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Collaborating with a robot biases human spatial attention

iscience 2025年第7期28卷

作者： Giulia Scorza Azzarà Joshua Zonca Francesco Rea Joo-Hyun Song Alessandra Sciutti Robotics Brain and Cognitive Sciences Unit Italian Institute of Technology 16152 Genoa GE Italy Department of Computer Science Bioengineering Robotics and Systems Engineering University of Genoa 16145 Genoa GE Italy Cognitive Architecture for Collaborative Technologies (CONTACT) Unit Italian Institute of Technology 16152 Genoa GE Italy Department of Cognitive & Psychological Sciences Brown University Providence RI 02912 USA

Advancements in collaborative technologies have increased the accessibility of human-robot interaction (HRI), yet a gap remains in understanding how HRI affects human systems. This work investigates how HRI influences human visual processing, specifically through the near-hand effect, a phenomenon where the proximity of one’s hand enhances spatial attention. Research has shown that this attentional priority can extend to another person’s hand during collaborative tasks, integrating it into an individual’s body schema. A critical question arises: can a robot’s anthropomorphic hand similarly bias human attentional priority? Our findings revealed that collaborative HRI facilitates target detection near the robot’s hand, an effect absent before the interaction. Additionally, we examined social and kinematic metrics that enhance this attentional shift by fostering joint body schema formation. These results highlight that HRI can shape human visual processing and body schema integration, offering insight into the interplay between our perceptual and cognitive systems and robotic collaborators.

关键词： Classification Description Robotics Social sciences

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On the relevance of Maslow's need theory in the age of artificial intelligence

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Technological Forecasting and Social Change 2025年 219卷

作者： Christian Montag A. Mehdi Riazi George Mikros Benjamin Becker Raian Ali Centre for Cognitive and Brain Sciences Institute of Collaborative Innovation University of Macau Macau SAR China Department of Computer and Information Science Faculty of Science and Technology University of Macau Macau SAR China Department of Psychology Faculty of Social Sciences University of Macau Macau SAR China College of Humanities and Social Sciences Hamad Bin Khalifa University Doha Qatar State Key Laboratory of Brain and Cognitive Sciences The University of Hong Kong Hong Kong SAR China Department of Psychology The University of Hong Kong Hong Kong SAR China College of Science and Engineering Hamad Bin Khalifa University Doha Qatar

The impact of Artificial Intelligence (AI) is being felt in societies worldwide and is increasingly affecting all aspects of our daily lives. This paper discusses how AI can foster or hinder human well-being. In detail, we adopt Maslow's classic theory on human needs to take a human-centered focus and discuss how this theory can guide the creation and governance of AI systems promoting human well-being. In addition, the present work explores the concept of humanity and being human in the age of AI and the challenges humans may encounter in the next decades.

关键词： Artificial intelligence Human needs Maslow Personality IMPACT-framework Self-determination-theory

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Reler: Relearning Controversial Regions to Accurately Segment Nasopharyngeal Carcinoma

Reler: Relearning Controversial Regions to Accurately Segmen...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Guihua Tao Haojiang Li Dandan Lu Ziqin Ling Lizhi Liu Hongmin Cai School of Computer Science and Engineering South China University of Technology Guangzhou China State Key Laboratory of Oncology in South China Collaborative Innovation Center for Cancer Medicine Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Sun Yat-sen University Cancer Center Sun Yat-sen University Guangzhou China Brain and Affective Cognitive Research Center Pazhou Lab Guangzhou China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Accurate nasopharyngeal carcinoma (NPC) segmentation is significant in preventing local recurrence and improving patients’ survival rates. However, existing deep learning-based methods often yield unsatisfactory segmentation results, especially in fine-grained detail. Because NPC is a tiny and infiltrative tumor with a huge background, traditional deep neural networks tend to be dominated by salient information, thus missing the fine-grained details of NPC. To achieve accurate NPC segmentation, a relearning controversial regions method (Reler) is proposed. It consists of three modules, including the controversial features generator (CFG), controversial features finding module (CFF), and controversial regions arbitration module (CRA). First, CFG constructs global and local feature extractors to generate two types of different features. Then, CFF finds the controversial features and corresponding regions by comparing the global and local features’ estimates of the segmentation results of the same input regions. Next, the CRA focuses on controversial features, relearns new features, and produces new segmentation results through a proposed Transformer-based self-attention network. Finally, the uncontroversial segmentation results from CFF and CRA are combined as the final segmentation results. Extensive experiments are conducted on a large NPC dataset containing 6342 images from 596 patients. The experimental results show that the proposed method Reler is effective and superior to the nine state-of-the-art methods.

关键词： Learning systems Deep learning Image segmentation Neural networks Feature extraction Transformers Generators

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A Tool to Facilitate Web-Browsing

arXiv

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arXiv 2024年

作者： Kelly, Christopher A. Fontanez, Jonatan Sharot, Tali Department of Experimental Psychology University College London LondonWC1H 0AP United Kingdom Max Planck University College London Centre for Computational Psychiatry and Ageing Research LondonWC1B 5EH United Kingdom Department of Brain and Cognitive Sciences Massachusetts Institute of Technology United States Institute for Human-Centered AI Stanford University StanfordCA United States Department of Psychology Stanford University StanfordCA United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States

Search engine results often misalign with users' goals due to opaque algorithms, leading to unhelpful or detrimental information consumption. To address this, we developed a Google Chrome plugin that provides "content labels" for webpages in Google search results, assessing Actionability (guiding actions), Knowledge (enhancing understanding), and Emotion. Using natural language processing and machine learning, the plugin predicts these properties from webpage text based on models trained on participants' ratings, effectively reflecting user perceptions. The implications include enhanced user control over information consumption and promotion of healthier engagement with online content, potentially improving decision-making and well-being. © 2024, CC BY-NC-SA.

关键词： Search engines

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Harmonized-Multinational qEEG norms (HarMNqEEG)

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NEUROIMAGE 2022年 256卷 119190-119190页

作者： Li, Min Wang, Ying Lopez-Naranjo, Carlos Hu, Shiang Reyes, Ronaldo Cesar Garcia Paz-Linares, Deirel Areces-Gonzalez, Ariosky Hamid, Aini Ismafairus Abd Evans, Alan C. Savostyanov, Alexander N. Calzada-Reyes, Ana Villringer, Arno Tobon-Quintero, Carlos A. Garcia-Agustin, Daysi Yao, Dezhong Dong, Li Aubert-Vazquez, Eduardo Reza, Faruque Razzaq, Fuleah Abdul Omar, Hazim Abdullah, Jafri Malin Galler, Janina R. Ochoa-Gomez, John F. Prichep, Leslie S. Galan-Garcia, Lidice Morales-Chacon, Lilia Valdes-Sosa, Mitchell J. Trondle, Marius Zulkifly, Mohd Faizal Mohd Rahman, Muhammad Riddha Bin Abdul Milakhina, Natalya S. Langer, Nicolas Rudych, Pavel Koenig, Thomas Virues-Alba, Trinidad A. Lei, Xu Bringas-Vega, Maria L. Bosch-Bayard, Jorge F. Valdes-Sosa, Pedro Antonio [a]The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China [b]Cuban Center for Neurocience La Habana Cuba [c]McGill Centre for Integrative Neuroscience Ludmer Centre for Neuroinformatics and Mental Health Montreal Neurological Institute Canada [d]Department of Neurosciences School of Medical Sciences Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [e]Brain and Behaviour Cluster School of Medical Sciences Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [f]Hospital Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [g]Humanitarian Institute Novosibirsk State University Novosibirsk 630090 Russia [h]Laboratory of Psychophysiology of Individual Differences Federal State Budgetary Scientific Institution Scientific Research Institute of Neurosciences and Medicine Novosibirsk 630117 Russia [i]Laboratory of Psychological Genetics at the Institute of Cytology and Genetics Siberian Branch of the Russian Academy of Sciences Novosibirsk 630090 Russia [j]University of Pinar del Río “Hermanos Saiz Montes de Oca” Pinar del Río Cuba [k]Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany [l]Department of Cognitive Neurology University Hospital Leipzig Leipzig Germany [m]Center for Stroke Research Charité-Universitätsmedizin Berlin Berlin Germany [n]Grupo Neuropsicología y Conducta - GRUNECO Faculty of Medicine Universidad de Antioquia Colombia [o]Research Department Institución Prestadora de Servicios de Salud IPS Universitaria Colombia [p]The Cuban center aging longevity and health Havana Cuba [q]Research Unit of NeuroInformation Chinese Academy of Medical Sciences Chengdu 2019RU035 China [r]School of Electrical Engineering Zhengzhou University Zhengzhou 4500

This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects " and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.

关键词： Quantitative EEG EEG cross-spectrum Riemannian geometry Batch effects Z-score Harmonization Malnutrition Covid induced brain dysfunction Developmental brain Chart

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Classifying Oscillatory Signatures of Expert vs NonExpert Meditators

Classifying Oscillatory Signatures of Expert vs NonExpert Me...

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International Joint Conference on Neural Networks (IJCNN)

作者： Pankaj Pandey Krishna Prasad Miyapuram Computer Science & Engineering Indian Institute of Technology Gandhinagar Gujarat India Center for Cognitive and Brain Sciences Indian Institute of Technology Gandhinagar Gujarat India

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

EEG oscillatory correlates of expert meditators have been studied in the time-frequency domain. Machine Learning techniques are required to expand the understanding of oscillatory signatures. In this work, we propose a methodological pipeline to develop machine learning models for the classification between expert and nonexpert meditative state. We carried out this study utilizing the online repository consisting of EEG dataset of 24 meditators that categorized as 12 experts and 12 nonexperts meditators. The pipeline consists of four stages that include feature engineering, machine learning classifiers, feature selection, and visualization. We decomposed signals using five wavelet families consisting of Haar, Biorthogonal(1.3-6.8), Daubechies( orders 2-10), Coiflet(orders 1-5), and Symlet(2-8), followed by feature extraction using relative entropy and power. We classified the meditative state between expert and non-expert meditators employing twelve classifiers to build machine learning models. Wavelet coefficients d8 shows the maximum classification accuracy in all the wavelet families. Wavelet orders Bior3.5 and Coif3 produce the maximum classification performance with the detail coefficient d8 using relative power. We have successfully classified the meditative state between expert and non-expert with 100% accuracy using d5,d6,d7,d8,a8 coefficients. Multi-Layer Perceptron and Quadratic Discriminant Analysis attain the highest accuracy. We have figured out the most discriminating channels during classification and reported 20 channels involving frontal, central and parietal regions. We plot the high dimensional structure of data by utilizing two feature reduction techniques PCA and t-SNE.

关键词： Feature extraction Machine learning Electroencephalography Pipelines Entropy Wavelet transforms Time-frequency analysis

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An Attention-Driven Two-Stage Clustering Method for Unsupervised Person Re-identification 1

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16th European Conference on computer Vision, ECCV 2020

作者： Ji, Zilong Zou, Xiaolong Lin, Xiaohan Liu, Xiao Huang, Tiejun Wu, Si State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China IDG/McGovern Institute for Brain Research Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary Studies Peking University Beijing China

ISBN: (数字)9783030586041

ISBN: (纸本)9783030586034

The progressive clustering method and its variants, which iteratively generate pseudo labels for unlabeled data and per form feature learning, have shown great process in unsupervised person re-identification (re-id). However, they have an intrinsic problem of modeling the in-camera variability of images successfully, that is, pedestrian features extracted from the same camera tend to be clustered into the same class. This often results in a non-convergent model in the real world application of clustering based re-id models, leading to degenerated performance. In the present study, we propose an attention-driven two-stage clustering (ADTC) method to solve this problem. Specifically, our method consists of two strategies. Firstly, we use an unsupervised attention kernel to shift the learned features from the image background to the pedestrian foreground, which results in more informative clusters. Secondly, to aid the learning of the attention driven clustering model, we separate the clustering process into two stages. We first use kmeans to generate the centroids of clusters (stage 1) and then apply the k-reciprocal Jaccard distance (KRJD) metric to re-assign data points to each cluster (stage 2). By iteratively learning with the two strategies, the attentive regions are gradually shifted from the background to the foreground and the features become more discriminative. Using two benchmark datasets Market1501 and DukeMTMC, we demonstrate that our model outperforms other state-of-the-art unsupervised approaches for person re-id. © 2020, Springer Nature Switzerland AG.

关键词： Cameras

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iCatcher+: Robust and Automated Annotation of Infants’ and Young Children’s Gaze Behavior From Videos Collected in Laboratory, Field, and Online Studies

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Advances in Methods and Practices in Psychological science 2023年第2期6卷

作者： Erel, Yotam Shannon, Katherine Adams Chu, Junyi Scott, Kim Kline Struhl, Melissa Cao, Peng Tan, Xincheng Hart, Peter Raz, Gal Piccolo, Sabrina Mei, Catherine Potter, Christine Jaffe-Dax, Sagi Lew-Williams, Casey Tenenbaum, Joshua Fairchild, Katherine Bermano, Amit Liu, Shari The Blavatnik School of Computer Science Tel Aviv University Tel Aviv-Yafo Israel Department of Psychology Stanford University Palo Alto CA United States Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge MA United States Computer Science & Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA United States School of Engineering and Applied Sciences Harvard University Cambridge MA United States Department of Psychology The University of Texas at El Paso El Paso TX United States The School of Psychological Sciences Tel Aviv University Tel Aviv-Yafo Israel Department of Psychology Princeton University Princeton NJ United States The MIT Quest for Intelligence Massachusetts Institute of Technology Cambridge MA United States Department of Psychological and Brain Sciences Johns Hopkins University Baltimore MD United States

Technological advances in psychological research have enabled large-scale studies of human behavior and streamlined pipelines for automatic processing of data. However, studies of infants and children have not fully reaped these benefits because the behaviors of interest, such as gaze duration and direction, still have to be extracted from video through a laborious process of manual annotation, even when these data are collected online. Recent advances in computer vision raise the possibility of automated annotation of these video data. In this article, we built on a system for automatic gaze annotation in young children, iCatcher, by engineering improvements and then training and testing the system (referred to hereafter as iCatcher+) on three data sets with substantial video and participant variability (214 videos collected in U.S. lab and field sites, 143 videos collected in Senegal field sites, and 265 videos collected via webcams in homes;participant age range = 4 months–3.5 years). When trained on each of these data sets, iCatcher+ performed with near human-level accuracy on held-out videos on distinguishing “LEFT” versus “RIGHT” and “ON” versus “OFF” looking behavior across all data sets. This high performance was achieved at the level of individual frames, experimental trials, and study videos;held across participant demographics (e.g., age, race/ethnicity), participant behavior (e.g., movement, head position), and video characteristics (e.g., luminance);and generalized to a fourth, entirely held-out online data set. We close by discussing next steps required to fully automate the life cycle of online infant and child behavioral studies, representing a key step toward enabling robust and high-throughput developmental research. © The Author(s) 2023.

关键词： cognitive development deep learning eye tracking online data collection open source

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CREDIT: Coarse-to-fine Sequence generation for dialogue state tracking

arXiv

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arXiv 2020年

作者： Chen, Zhi Chen, Lu Xu, Zihan Zhao, Yanbin Zhu, Su Yu, Kai Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Eng. SpeechLab Department of Computer Science and Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai China

In dialogue systems, a dialogue state tracker aims to accurately find a compact representation of the current dialogue status, based on the entire dialogue history. While previous approaches often define dialogue states as a combination of separate triples (domain-slot-value), in this paper, we employ a structured state representation and cast dialogue state tracking as a sequence generation problem. Based on this new formulation, we propose a CoaRsE-to-fine DIalogue state Tracking (CREDIT) approach. Taking advantage of the structured state representation, which is a marked language sequence, we can further fine-tune the pre-trained model (by supervised learning) by optimizing natural language metrics with the policy gradient method. Like all generative state tracking methods, CREDIT does not rely on pre-defined dialogue ontology enumerating all possible slot values. Experiments demonstrate our tracker achieves encouraging joint goal accuracy for the five domains in MultiWOZ 2.0 and MultiWOZ 2.1 datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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Dual learning for dialogue state tracking

arXiv

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arXiv 2020年

作者： Chen, Zhi Chen, Lu Zhao, Yanbin Zhu, Su Yu, Kai Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Eng. SpeechLab Department of Computer Science and Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai China

In task-oriented multi-turn dialogue systems, dialogue state refers to a compact representation of the user goal in the context of dialogue history. Dialogue state tracking (DST) is to estimate the dialogue state at each turn. Due to the dependency on complicated dialogue history contexts, DST data annotation is more expensive than single-sentence language understanding, which makes the task more challenging. In this work, we formulate DST as a sequence generation problem and propose a novel dual-learning framework to make full use of unlabeled data. In the dual-learning framework, there are two agents: the primal tracker agent (utterance-to-state generator) and the dual utterance generator agent (state-to-utterance generator). Compared with traditional supervised learning framework, dual learning can iteratively update both agents through the reconstruction error and reward signal respectively without labeled data. Reward sparsity problem is hard to solve in previous DST methods. In this work, the reformulation of DST as a sequence generation model effectively alleviates this problem. We call this primal tracker agent dual-DST. Experimental results on MultiWOZ2.1 dataset show that the proposed dual-DST works very well, especially when labelled data is limited. It achieves comparable performance to the system where labeled data is fully used. Copyright © 2020, The Authors. All rights reserved.

关键词： Iterative methods

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