 52 (2018) 466–472] Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-07-21 Fu-You Li, Wan-Nan Zhao
Abstract not available
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Retraction notice to “Real time regression analysis in internet of stock market cycles” [Cogn. Syst. Res. 52 (2018) 371–379] Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-07-21 Xiaolin Li
Abstract not available
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Retraction notice to “Design of dual-frequency antenna for IoT applications” [Cogn. Syst. Res. 52 (2018) 891–895] Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-07-21 Yun-xing Yang, Hui-chang Zhao, Si Chen, Shu-ning Zhang
Abstract not available
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Retraction notice to “Multivariate linear regression analysis on online image study for IoT” [Cogn. Syst. Res. 52 (2018) 312–316] Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-07-21 Xuanxuan Zhu
Abstract not available
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Retraction notice to “IoT study on the total risk management and cluster-coordinated development based on synergy theory” [Cogn. Syst. Res. 52 (2018) 809–815] Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-07-21 Xuejie Niu
Abstract not available
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Retraction notice to “The optimization of intelligent long-distance multimedia sports teaching system for IOT” [Cogn. Syst. Res. 52 (2018) 678–684] Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-07-21 Wanmin Gong, Litao Tong, Weiwei Huang, Shen Wang
Abstract not available
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Retraction notice to “Parallel distributed computing based wireless sensor network anomaly data detection in IoT framework” [Cogn. Syst. Res. 52 (2018) 342–350] Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-07-20
Abstract not available
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Retraction notice to “K-means clustering analysis and evaluation for internet of acoustic environment characteristics” [Cogn. Syst. Res. 52 (2018) 603–609] Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-07-20
Abstract not available
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Retraction notice to “Fuzzy cluster correlation mapping for online evaluation of teaching efficacy towards IoT study” [Cogn. Syst. Res. 52 (2018) 365–370] Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-07-20
Abstract not available
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Retraction notice to “Fuzzy clustering based self-organizing neural network for real time evaluation of wind music” [Cogn. Syst. Res. 52 (2018) 359–364] Cogn. Syst. Res. (IF 3.9) Pub Date : 2023-07-20
Abstract not available