Attention is a complex and broad concept, studied across multiple disciplines spanning artificial intelligence, cognitive science, psychology, neuroscience, and related fields. Although many of the ideas regarding attention do not significantly overlap among these fields, there is a common theme of adaptive control of limited resources. In this work, we review the concept and variants of attention in artificial neural networks (ANNs). We also discuss the origin of attention from the neuroscience point of view parallel to that of ANNs. Instead of having seemingly disconnected dialogues between varied disciplines, we suggest grounding the ideas on common conceptual frameworks for a systematic analysis of attention and towards possible unification of ideas in AI and Neuroscience.
Conscious Intelligent Systems - Part II - Mind, Thought, Language and Understanding
这是一篇关于意识智能系统的论文的第二部分。我们使用在第一部分（意识智能系统第1部分：IXI（arxiv id cs.AI/0612056））中获得的理解来看待理解。我们看到心灵的存在如何影响理解和智能系统;我们看到心灵的存在需要语言。语言的兴起反过来又对理解产生了重要影响。我们讨论了类人的问题，以及自我意识的问题（以及心灵/思想/语言）如何影响类人。
MIA-Mind: A Multidimensional Interactive Attention Mechanism Based on MindSpore
School of Information Technology（信息科技学院）；Network Security Research Center（网络安全研究中心）；Big Data and Policing Technology Laboratory（大数据与警务技术实验室）；Guangxi Police College（广西警察学院）
IMT Lille Douai（IMT里尔杜埃）；CRIStAL UMR CNRS 9189（法国国家科学研究中心信息、自动化与信号处理实验室）；University of Mons（蒙斯大学）
Visual attention estimation is an active field of research at the crossroads of different disciplines: computer vision, artificial intelligence and medicine. One of the most common approaches to estimate a saliency map representing attention is based on the observed images. In this paper, we show that visual attention can be retrieved from EEG acquisition. The results are comparable to traditional predictions from observed images, which is of great interest. For this purpose, a set of signals has been recorded and different models have been developed to study the relationship between visual at…
Humboldt-University of Berlin（柏林洪堡大学）；University of Tartu（塔尔图大学）
Can deep learning (DL) guide our understanding of computations happening in biological brain? We will first briefly consider how DL has contributed to the research on visual object recognition. In the main part we will assess whether DL could also help us to clarify the computations underlying higher cognitive functions such as Theory of Mind. In addition, we will compare the objectives and learning signals of brains and machines, leading us to conclude that simply scaling up the current DL algorithms will most likely not lead to human level Theory of Mind.
Mind What Matters for Reasoning: Aligning Cross-Modal Attention via Selective Probability Mass Concentration
University of Technology Sydney（悉尼科技大学）；East China Normal University（华东师范大学）；The University of New South Wales（新南威尔士大学）；Shanghai Jiaotong University（上海交通大学）；The Education University of Hong Kong（香港教育大学）
We present modality gap, an intriguing geometric phenomenon of the representation space of multi-modal models. Specifically, we show that different data modalities (e.g. images and text) are embedded at arm's length in their shared representation in multi-modal models such as CLIP. Our systematic analysis demonstrates that this gap is caused by a combination of model initialization and contrastive learning optimization. In model initialization, we show empirically and theoretically that the representation of a common deep neural network is restricted to a narrow cone. As a consequence, in a mu…
UCLA（加州大学洛杉矶分校）；University of Michigan（密歇根大学）；Oregon State University（俄勒冈州立大学）；Beijing Institute for General AI (BIGAI)（北京通用人工智能研究院）；Tsinghua University（清华大学）；Peking University（北京大学）
We propose CX-ToM, short for counterfactual explanations with theory-of mind, a new explainable AI (XAI) framework for explaining decisions made by a deep convolutional neural network (CNN). In contrast to the current methods in XAI that generate explanations as a single shot response, we pose explanation as an iterative communication process, i.e. dialog, between the machine and human user. More concretely, our CX-ToM framework generates sequence of explanations in a dialog by mediating the differences between the minds of machine and human user. To do this, we use Theory of Mind (ToM) which …
Mind the Gap! Bridging Explainable Artificial Intelligence and Human Understanding with Luhmann's Functional Theory of Communication
School of Law, Birkbeck, University of London（伦敦大学伯贝克学院法学院）；Department of Computer Science, ETH Zurich（苏黎世联邦理工学院计算机科学系）；ARC Centre of Excellence for Automated Decision-Making and Society, RMIT University（皇家墨尔本理工大学自动化决策与社会ARC卓越研究中心）
Machine learning of Theory of Mind (ToM) is essential to build social agents that co-live with humans and other agents. This capacity, once acquired, will help machines infer the mental states of others from observed contextual action trajectories, enabling future prediction of goals, intention, actions and successor representations. The underlying mechanism for such a prediction remains unclear, however. Inspired by the observation that humans often infer the character traits of others, then use it to explain behaviour, we propose a new neural ToM architecture that learns to generate a latent…
Understanding Artificial Theory of Mind: Perturbed Tasks and Reasoning in Large Language Models
Bonn-Aachen International Center for Information Technology (b-it)（波恩-埃森国际信息科技中心）；University of Bonn（波恩大学）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔机器学习与人工智能研究所）；Research Center Trustworthy Data Science and Security (RC-Trust)（可信数据科学与安全研究中心）；University of Duisburg-Essen（埃森-杜伊斯堡大学）
本研究通过扰动任务和链式推理提示探讨大语言模型的理论 of mind能力，发现其鲁棒性受扰动影响显著，CoT提示虽提升整体表现，但对某些扰动类别却降低准确性。
Human social behavior is structured by relationships. We form teams, groups, tribes, and alliances at all scales of human life. These structures guide multi-agent cooperation and competition, but when we observe others these underlying relationships are typically unobservable and hence must be inferred. Humans make these inferences intuitively and flexibly, often making rapid generalizations about the latent relationships that underlie behavior from just sparse and noisy observations. Rapid and accurate inferences are important for determining who to cooperate with, who to compete with, and ho…
Computational complexity has often been ignored in philosophy of mind, in philosophical artificial intelligence studies. The purpose of this paper is threefold. First and foremost, to show the importance of complexity rather than computability in philosophical and AI problems. Second, to rephrase the notion of computability in terms of solvability, i.e. treating computability as non-sufficient for establishing intelligence. The Church-Turing thesis is therefore revisited and rephrased in order to capture the ontological background of spatial and temporal complexity. Third, to emphasize ontolog…
We present a new explainable AI (XAI) framework aimed at increasing justified human trust and reliance in the AI machine through explanations. We pose explanation as an iterative communication process, i.e. dialog, between the machine and human user. More concretely, the machine generates sequence of explanations in a dialog which takes into account three important aspects at each dialog turn: (a) human's intention (or curiosity); (b) human's understanding of the machine; and (c) machine's understanding of the human user. To do this, we use Theory of Mind (ToM) which helps us in explicitly mod…
Fair in Mind, Fair in Action? A Synchronous Benchmark for Understanding and Generation in UMLLMs
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；The Chinese University of Hong Kong（香港中文大学）；School of Software Technology, Zhejiang University（浙江大学软件学院）；Ningbo Global Innovation Center, Zhejiang University（浙江大学宁波全球创新中心）；Collaborative Innovation Center of Novel Software Technology and Industrialization（新型软件技术与产业化协同创新中心）
MMToM-QA: Multimodal Theory of Mind Question Answering
New York University（纽约大学）；Harvard University（哈佛大学）；Massachusetts Institute of Technology（麻省理工学院）；UC San Diego（加州大学圣迭戈分校）；University of Virginia（弗吉尼亚大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Universidad Autonoma de Madrid（马德里自治大学）；Universidad Politecnica de Madrid（马德里理工大学）
This work seeks to study the beneficial properties that an autonomous agent can obtain by implementing a cognitive architecture similar to the one of conscious beings. Along this document, a conscious model of autonomous agent based in a global workspace architecture is presented. We describe how this agent is viewed from different perspectives of philosophy of mind, being inspired by their ideas. The goal of this model is to create autonomous agents able to navigate within an environment composed of multiple independent magnitudes, adapting to its surroundings in order to find the best possib…
Psychomatics -- A Multidisciplinary Framework for Understanding Artificial Minds
Catholic University of Sacred Heart（圣心天主教大学）；Istituto Auxologico Italiano IRCCS（意大利奥索洛吉科研究所）；University of Milano - Bicocca（米兰比可卡大学）；Virtual Reality Medical Center（虚拟现实医疗中心）；Interactive Media Institute（交互媒体研究所）
Sensitivity Meets Sparsity: The Impact of Extremely Sparse Parameter Patterns on Theory-of-Mind of Large Language Models
Stanford University（斯坦福大学）；Princeton University（普林斯顿大学）；University of Minnesota Twin Cities（明尼苏达大学双城分校）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Stevens Institute of Technology（斯蒂文斯理工学院）
本文从机制视角研究大语言模型心智理论能力的涌现，提出识别 ToM 敏感参数的方法，发现仅扰动 0.001% 的极端稀疏参数即可显著损害 ToM 性能，并揭示其与 RoPE 位置编码及注意力机制的深层关联。
