Givenness Hierarchy Theoretic Cognitive Status Filtering
Colorado School of Mines（科罗拉多矿业学院）
Poulomi Pal, Lixiao Zhu, Andrea Golden-Lasher, Akshay Swaminathan, Tom Williams
备注：
To be published in the proceedings of the 2020 Annual Meeting of the Cognitive Science Society (COGSCI). Supplemental materials available at https://osf.io/qse7y/
For language-capable interactive robots to be effectively introduced into human society, they must be able to naturally and efficiently communicate about the objects, locations, and people found in human environments. An important aspect of natural language communication is the use of pronouns. Ac-cording to the linguistic theory of the Givenness Hierarchy(GH), humans use pronouns due to implicit assumptions about the cognitive statuses their referents have in the minds of their conversational partners. In previous work, Williams et al. presented the first computational implementation of the full GH for the purpose of robot language understanding, leveraging a set of rules informed by the GH literature. However, that approach was designed specifically for language understanding,oriented around GH-inspired memory structures used to assess what entities are candidate referents given a particular cognitive status. In contrast, language generation requires a model in which cognitive status can be assessed for a given entity. We present and compare two such models of cognitive status: a rule-based Finite State Machine model directly informed by the GH literature and a Cognitive Status Filter designed to more flexibly handle uncertainty. The models are demonstrated and evaluated using a silver-standard English subset of the OFAI Multimodal Task Description Corpus.
Language-capable interactive robots participating in dialogues with human interlocutors must be able to naturally and efficiently communicate about the entities in their environment. A key aspect of such communication is the use of anaphoric language. The linguistic theory of the Givenness Hierarchy(GH) suggests that humans use anaphora based on the cognitive statuses their referents have in the minds of their interlocutors. In previous work, researchers presented GH-theoretic approaches to robot anaphora understanding. In this paper we describe how the GH might need to be used quite different…
The population is aging, and becoming more tech-savvy. The United Nations predicts that by 2050, one in six people in the world will be over age 65 (up from one in 11 in 2019), and this increases to one in four in Europe and Northern America. Meanwhile, the proportion of American adults over 65 who own a smartphone has risen 24 percentage points from 2013-2017, and the majority have Internet in their homes. Smart devices and smart home technology have profound potential to transform how people age, their ability to live independently in later years, and their interactions with their circle of …
Peri-AIIMS: Perioperative Artificial Intelligence Driven Integrated Modeling of Surgeries using Anesthetic, Physical and Cognitive Statuses for Predicting Hospital Outcomes
University of Florida（佛罗里达大学）；UF Health（佛罗里达大学健康医疗中心）；Rowan University（罗文大学）
Driving as a Diagnostic Tool: Scenario-based Cognitive Assessment in Older Drivers from Driving Video
organization= Department of Electrical \& Computer Engineering , addressline= Iowa State University , city= Ames , state= IA , postcode= 50011 , country= USA；organization= Institute for Transportation , addressline= Iowa State University , city= Ames , state= IA , postcode= 50010 , country= USA；Brain Health Lab , addressline= University of Nebraska Medical Center , city= Omaha , state= NE , postcode= 68198 , country= USA；organization= Department of Mechanical Engineering , addressline= Iowa State University , city= Ames , state= IA , postcode= 50011 , country= USA
A Survey of Models for Cognitive Diagnosis: New Developments and Future Directions
University of Science and Technology of China（中国科学技术大学）；State Key Laboratory of Cognitive Intelligence（认知智能国家重点实验室）；Anhui Province Key Laboratory of Science Education and Communication（安徽省科学教育与传播重点实验室）；iFLYTEK AI Research（讯飞AI研究院）
Cognitive Biases in Large Language Models for News Recommendation
University of Amsterdam（阿姆斯特丹大学）；Shandong University（山东大学）；Leiden University（莱顿大学）；Peoples' Friendship University of Russia (RUDN University)（俄罗斯人民友谊大学（RUDN大学））；Joint Institute for Nuclear Research（联合核子研究所）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；University of Skövde（舍夫德大学）
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Saarland Informatics Campus（萨尔兰信息学园区）
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Ritsumeikan University（立命馆大学）；The University of Tokyo（东京大学）；The Whole Brain Architecture Initiative（全脑架构倡议）；Osaka University（大阪大学）；Okinawa Institute of Science and Technology Graduate University（冲绳科学技术大学院大学）；Tamagawa University（玉川大学）；The University of Electro-Communications（电气通信大学）
Building a humanlike integrative artificial cognitive system, that is, an artificial general intelligence (AGI), is the holy grail of the artificial intelligence (AI) field. Furthermore, a computational model that enables an artificial system to achieve cognitive development will be an excellent reference for brain and cognitive science. This paper describes an approach to develop a cognitive architecture by integrating elemental cognitive modules to enable the training of the modules as a whole. This approach is based on two ideas: (1) brain-inspired AI, learning human brain architecture to b…
Ritsumeikan University（立命馆大学）；Keio University（庆应义塾大学）；The University of Tokyo（东京大学）；Università Milano-Bicocca（米兰比可卡大学）；University of Essex（埃塞克斯大学）；Donders Institute for Brain, Cognition and Behaviour（唐德斯脑、认知与行为研究所）；Cajal International Neuroscience Center, Spanish National Research Council（西班牙国家研究委员会卡哈尔国际神经科学中心）；Bogazici University（博阿齐奇大学）；Queen Mary University of London（伦敦玛丽女王大学）；The University of Electro-Communications（电气通信大学）；National Autonomous University of Mexico（墨西哥国立自治大学）；Universidad Autónoma del Estado de Morelos（莫雷洛斯州自治大学）；Institute of Cognitive Sciences and Technologies, National Research Council of Italy（意大利国家研究委员会认知科学与技术研究所）
Creating autonomous robots that can actively explore the environment, acquire knowledge and learn skills continuously is the ultimate achievement envisioned in cognitive and developmental robotics. Their learning processes should be based on interactions with their physical and social world in the manner of human learning and cognitive development. Based on this context, in this paper, we focus on the two concepts of world models and predictive coding. Recently, world models have attracted renewed attention as a topic of considerable interest in artificial intelligence. Cognitive systems learn…
A Surface-Based Federated Chow Test Model for Integrating APOE Status, Tau Deposition Measure, and Hippocampal Surface Morphometry
School of Computing and Augmented Intelligence, Arizona State University（亚利桑那州立大学计算与增强智能学院）；Banner Alzheimer’s Institute（班纳阿尔茨海默病研究所）；Faculty of Dentistry, The University of Hong Kong（香港大学牙医学院）；Department of Neurology, Mayo Clinic Arizona（梅奥诊所亚利桑那州神经内科）；Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, University of Southern California（南加州大学史蒂文斯神经影像与信息学研究所影像遗传学中心）
Edge Computing based Human-Robot Cognitive Fusion: A Medical Case Study in the Autism Spectrum Disorder Therapy
Intelligent Social Systems and Swarm Robotics Lab (IS$^3$R)（智能社会系统与群体机器人实验室）；Computer Science and Information Systems Department（计算机科学与信息系统系）；Bradley University（布拉德利大学）
D-Artemis: A Deliberative Cognitive Framework for Mobile GUI Multi-Agents
Tianjin University（天津大学）；Didichuxing Co. Ltd（滴滴出行有限公司）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Huazhong Agricultural University（华中农业大学）；Sichuan University（四川大学）；Tianjin University of Science and Technology（天津科技大学）
Do Large Language Models Possess a Theory of Mind? A Comparative Evaluation Using the Strange Stories Paradigm
ELTE Research Centre for Linguistics（ELTE语言学研究中心）；Department of Cognitive Science, Faculty of Natural Sciences, Budapest University of Technology and Economics（布达佩斯技术与经济大学自然科学学院认知科学系）；Institute of Mathematics, Eötvös Lóránd University（欧多布雷尼大学数学研究所）
Tsinghua University（清华大学）；Institute for Artificial Intelligence（人工智能研究院）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）
Reading comprehension is a complex cognitive process involving many human brain activities. Plenty of works have studied the patterns and attention allocations of reading comprehension in information retrieval related scenarios. However, little is known about what happens in human brain during reading comprehension and how these cognitive activities can affect information retrieval process. Additionally, with the advances in brain imaging techniques such as electroencephalogram (EEG), it is possible to collect brain signals in almost real time and explore whether it can be utilized as feedback…
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Queen Mary University of London（伦敦玛丽女王大学）；Massachusetts General Hospital（马萨诸塞综合医院）；The Alan Turing Institute（艾伦·图灵研究所）；University of Edinburgh（爱丁堡大学）；Jožef Stefan Institute（约热夫·斯特凡研究所）
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Departament de Filologia Catalana, Universitat Autònoma de Barcelona（加泰罗尼亚语言系系，巴塞罗那自治大学）；Institut für Psychologie, Humboldt-Universitat zu Berlin（柏林洪堡大学心理学研究所）；Institució Catalana de Recerca i Estudis Avançats (ICREA)（加泰罗尼亚高级研究与高级教育研究所）
