Cognitive Systems Research最新文献

{"title":"What you need to know about a learning robot: Identifying the enabling architecture of complex systems","authors":"Helen Beierling ,&nbsp;Phillip Richter ,&nbsp;Mara Brandt ,&nbsp;Lutz Terfloth ,&nbsp;Carsten Schulte ,&nbsp;Heiko Wersing ,&nbsp;Anna-Lisa Vollmer","doi":"10.1016/j.cogsys.2024.101286","DOIUrl":"10.1016/j.cogsys.2024.101286","url":null,"abstract":"<div><div>Nowadays we deal with robots and AI more and more in our everyday life. However, their behavior is not always apparent to most lay users, especially in error situations. This can lead to misconceptions about the behavior of the technologies being used. This in turn can lead to misuse and rejection by users. Explanation, for example through transparency, can address these misconceptions. However, explaining the entire software or hardware would be confusing and overwhelming for users. Therefore, this paper focuses on the ‘enabling’ architecture. It describes those aspects of a robotic system that may need to be explained to enable someone to use the technology effectively. Furthermore, this paper deals with the ‘explanandum’, i.e. the corresponding misunderstandings or missing concepts of the enabling architecture that need to be clarified. Thus, we have developed and are presenting an approach to determine the ‘enabling’ architecture and the resulting ‘explanandum’ of complex technologies.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"88 ","pages":"Article 101286"},"PeriodicalIF":2.1,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A mathematical formulation of learner cognition for personalised learning experiences","authors":"Jeena A. Thankachan,&nbsp;Bama Srinivasan","doi":"10.1016/j.cogsys.2024.101283","DOIUrl":"10.1016/j.cogsys.2024.101283","url":null,"abstract":"<div><div>The paper focuses on the assessment of cognitive skills within Virtual Learning Environments (VLEs). In response to the global shift to remote learning amid the COVID-19 pandemic, VLEs, which include learning management systems (LMS) and online collaboration platforms, gained prominence. The proposed work leverages an established Cattell–Horn–Carroll (CHC) theory to propose eight metrics, which collectively form a part of Cognitive Evaluation Metrics (CEM). The proposed metrics introduce a novel computational approach for multimode evaluation of learners’ cognitive abilities for each learning task within a learning environment. The paper details the formalism for the evaluation of the metrics and makes a contribution towards the potential of the proposed methodology to evaluate cognitive abilities. Additionally, the work implements CEM integration into the learner module of a Game-Based Learning (GBL) environment. Analysis of simulations in the GBL environment, along with statistical analysis, provides insights into the normal distribution of cognitive metrics. This reveals diverse ranges in various abilities such as long or short term memory, working memory, reasoning, attention, and processing speed. The paper also explores the impact of virtual assistants, which highlights their limited relevance to enhance cognitive abilities but serve as valuable on-demand support resources.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"88 ","pages":"Article 101283"},"PeriodicalIF":2.1,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of the emotional component of inner pronunciation: EEG-ERP study","authors":"Ivanov Viacheslav,&nbsp;Vartanov Alexander","doi":"10.1016/j.cogsys.2024.101287","DOIUrl":"10.1016/j.cogsys.2024.101287","url":null,"abstract":"<div><div>The article discusses the problems of identifying the emotional component of inner pronunciation using psychophysiological methods. In the course of preliminary analysis, P200, N400 and LPC were identified, associated with various parameters of prosody regulation during inner pronunciation. An experimental study of inner pronunciation using event-related potentials from EEG was conducted to isolate these components. In addition, a new method of localizing sources of activity using EEG “virtually implanted electrode” was applied in order to study possible sources of the isolated components. The results show the connection of EEG components with various characteristics of prosody (P200 − the beginning of prosody encoding, N400 − the valence of the emotion, LPC − the intensity of the emotion). Based on the results, the participation of various brain structures in the generation of each of the components was also analyzed.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"88 ","pages":"Article 101287"},"PeriodicalIF":2.1,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"Tianyi Zhang,&nbsp;Jun Tang","doi":"10.1016/j.cogsys.2024.101289","DOIUrl":"10.1016/j.cogsys.2024.101289","url":null,"abstract":"","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"88 ","pages":"Article 101289"},"PeriodicalIF":2.1,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards emotion-aware intelligent agents by utilizing knowledge graphs of experiences","authors":"Raziyeh Zall,&nbsp;Mohammad Reza Kangavari","doi":"10.1016/j.cogsys.2024.101285","DOIUrl":"10.1016/j.cogsys.2024.101285","url":null,"abstract":"<div><p>Because of the increasing presence of intelligent agents in various aspects of human social life, social skills play a vital role in ensuring these systems exhibit acceptable and realistic behavior in social communication. The importance of emotional intelligence in social capabilities is noteworthy, so incorporating emotions into the behaviors of intelligent agents is essential. Therefore, some computational models of emotions have been presented to develop intelligent agents that exhibit emotional human-like behaviors. However, most current computational models of emotions neglect the dynamic learning of the affective meaning of events based on agents’ experiences. Such models evaluate the events in the environment according to emotional aspects without considering the context of the situations. Also, these models capture the emotional states of agents by using predefined rules determined according to psychological theories. Therefore, they disregard the data-driven methods that can obtain the relationships between appraisal variables and emotions based on natural human data with fewer assumptions on the nature of such relationships. To address these issues, we proposed a novel and unified affective-cognitive framework (EIAEC) to facilitate the development of emotion-aware intelligent agents. EIAEC uses appraisal theories to acquire the emotional states of the agent in various situations. This paper contains four main contributions: 1- We have designed an efficient episodic memory that uses events and their conditional contexts to store and retrieve knowledge and experiences. This memory facilitates emotional expressions and decision-making adapted to the situations of the agent. 2- A novel method has been proposed that learns context-dependent affective values associated with events by using the agent’s experiences in various contexts. Subsequently, we acquired appraisal variables using the elements and related meta-data in episodic memory. 3- We have proposed a new data-driven method that maps appraisal variables to emotional states. 4- Moreover, a method has been developed to update the activation values regarding actions by using the emotional states of the agent. This method models the influence of emotions on the agent’s decision-making. Finally, we simulate a driving scenarios in our proposed framework to manifest the generated emotions in different situations and conditions. Moreover, we show how the proposed method learns the affective meaning of events and actions used in appraisal computing.</p></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"88 ","pages":"Article 101285"},"PeriodicalIF":2.1,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142239194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the impact of virtual reality flight simulations on EEG neural patterns and task performance","authors":"Evy van Weelden ,&nbsp;Travis J. Wiltshire ,&nbsp;Maryam Alimardani ,&nbsp;Max M. Louwerse","doi":"10.1016/j.cogsys.2024.101282","DOIUrl":"10.1016/j.cogsys.2024.101282","url":null,"abstract":"<div><p>Neurophysiological measurements, such as electroencephalography (EEG), can be used to derive insight into pilots’ mental states during flight training and to track learning progress in order to optimize the training experience for each individual. Prior work has demonstrated that the level of fidelity of a flight simulation (2D Desktop vs. 3D VR) is associated with different cortical activity in relation to task demands. However, it remains unknown whether simulation fidelity affects flight performance, and whether this effect can be observed in EEG neurophysiological responses associated with workload. The current study therefore assessed whether an EEG-based index of workload and task engagement is predictive of performance during flight training in different simulation environments. We conducted a within-subject designed experiment with 53 novice participants who performed two flight tasks (speed change, medium turn) under two conditions (Desktop vs. VR). EEG signals were collected throughout the experiment to quantify mental workload using the beta-ratio (<span><math><mfrac><mi>β</mi><mrow><mi>α</mi><mo>+</mo><mi>θ</mi></mrow></mfrac></math></span>). The VR condition showed increased beta-ratios in all lobes, including frontal and parietal areas, compared to the Desktop simulation. Additionally, we observed an effect of simulator environment on performance, as VR was associated with improved flight performance. However, we found no evidence of a relationship between the beta-ratio and performance. Our findings demonstrate that the brain responds differently to tasks in training environments of various levels of fidelity. However, more research into the neurometrics of flight training is needed in order to develop brain-computer interfaces that can enhance current pilot training methods by providing personalized feedback in real-time.</p></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"88 ","pages":"Article 101282"},"PeriodicalIF":2.1,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1389041724000767/pdfft?md5=9f83e22aa6fb4740aa0fce68260eb7b7&pid=1-s2.0-S1389041724000767-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142164457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A design for neural network model of continuous reading","authors":"Jarkko Hautala ,&nbsp;Mirka Saarela ,&nbsp;Otto Loberg ,&nbsp;Tommi Kärkkäinen","doi":"10.1016/j.cogsys.2024.101284","DOIUrl":"10.1016/j.cogsys.2024.101284","url":null,"abstract":"<div><p>Cognition and learning are exceedingly modeled as an associative activity of connectionist neural networks. However, only a few such models exist for continuous reading, which involves the delicate coordination of word recognition and eye movements. Moreover, these models are limited to only orthographic level of word processing with predetermined lexicons. Here, we present a conceptual design of a developmentally plausible neural network model of reading designed to simulate word learning, parafoveal preview activation of words, their later foveal word recognition including phonological decoding, and forward saccade length as a control mechanism for intake of new textual information. We will discuss the theoretical advancements of the design and avenues for future developments.</p></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"88 ","pages":"Article 101284"},"PeriodicalIF":2.1,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1389041724000780/pdfft?md5=fe87ae81ac59de6166a8de673672c548&pid=1-s2.0-S1389041724000780-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142168016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the logic of agent’s emotions","authors":"Yuanyi Wang ,&nbsp;Zhen Liu ,&nbsp;Tingting Liu ,&nbsp;Alexei V. Samsonovich ,&nbsp;Valentin V. Klimov","doi":"10.1016/j.cogsys.2024.101281","DOIUrl":"10.1016/j.cogsys.2024.101281","url":null,"abstract":"<div><p>Emotions can be instrumental in shaping the cognition of an intelligent agent. This work presents a yet another attempt to formalize emotions based on the Ortony-Clore-Collins (OCC) model. Specifically, we are interested in emotions, the appraisal of which evaluates the consequences for others. The formal modeling framework introduced here is based on the multiagent Affective Probabilistic Logic (AfPL), which allows us to compute the potential of a given emotion, which represents the emotion’s intensity. The value of this potential allows us to distinguish experienced emotions from mere affective responses using a threshold. The framework describes basic as well as compound emotions. An illustrative practical application scenario in the field of intelligent tutoring is analyzed, demonstrating that the model is robust and practically useful in real-life applications. Broader impact and future research directions are discussed.</p></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"88 ","pages":"Article 101281"},"PeriodicalIF":2.1,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142239193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive network modeling for joint action and memory recall for elderly by detecting interpersonal synchrony","authors":"Yijie Xu ,&nbsp;Sophie C.F. Hendrikse ,&nbsp;Jan Treur ,&nbsp;Peter H.M.P. Roelofsma","doi":"10.1016/j.cogsys.2024.101280","DOIUrl":"10.1016/j.cogsys.2024.101280","url":null,"abstract":"<div><p>This paper explores the potential of adaptive network modeling for joint action and memory recall among elderly through detecting interpersonal synchrony. With the aging population increasing, there is a crucial need to focus on the health and social interaction of older adults. Based on research of the significance of social interaction and memory use for the elderly, as well as the role of interpersonal synchrony in joint action, this paper aims to analyse computationally how to enhance positive effects of social interactions among older individuals by applying an adaptive network model. The research examines the concept of interpersonal synchrony and its impact on joint action, memory, and emotional well-being in elderly populations. Through simulation experiments and analysis, the study demonstrates the potential benefits for music in memory recall for older adults with cognitive decline, highlighting the importance of social interaction and emotional resonance. This study offers a valuable contribution to understanding and improving social interactions and memory recall among the elderly.</p></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"88 ","pages":"Article 101280"},"PeriodicalIF":2.1,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1389041724000743/pdfft?md5=cf7f62f35c4e17b003e1165735b663ab&pid=1-s2.0-S1389041724000743-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142117425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A motivational-based learning model for mobile robots","authors":"Letícia Berto ,&nbsp;Paula Costa ,&nbsp;Alexandre Simões ,&nbsp;Ricardo Gudwin ,&nbsp;Esther Colombini","doi":"10.1016/j.cogsys.2024.101278","DOIUrl":"10.1016/j.cogsys.2024.101278","url":null,"abstract":"<div><p>Humans have needs motivating their behavior according to intensity and context. However, we also create preferences associated with each action’s perceived pleasure, which is susceptible to changes over time. This makes decision-making more complex, requiring learning to balance <em>needs</em> and preferences according to the context. To understand how this process works and enable the development of robots with a motivational-based learning model, we computationally model a motivation theory proposed by Hull. In this model, the agent (an abstraction of a mobile robot) is motivated to keep itself in a state of homeostasis. We introduced hedonic dimensions to explore the impact of preferences on decision-making and employed reinforcement learning to train our motivated-based agents. In our experiments, we deploy three agents with distinct energy decay rates, simulating different metabolic rates, within two diverse environments. We investigate the influence of these conditions on their strategies, movement patterns, and overall behavior. The findings reveal that agents excel at learning more effective strategies when the environment allows for choices that align with their metabolic requirements. Furthermore, we observe that incorporating pleasure as a component of the motivational mechanism affects behavior learning, particularly for agents with regular metabolisms depending on the environment. Our study also unveils that, when confronted with survival challenges, agents prioritize immediate <em>needs</em> over pleasure and equilibrium. These insights shed light on how robotic agents can adapt and make informed decisions in demanding scenarios, demonstrating the intricate interplay between motivation, pleasure, and environmental context in autonomous systems.</p></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"88 ","pages":"Article 101278"},"PeriodicalIF":2.1,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142128212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}