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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Haeyoung Lee Sunhee Kim Minhwa Chung Interdisciplinary Program in Cognitive Science Seoul National University Seoul Republic of Korea Department of French Language Education Seoul National University Seoul Republic of Korea Department of Linguistics Seoul National University Seoul Republic of Korea

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

The advancement of Automatic Pronunciation Assessment (APA) systems has been significantly improved by Self-supervised Learning (SSL) models. However, despite these performance gains, there remains a lack of systematic research on effective utilization of SSL models and the explainability of their behavior in APA. This study aims to evaluate pronunciation with high accuracy using SSL models and to provide explanations for the scoring outcomes. To achieve this, we fine-tune various SSL models using multiple strategies, comparing their performance through extrinsic analysis to identify the key factors influencing performance improvements. Furthermore, intrinsic analysis is conducted using Principal Component Analysis (PCA) to gain insights into the model’s scoring patterns. Extrinsic analysis highlights the importance of strategic fine-tuning and acoustic similarity between fine-tuning and pre-training datasets. Intrinsic analysis reveals that different SSL models focus on distinct pronunciation features, with the Wav2Vec2.0 model capturing more advantageous information for APA. This study presents the first in-depth analysis of SSL models in APA, proposing a novel intrinsic analysis method based on feature distribution manifolds. We provide model-specific fine-tuning guidelines for APA tasks and recommend appropriate SSL models based on specific pronunciation assessment goals. This research significantly contributes to the future development of explainable APA systems based on SSL models.

关键词： Manifolds Analytical models Systematics Shape Self-supervised learning Information processing Performance gain Vectors Principal component analysis Guidelines

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Building human-like communicative intelligence: a grounded perspective

arXiv

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arXiv 2022年

作者： Dubova, Marina Cognitive Science Program Indiana University 1101 E. 10th Street BloomingtonIN47405 United States

Modern Artificial Intelligence (AI) systems excel at diverse tasks, from image classification to strategy games, even outperforming humans in many of these domains. After making astounding progress in language learning in the recent decade, AI systems, however, seem to approach the ceiling that does not reflect important aspects of human communicative capacities. Unlike human learners, communicative AI systems often fail to systematically generalize to new data, suffer from sample inefficiency, fail to capture common-sense semantic knowledge, and do not translate to real-world communicative situations. cognitive science offers several insights on how AI could move forward from this point. This paper aims to: (1) suggest that the dominant cognitively-inspired AI directions, based on nativist and symbolic paradigms, lack necessary substantiation and concreteness to guide progress in modern AI, and (2) articulate an alternative, "grounded", perspective on AI advancement, inspired by Embodied, Embedded, Extended, and Enactive Cognition (4E) research. I review results on 4E research lines in cognitive science to distinguish the main aspects of naturalistic learning conditions that play causal roles for human language development. I then use this analysis to propose a list of concrete, implementable components for building "grounded" linguistic intelligence. These components include embodying machines in a perception-action cycle, equipping agents with active exploration mechanisms so they can build their own curriculum, allowing agents to gradually develop motor abilities to promote piecemeal language development, and endowing the agents with adaptive feedback from their physical and social environment. I hope that these ideas can direct AI research towards building machines that develop human-like language abilities through their experiences with the world. © 2022, CC BY-NC-ND.

关键词： Artificial intelligence

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CAUS: A Dataset for Question Generation based on Human Cognition Leveraging Large Language Models

arXiv

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arXiv 2024年

作者： Shin, Minjung Kim, Donghyun Ryu, Jeh-Kwang Interdisciplinary Program in Cognitive Science Seoul National University Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of Department of Physical Education Dongguk University Pildong-ro Jung-gu Seoul08826 Korea Republic of

We introduce the Curious About Uncertain Scene (CAUS) dataset, designed to enable Large Language Models, specifically GPT-4, to emulate human cognitive processes for resolving uncertainties. Leveraging this dataset, we investigate the potential of LLMs to engage in questioning effectively. Our approach involves providing scene descriptions embedded with uncertainties to stimulate the generation of reasoning and queries. The queries are then classified according to multidimensional criteria. All procedures are facilitated by a collaborative system involving both LLMs and human researchers. Our results demonstrate that GPT-4 can effectively generate pertinent questions and grasp their nuances, particularly when given appropriate context and instructions. The study suggests that incorporating human-like questioning into AI models improves their ability to manage uncertainties, paving the way for future advancements in Artificial Intelligence (AI). Copyright © 2024, The Authors. All rights reserved.

关键词： Large datasets

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Adaptive Robot-Mediated Assessment using LLM for Enhanced Survey Quality in Older Adults Care programs 25

Adaptive Robot-Mediated Assessment using LLM for Enhanced Su...

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ACM/IEEE International Conference on Human-Robot Interaction (HRI)

作者： Cheonshu Park Miyoung Cho Minjung Shin Jeh-Kwang Ryu Minsu Jang Social Robotics Research Lab Electronics and Telecommunications Research Institute (ETRI) Daejeon Republic of Korea Interdisciplinary Program in Cognitive Science Seoul National University Seoul Republic of Korea Department of Physical Education Dongguk University Seoul Republic of Korea

ISBN: (数字)9798350378931

ISBN: (纸本)9798350378948

This study presents an adaptive human-robot interaction (HRI) system that evaluates older adult participants' satisfaction with personalized health care programs. By integrating the CLOi robot with a large language model (LLM), the system conducts satisfaction surveys that adapt in real-time to participant responses. The system was applied to evaluate healthcare programs that include physical health measurements, exercise assessments, and virtual reality (VR) experiences. The system utilizes the CLOi robot and Claude API to analyze response clarity in real-time, automatically generating contextually appropriate follow-up questions when responses are deemed ambiguous. This adaptive questioning strategy ensures comprehensive response quality before proceeding to subsequent survey items. We conducted a preliminary feasibility study with five older adult participants to evaluate our approach. The system leverages LLM prompts to analyze gaps between question intent and participant responses, generating targeted follow-up questions as needed. Results demonstrate that our LLM-enhanced robotic interview system effectively reduced response ambiguity through dynamic follow-up questioning, achieving an 85% response resolution rate. This adaptive approach improved the clarity and specificity of healthcare satisfaction assessments for older adults.

关键词： Surveys Large language models Human-robot interaction Medical services Virtual reality Particle measurements Real-time systems Question generation Older adults Robots

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Over the Quantum Rainbow: Explaining Hybrid Quantum Reinforcement Learning

Over the Quantum Rainbow: Explaining Hybrid Quantum Reinforc...

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Quantum Computing and Engineering (QCE), IEEE International Conference on

作者： Junghoon Park Jiook Cha Samuel Yen-Chi Chen Shinjae Yoo Huan-Hsin Tseng Interdisciplinary Program in Artificial Intelligence Seoul National University Department of Psychology Seoul National University Department of Brain and Cognitive Sciences Seoul National University Graduate School of Data Science Seoul National University Wells Fargo Computational Science Initiative Brookhaven National Laboratory

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

In the realm of artificial intelligence, deep rein-forcement learning (RL) agents struggle with generalizability and require substantial computational resources, unlike humans who easily adapt and generalize across tasks. To address these challenges, we introduce Quantum Rainbow, a hybrid algorithm that leverages the neural mechanisms of human decision-making and the efficiency of quantum computing. Quantum Rainbow combines variational quantum circuits with the Rainbow Deep Q-Network (DQN) model to create a novel approach in rein-forcement learning that integrates quantum principles into deep learning paradigms. We evaluate our model using behavioral experiments through the Iowa Gambling Task and 4-Armed Bandit Task. Our investigations reveal a significant relationship between the architecture of quantum circuits and the performance of quantum RL agents. Specifically, using causal discovery methods, we demonstrate the critical role of quantum entanglement in enhancing model performance. These findings not only show promising results but also pave the way for future explorations into optimizing quantum circuit architectures for reinforcement learning applications. This study underscores the potential of quantum-enhanced algorithms to achieve “quantum advantage” by addressing fundamental limitations in conventional deep RL methods.

关键词： Analytical models Quantum computing Quantum algorithm Quantum advantage Quantum entanglement Computational modeling Reinforcement learning Quantum state Integrated circuit modeling Quantum circuit

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A Novel Interpretable Fusion Analytic Framework for Investigating Functional Brain Connectivity Differences in cognitive Impairments

arXiv

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arXiv 2024年

作者： Jeon, Yeseul Kim, Jeong-Jae Yu, SuMin Choi, Junggu Han, Sanghoon Department of Statistics and Data Science Yonsei University Seoul Korea Republic of Graduate Program in Cognitive Science Yonsei University Seoul Korea Republic of Department of Psychology and Neuroscience Duke University DurhamNC United States Department of Psychology Yonsei University Seoul Korea Republic of

Functional magnetic resonance imaging (fMRI) data is characterized by its complexity and high-dimensionality, encompassing signals from various regions of interests (ROIs) that exhibit intricate correlations. Analyzing fMRI data directly proves challenging due to its intricate structure. Nevertheless, ROIs convey crucial information about brain activities through their connections, offering insights into distinctive brain activity characteristics between different groups. To address this, we propose a cuttingedge interpretable fusion analytic framework that facilitates the identification and understanding of ROI connectivity disparities between two groups, thereby revealing their unique features. Our novel approach encompasses three key steps. Firstly, we construct ROI functional connectivity networks (FCNs) to effectively manage fMRI data. Secondly, employing the FCNs, we utilize a self-attention deep learning model for binary classification, generating an attention distribution that encodes group differences. Lastly, we employ a latent space item-response model to extract group representative ROI features, visualizing these features on the group summary FCNs. We validate the effectiveness of our framework by analyzing four types of cognitive impairments, showcasing its capability to identify significant ROIs contributing to the differences between the two disease groups. This novel interpretable fusion analytic framework holds immense potential for advancing our understanding of cognitive impairments and could pave the way for more targeted therapeutic interventions. Copyright © 2024, The Authors. All rights reserved.

关键词： Neurophysiology

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Crosslinguistic word order variation reflects evolutionary pressures of dependency and information locality

arXiv

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arXiv 2022年

作者： Hahn, Michael Xu, Yang Department of Linguistics Stanford University United States SFB 1102 Saarland University Germany Department of Computer Science Cognitive Science Program University of Toronto Canada

Languages vary considerably in syntactic structure. About 40% of the world’s languages have subject-verb-object order, and about 40% have subject-object-verb order. Extensive work has sought to explain this word order variation across languages. However, the existing approaches are not able to explain coherently the frequency distribution and evolution of word order in individual languages. We propose that variation in word order reflects different ways of balancing competing pressures of dependency locality and information locality, whereby languages favor placing elements together when they are syntactically related or contextually informative about each other. Using data from 80 languages in 17 language families and phylogenetic modeling, we demonstrate that languages evolve to balance these pressures, such that word order change is accompanied by change in the frequency distribution of the syntactic structures which speakers communicate to maintain overall efficiency. Variability in word order thus reflects different ways in which languages resolve these evolutionary pressures. We identify relevant characteristics that result from this joint optimization, particularly the frequency with which subjects and objects are expressed together for the same verb. Our findings suggest that syntactic structure and usage across languages co-adapt to support efficient communication under limited cognitive resources. © 2022, CC BY-NC-ND.

关键词： Modeling languages

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Chaining and historical adjective extension 42

Chaining and historical adjective extension

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42nd Annual Meeting of the cognitive science Society: Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020

作者： Grewal, Karan Xu, Yang Department of Computer Science University of Toronto Canada Department of Computer Science Cognitive Science Program University of Toronto Canada

A hallmark of natural language is the innovative reuse of existing words. We examine how adjectives extend over time to describe nouns and form previously unattested adjective-noun pairings. Our approach is based on the idea of chaining that postulates word meaning to extend by linking novel referents to existing ones that are close in semantic space. We test this proposal by exploring a set of models that learn to infer adjective-noun pairings from historical text corpora for a period of 150 years. Our findings across three diverse sets of adjectives support a chaining mechanism that is sensitive to semantic neighbourhood density, best captured by an exemplar model of category extension. This work sheds light on the generative cognitive mechanisms of word usage extension. © 2020 The Author(s)

关键词： Semantics

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Not any gifted is an expert in mathematics and not any expert in mathematics is gifted

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Gifted and Talented International 2022年第1期37卷 25-41页

作者： Paz-Baruch, Nurit Leikin, M. Leikin, R. The Science and Technology Education Program Bar-Ilan University Ramat-Gan Israel Department of Mathematics Education Range (Research and Advancement of Giftedness and Excellence) Center Neuro-cognitive Laboratory for the Investigation of Creativity Ability and Giftedness University of Haifa Haifa Israel Departments of Special Education and Learning Disabilities Faculty of Education University of Haifa Haifa Israel

Mathematical giftedness (MG) is an intriguing phenomenon, the nature of which has yet to be sufficiently explored. This study goes a step further in understanding how MG is related to expertise in mathematics (EM) and general giftedness (G). cognitive testing was conducted among 197 high school students with different levels of G and of EM. Based on our previous studies, we perceive MG as a combination of G and EM. Exploratory factor analysis of test results revealed five main cognitive factors: visual-serial processing (VSP);arithmetic abilities (AA);pattern recognition (PR);auditory working memory (AWM);visual-spatial working memory (VSWM);and Structural equation modeling (SEM) based on the factor analysis revealed clear differences in the role of cognitive abilities as predictors of EM, G, and MG. The study demonstrates that visual components are especially important for the development of EM and that G students are less dependent on their visual cognitive processing. Based on the study results, we argue that EM, G, and MG, which are often considered equivalent characteristics, are interrelated but different in nature. The paper presents a research-based justification that not any gifted is an expert in mathematics and not any expert in mathematics is gifted. © 2022 World Council for Gifted and Talented Children.

关键词： cognitive abilities expertise in mathematics general giftedness Mathematical giftedness

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How nouns surface as verbs: Inference and generation in word class conversion 42

How nouns surface as verbs: Inference and generation in word...

引用

42nd Annual Meeting of the cognitive science Society: Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020

作者： Yu, Lei El Sanyoura, Lana Xu, Yang Department of Computer Science University of Toronto Canada Department of Computer Science Cognitive Science Program University of Toronto Canada

Word class conversion refers to the extended use of a word from one grammatical class to another without overt morphological marking. Noun-to-verb conversion, or denominalization, is one form of word class conversion studied extensively in the literature. Previous work has suggested that novel denominal verb usages are comprehensible if the listener can compute the intended meaning based on shared knowledge with the speaker. However, no existing work has explored the computational mechanism under this proposal. We propose a frame-semantic generative model, Noun2Verb, that supports the inference and generation of novel denominal verb usages via semi-supervised learning. We evaluate this framework in a dataset of denominal verbs drawn from adults and children against a state-of-the-art model from natural language processing. Our results show that Noun2Verb aligns better with human interpretation and bridges the gap between machines and humans in lexical innovation. © 2020 The Author(s)

关键词： Semantics

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