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55th Human Factors and Ergonomics Society Annual Meeting, HFES 2011

作者： Hildebrand, Emily A. Branaghan, Russell J. Neuman, Brooke L. Jolly, Jonathan Garland, T.B. Taggart, Mistey Epstein, Dana R. Babcock-Parziale, Judith Brown, Victoria Phoenix VA Health Care System United States Cognitive Science and Engineering Program Arizona State University United States Southern Arizona VA Health Care System United States

ISBN: (纸本)9780945289395

Endoscope reprocessing has been identified as a complex procedure that, when completed incorrectly, can lead to patient outbreaks due to cross contamination. Most reported incidents cite human error as the cause, but little research to date has explored the human factors related issues surrounding reprocessing procedures. The purpose of this study was to interview experts in endoscope reprocessing to determine the most difficult steps and error prone tasks in the procedure as well as identify potential contributing factors to error. Relationships between tasks and contributing factors are examined and implications for future research are discussed.

关键词： Endoscopy

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Estimating Multiple Evoked Emotions from Videos 35

Estimating Multiple Evoked Emotions from Videos

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35th Annual Meeting of the cognitive science Society - Cooperative Minds: Social Interaction and Group Dynamics, CogSci 2013

作者： Choe, Wonhee Chun, Hyo-Sun Noh, Junhyug Lee, Seong-Deok Zhang, Byoung-Tak Cognitive Science Program Seoul National University Seoul151-744 Korea Republic of R&D Center Samsung Electronics Suwon 443-742 Korea Republic of School of Computer Science and Engineering Seoul National University Seoul151-744 Korea Republic of Yongin449-712 Korea Republic of Computer Science and Engineering Cognitive Science and Brain Science Programs Seoul National University Seoul151-744 Korea Republic of

ISBN: (纸本)9780976831891

Video-sharing websites have begun to provide easy access to user-generated video content. How do we find what we want to view among the huge video database? When people search for a video, they may want to know whether the video evokes a certain emotional sensation. The evoked emotion is one of the important factors we consider when we select a video. One of the key concepts of evoked emotions from videos: the evoked emotions are different for each scene and for each viewer. Considering these differences, we obtained human-evoked emotions from 33 videos. We used these emotions to estimate the multiple emotions evoked by each scene of the videos. Using a computational model of emotion estimation based on mid-level visual features, we found that, in individual videos, the same scene evoked multiple emotions. Our results show that a video evoked different emotions from different people. A computational model might deliver probabilistic multiple-evoked emotions from video analyses. © CogSci *** rights reserved.

关键词： Video recording

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Multifunctionality in embodied agents: Three levels of neural reuse

arXiv

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arXiv 2018年

作者： Candadai, Madhavun Izquierdo, Eduardo J. Cognitive Science Program Indiana University BloomingtonIN47406 United States Cognitive Science Program School of Informatics Computing and Engineering Indiana University BloomingtonIN47406 United States

The brain in conjunction with the body is able to adapt to new environments and perform multiple behaviors through reuse of neural resources and transfer of existing behavioral traits. Although mechanisms that underlie this ability are not well understood, they are largely attributed to neuromodulation. In this work, we demonstrate that an agent can be multifunctional using the same sensory and motor systems across behaviors, in the absence of modulatory mechanisms. Further, we lay out the different levels at which neural reuse can occur through a dynamical filtering of the brain-body-environment system's operation: structural network, autonomous dynamics, and transient dynamics. Notably, transient dynamics reuse could only be explained by studying the brain-body-environment system as a whole and not just the brain. The multifunctional agent we present here demonstrates neural reuse at all three levels. Copyright © 2018, The Authors. All rights reserved.

关键词： Dynamical systems

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Human Learners Integrate Visual and Linguistic Information in Cross-Situational Verb Learning 43

Human Learners Integrate Visual and Linguistic Information i...

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43rd Annual Meeting of the cognitive science Society: Comparative Cognition: Animal Minds, CogSci 2021

作者： Zhang, Yayun Amatuni, Andrei Cain, Ellis Wang, Xizi Crandall, David Yu, Chen Department of Psychology University of Texas Austin United States Cognitive Science Program Indiana University Bloomington United States Luddy School of Informatics Computing and Engineering Indiana University Bloomington United States

Learning verbs is challenging because it is difficult to infer the precise meaning of a verb when there are a multitude of relations that one can derive from a single event. To study this verb learning challenge, we used children’s egocentric view collected from naturalistic toy-play interaction as learning materials and investigated how visual and linguistic information provided in individual naming moments as well as cross-situational information provided from multiple learning moments can help learners resolve this mapping problem using the Human Simulation Paradigm. Our results show that learners benefit from seeing children’s egocentric views compared to third-person observations. In addition, linguistic information can help learners identify the correct verb meaning by eliminating possible meanings that do not belong to the linguistic category. Learners are also able to integrate visual and linguistic information both within and across learning situations to reduce the ambiguity in the space of possible verb meanings. © cognitive science Society: Comparative Cognition: Animal Minds, CogSci *** rights reserved.

关键词： Toys

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Self-packaged stretchable printed circuits with ligand-bound liquid metal particles in elastomer

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Nature Communications 2025年第1期16卷 1-11页

作者： Hyeonyeob Seo Gun-Hee Lee Dong-Yeong Kim Kum Seok Nam Jiwoo Park Yeonzu Son Semin Kim Congqi Yang Seongjun Park Joonhee Won Hyunjun Kim Steve Park Jae-Young Bae Seung-Kyun Kang Jiheong Kang Department of Bio and Brain Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea Departments of Cogno-Mechatronics Engineering Pusan National University Busan Republic of Korea Departments of Optics and Mechatronics Engineering Pusan National University Busan Republic of Korea School of Electrical Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea Program of Brain and Cognitive Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon South Korea Graduate School of Semiconductor Technology Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea Medical Research Center Seoul National University Seoul Republic of Korea Department of Biomedical Sciences College of Medicine Seoul National University Seoul Republic of Korea School of Transdisciplinary Innovations Seoul National University Seoul Republic of Korea Interdisciplinary Program in Bioengineering College of Engineering Seoul National University Seoul Republic of Korea Department of Transdisciplinary Medicine Seoul National University Hospital Seoul South Korea Department of Materials Science and Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea KAIST Institute for NanoCentury Daejeon Republic of Korea Department of Materials Science and Engineering Seoul National University Seoul Republic of Korea Department of Chemistry Seoul National University Seoul Republic of Korea

Packaging in stretchable electronics is crucial to protect components from environmental damage while preserving mechanical flexibility and providing electrical insulation. The conventional packaging process involves multiple steps that increase in complexity as the number of circuit layers multiply. In this study, we introduce a self-packaged stretchable printed circuit board enabled by the in situ phase separation of liquid metal particles (LMPs) within various polymer matrices during solution-based printing processes. The ligand-bound LMPs (LB-LMPs), engineered to inhibit oxide growth, undergo in situ sintering, prompting vertical phase separation. This synthesis strategy not only achieves high initial conductivity of the LMPs but also encapsulates them within the polymer matrix, preventing leakage and providing electrical insulation. Our method enables multi-layer circuit printing, eliminating the need for additional activation and packaging processes. Furthermore, by integrating conductive materials into packaging layers for selective electrical conductivity, vertical interconnect accesses and conductive pads can be formed, enabling large-scale, stretchable, and leakage-free multi-layer electrical circuits and bio-interfaces.

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Cortical Network Modeling for Inverse Kinematic Computation of an Anthropomorphic Finger

Cortical Network Modeling for Inverse Kinematic Computation ...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society

作者： Rodolphe J. Gentili Hyuk Oh Javier Molina Jose L. Contreras-Vidal Department of Kinesiology and the Neuroscience and Cognitive Science Program University of Maryland Neuroscience and Cognitive Science Program University of Maryland Department of Systems Engineering and Automation. Technical University of Cartagena Department of Kinesiology Fischell Department of Bioengineering and Neuroscience and Cognitive Science Program University of Maryland

ISBN: (纸本)9781424441211

The performance of reaching movements to visual targets requires complex kinematic mechanisms such as redundant, multijointed, anthropomorphic actuators and thus is a difficult problem since the relationship between sensory and motor coordinates is highly nonlinear. In this article, we present a neural model able to learn the inverse kinematics of a simulated anthropomorphic robot finger (ShadowHand finger) having four degrees of freedom while performing 3D reaching movements. The results revealed that this neural model was able to control accurately and robustly the finger when performing single 3D reaching movements as well as more complex patterns of motion while generating kinematics comparable to those observed in human. The long term goal of this research is to design a bio-mimetic controller providing adaptive, robust and flexible control of dexterous robotic/prosthetics hands.

关键词： Fingers kinematics Degree of freedom inverse kinematics Movements Redundant Anthropomorphic robots

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Three theorems: Brain-like networks logically reason and optimally generalize

Three theorems: Brain-like networks logically reason and opt...

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International Joint Conference on Neural Networks (IJCNN)

作者： Juyang Weng Department of Computer Science and Engineering Cognitive Science Program and Neuroscience Program Michigan State University East Lansing MI USA

Finite Automata (FA) is a base net for many sophisticated probability-based systems of artificial intelligence. However, an FA processes symbols, instead of images that the brain senses and produces (e.g., sensory images and motor images). Of course, many recurrent artificial neural networks process images. However, their non-calibrated internal states prevent generalization, let alone the feasibility of immediate and error-free learning. I wish to report a general-purpose Developmental program (DP) for a new type of, brain-anatomy inspired, networks - Developmental Networks (DNs). The new theoretical results here are summarized by three theorems. (1) From any complex FA that demonstrates human knowledge through its sequence of the symbolic inputs-outputs, the DP incrementally develops a corresponding DN through the image codes of the symbolic inputs-outputs of the FA. The DN learning from the FA is incremental, immediate and error-free. (2) After learning the FA, if the DN freezes its learning but runs, it generalizes optimally for infinitely many image inputs and actions based on the embedded inner-product distance, state equivalence, and the principle of maximum likelihood. (3) After learning the FA, if the DN continues to learn and run, it “thinks” optimally in the sense of maximum likelihood based on its past experience.

关键词： Neurons Tin Automata Humans Brain models Learning systems

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Brains as Optimal Emergent Turing Machines

Brains as Optimal Emergent Turing Machines

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International Joint Conference on Neural Networks

作者： Juyang Weng Department of Computer Science and Engineering Cognitive Science Program and Neuroscience Program Michigan State University East Lansing 48824 USA

ISBN: (纸本)9781509006212

The theory and experiments outlined in Weng 2015 [1] modeled brains as naturally emerging Turing Machines (TMs) inside Developmental Networks (DNs) - a new class of brain inspired neural networks. However, TMs originally proposed by Alan Turing 1936 [2] were deterministic. If they involved probability to handle uncertainty, the probability was in the mind of the human programmer for a specific task. Although the task-specific program will run on a Universal TM - a model of the popular Von Neumann Computers - the Universal TM itself had no imbedded probability. Yet, each brain samples from infinitely many stimuli and actions through the real word, unlike a TM that deals with only tape-symbols from a finite and static alphabet. Computationally, is a brain optimal? If the answer is yes, in what sense? This theoretical work reports the overall brain optimality. It shows that a biological brain, modeled by the DN, is optimal in the sense of maximum likelihood, conditioned on (1) the genome program - the Developmental program (DP) that regulates the development of body, sensors, effectors and the limited computational resource (e.g., brain size and types of neural transmitters), and (2) the incremental lifetime experience - including teaching from the environments and self exploration and discovery through the real world.

关键词： Brain modeling Computational modeling Hidden Markov models Turing machines Biological neural networks Computers Neurons

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Emergent Turing Machine as a General Purpose Approximator

Emergent Turing Machine as a General Purpose Approximator

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International Joint Conference on Neural Networks

作者： Zejia Zheng Xiang Wu Juyang Weng Cognitive Science Program Department of Computer Science and Engineering Neuroscience Program Michigan State University East Lansing MI USA

An autonomous navigation agent needs to learn the navigation rules and the transition among different navigation states, which can be summarized in Turing Machine (TM) controlled by a navigation Finite Automata. However, real-world inputs to this TM is always noisy with numerous appearance variances and distractors, which made it impossible to handcraft symbolic representations or features to detect. Developmental Network-1 (DN-1) incrementally learns a Finite Automata error-free using emergent representation but lacks hierarchy in its internal representation. In this paper, we extend DN-1 by adding lateral connection and multiple types of neurons to form hierarchical representation both internally and in the motor area. The formed hierarchical representation is more robust against distractors compared to the global feature patches in DN-1. Compared to other FA based methods, DN-2 directly uses natural input without handcrafting. The navigation states are emergent from the firing patterns in network areas instead of the hand-crafted symbolic states in the neural network output layer. Real-world and simulated navigation experiments showed that DN-2 successfully approximated rules of navigation using natural inputs. The agent learns different levels of concepts from simple to complex with the help of internal hierarchical representation.

关键词： Neurons Navigation Turing machines Biological neural networks Task analysis Head Magnetic heads

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Downstream Task Performance of Pre-Trained Model For Outdoor Audio Event Classification 9

Downstream Task Performance of Pre-Trained Model For Outdoor...

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9th International Conference on Signal Processing and Intelligent Systems, ICSPIS 2023

作者： Rochadiani, Theresia Herlina Heryadi, Yaya Sonata, Ilvico Lukas Suparta, Wawan Bina Nusantara University Binus Graduate Program- Computer Science Jakarta Indonesia Universitas Katolik Indonesia Atma Jaya Cognitive Engineering Research Group Jakarta Indonesia Institut Teknologi Nasional Yogyakarta Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350369922

Our surroundings' auditory landscapes are a wealth of knowledge, providing insights into a range of outdoor pursuits. The automatic classification of these actions using audio event detection and classification (AEDC) systems has a great deal of potential applications in environmental monitoring, security, surveillance, and driverless cars. Even though AEDC has advanced significantly in indoor settings, difficulties still exist outside because of the variable and changing acoustic conditions brought on by elements like the weather, different sound sources, and ambient noise from traffic and industry. This study suggests an outdoor audio event categorization model based on convolutional neural networks (CNNs). The suggested model shows passable accuracy by utilizing adaptation to the downstream task using the ESC-50 dataset and transfer learning from a pre-trained model. The effectiveness of multi-class audio classification models in downstream tasks is analyzed in this paper, with an emphasis on the effect of an increasing number of output classes on accuracy. Three models - three, four, and five classes - with different output class configurations are used in the study, and their training and validation accuracies are assessed. Although the accuracy scores above 80% are commendable, the data show a discernible reduction in performance as the number of classes rises. Notably, the three-class model attains a validation accuracy exceeding 90%, whereas the four-class and five-class models exhibit reduced accuracies, falling below 90% and 85%, respectively. © 2023 IEEE.

关键词： Convolutional neural networks

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