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Exploring lightweight interventions at posting time to reduce the sharing of misinformation on social media

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arXiv 2021年

作者： Jahanbakhsh, Farnaz Zhang, Amy X. Berinsky, Adam J. Pennycook, Gordon Rand, David G. Karger, David R. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States Allen School of Computer Science & Engineering University of Washington United States Political Science Massachusetts Institute of Technology United States Hill/Levene Schools of Business University of Regina Canada Sloan School of Management/Brain and Cognitive Sciences Massachusetts Institute of Technology United States

When users on social media share content without considering its veracity, they may unwittingly be spreading misinformation. In this work, we investigate the design of lightweight interventions that nudge users to assess the accuracy of information as they share it. Such assessment may deter users from posting misinformation in the first place, and their assessments may also provide useful guidance to friends aiming to assess those posts themselves. In support of lightweight assessment, we first develop a taxonomy of the reasons why people believe a news claim is or is not true;this taxonomy yields a checklist that can be used at posting time. We conduct evaluations to demonstrate that the checklist is an accurate and comprehensive encapsulation of people’s free-response rationales. In a second experiment, we study the effects of three behavioral nudges—1) checkboxes indicating whether headings are accurate, 2) tagging reasons (from our taxonomy) that a post is accurate via a checklist and 3) providing free-text rationales for why a headline is or is not accurate—on people’s intention of sharing the headline on social media. From an experiment with 1668 participants, we find that both providing accuracy assessment and rationale reduce the sharing of false content. They also reduce the sharing of true content, but to a lesser degree that yields an overall decrease in the fraction of shared content that is false. Our findings have implications for designing social media and news sharing platforms that draw from richer signals of content credibility contributed by users. In addition, our validated taxonomy can be used by platforms and researchers as a way to gather rationales in an easier fashion than free-response. © 2021, CC BY.

关键词： Social networking (online)

Automated Detection of Epileptic Spikes and Seizures Incorporating a Novel Spatial Clustering Prior

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Automated Detection of Epileptic Spikes and Seizures Incorpo...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Hanyang Dong Shurong Sheng Xiongfei Wang Jia-Hong Gao Yi Sun Wanli Yang Kuntao Xiao Pengfei Teng Guoming Luan Zhao Lv School of Artificial Intelligence Anhui University Hefei China Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Department of Neurosurgery Beijing Key Laboratory of Epilepsy Sanbo Brain Hospital Capital Medical University Beijing China McGovern Institute for Brain Research Peking University Beijing China Center for MRI Research Academy for Advanced Interdisciplinary Studies Peking University Beijing China Beijing City Key Lab for Medical Physics and Engineering Institute of Heavy Ion Physics School of Physics Peking University Beijing China Anhui Province Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

A Magnetoencephalography (MEG) time-series recording consists of multi-channel signals collected by superconducting sensors, with each signal’s intensity reflecting magnetic field changes over time at the sensor location. Automating epileptic MEG spike detection significantly reduces manual assessment time and effort, yielding substantial clinical benefits. Existing research addresses MEG spike detection by encoding neural network inputs with signals from all channel within a time segment, followed by classification. However, these methods overlook simultaneous spiking occurred from nearby sensors. We introduce a simple yet effective paradigm that first clusters MEG channels based on their sensor’s spatial position. Next, a novel convolutional input module is designed to integrate the spatial clustering and temporal changes of the signals. This module is fed into a custom MEEG-ResNet3D developed by the authors, which learns to extract relevant features and classify the input as a spike clip or not. Our method achieves an F1 score of 94.73% on a large real-world MEG dataset Sanbo-CMR collected from two centers, outperforming state-of-the-art approaches by 1.85%. Moreover, it demonstrates efficacy and stability in the Electroencephalographic (EEG) seizure detection task, yielding an improved weighted F1 score of 1.4% compared to current state-of-the-art techniques evaluated on TUSZ, whch is the largest EEG seizure dataset.

关键词： Convolution Magnetic sensors Neural networks Magnetoencephalography Manuals Feature extraction Electroencephalography Encoding Recording Magnetic fields

Meta-control of social learning strategies

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arXiv 2021年

作者： Yaman, Anil Bredeche, Nicolas Çaylak, Onur Leibo, Joel Z. Lee, Sang Wan Computer Science Department Vrije Universiteit Amsterdam Amsterdam Netherlands Institut des Systèmes Intelligents et de Robotique Sorbonne Université CNRS Paris France Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands DeepMind London United Kingdom Department of Bio and Brain Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Center for Neuroscience-Inspired AI Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Program of Brain and Cognitive Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of KI for Health Science and Technology Korea Advanced Institute of Science and Technology Daejeon Korea Republic of KI for Artificial Intelligence Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Computer Science Department Vrije Universiteit Amsterdam Amsterdam Netherlands

Social learning, copying other's behavior without actual experience, offers a cost-effective means of knowledge acquisition. However, it raises the fundamental question of which individuals have reliable information: successful individuals versus the majority. The former and the latter are known respectively as success-based and conformist social learning strategies. We show here that while the success-based strategy fully exploits the benign environment of low uncertainly, it fails in uncertain environments. On the other hand, the conformist strategy can effectively mitigate this adverse effect. Based on these findings, we hypothesized that meta-control of individual and social learning strategies provides effective and sample-efficient learning in volatile and uncertain environments. Simulations on a set of environments with various levels of volatility and uncertainty confirmed our hypothesis. The results imply that meta-control of social learning affords agents the leverage to resolve environmental uncertainty with minimal exploration cost, by exploiting others' learning as an external knowledge base. Copyright © 2021, The Authors. All rights reserved.

关键词： Learning systems

High-resolution myelin-water fraction and quantitative relaxation mapping using 3D ViSTa-MR fingerprinting

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arXiv 2023年

作者： Liao, Congyu Cao, Xiaozhi Iyer, Siddharth Srinivasan Schauman, Sophie Zhou, Zihan Yan, Xiaoqian Chen, Quan Li, Zhitao Wang, Nan Gong, Ting Wu, Zhe He, Hongjian Zhong, Jianhui Yang, Yang Kerr, Adam Grill-Spector, Kalanit Setsompop, Kawin Department of Radiology Stanford University StanfordCA United States Department of Electrical Engineering Stanford University StanfordCA United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Center for Brain Imaging Science and Technology College of Biomedical Engineering & Instrument Science Zhejiang University Zhejiang Hangzhou China Department of Psychology Stanford University StanfordCA United States Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School CharlestownMA United States Techna Institute University Health Network TorontoON Canada School of Physics Zhejiang University Zhejiang Hangzhou China Department of Imaging Sciences University of Rochester RochesterNY United States Department of Radiology and Biomedical Imaging University of California San FranciscoCA United States Stanford Center for Cognitive and Neurobiological Imaging Stanford University StanfordCA United States

Purpose: This study aims to develop a high-resolution whole-brain multi-parametric quantitative MRI approach for simultaneous mapping of myelin-water fraction (MWF), T1, T2, and proton-density (PD), all within a clinically feasible scan time. Methods: We developed 3D ViSTa-MRF, which combined Visualization of Short Transverse relaxation time component (ViSTa) technique with MR Fingerprinting (MRF), to achieve high-fidelity whole-brain MWF and T1/T2/PD mapping on a clinical 3T scanner. To achieve fast acquisition and memory-efficient reconstruction, the ViSTa-MRF sequence leverages an optimized 3D tiny-golden-angle-shuffling spiral-projection acquisition and joint spatial-temporal subspace reconstruction with optimized preconditioning algorithm. With the proposed ViSTa-MRF approach, high-fidelity direct MWF mapping was achieved without a need for multi-compartment fitting that could introduce bias and/or noise from additional assumptions or priors. Results: The in-vivo results demonstrate the effectiveness of the proposed acquisition and reconstruction framework to provide fast multi-parametric mapping with high SNR and good quality. The in-vivo results of 1mm- and 0.66mm-iso datasets indicate that the MWF values measured by the proposed method are consistent with standard ViSTa results that are 30x slower with lower SNR. Furthermore, we applied the proposed method to enable 5-minute whole-brain 1mm-iso assessment of MWF and T1/T2/PD mappings for infant brain development and for post-mortem brain samples. Conclusions: In this work, we have developed a 3D ViSTa-MRF technique that enables the acquisition of whole-brain MWF, quantitative T1, T2, and PD maps at 1mm and 0.66mm isotropic resolution in 5 and 15 minutes, respectively. This advancement allows for quantitative investigations of myelination changes in the brain. © 2023, CC BY.

关键词： Mapping

Predicting Dominant Beat Frequency from brain Responses While Listening to Music

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Predicting Dominant Beat Frequency from Brain Responses Whil...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Pankaj Pandey Nashra Ahmad Krishna Prasad Miyapuram Derek Lomas Computer Science and Engineering Indian Institute of Technology Gandhinagar Gujarat India Centre for Cognitive and Brain Sciences Indian Institute of Technology Gandhinagar Gujarat India Industrial Design Engineering for Positive AI Delft University of Technology Delft Netherland

ISBN: (纸本)9781665429825

Modern neuroscience has shown that the brain is profoundly rhythmic and that frequencies of neural rhythms are responsive to frequencies of musical rhythms. We collected Electroencephalography (EEG) response on 12 naturalistic music stimuli (songs), from 20 participants. We retrieved the tempo and its sub-harmonics from our stimuli (songs), and further used this information to predict the beats in the brain response using Machine Learning techniques. We observed a hierarchy of beats in each of the songs, with a specific beat frequency to be dominant (i.e. higher in magnitude) than others. This led us to form three groups of songs and their brain responses, with each of the groups indicating the frequency of a beat that dominated in the hierarchy of beat structure of that song. We used small segments of 1, 3 and 5 seconds of brain responses, rather than the entire song duration. We further created two sets for classification of the three groups of brain responses and utilized two spatial filtering techniques: Mean across electrodes (ME) and first principal component (PC1), and a Dense method using data from all electrodes. This was followed by feature extraction using band power. We developed univariate and multivariate models for classification to demonstrate the significance of each frequency band which represent beat frequencies. The dense method outperformed ME and PC1. Features related to eighth note generated maximum discrimination between classes. We also observed a positive correlation between window length and rate of correct prediction. Accuracy from one second to five seconds window improved significantly in both the sets. We achieved maximum accuracy of 70% and 56% accuracies for binary and ternary classification respectively, which is 20% above chance-level accuracy. Random Forest and kNN performed better than SVM. This work contributes to the growing body of knowledge to understand the underlying neural mechanism of rhythm processing in the brain.

关键词： Electrodes Support vector machines Neuroscience Correlation Filtering Conferences Rhythm

DOMINO++: Domain-aware Loss Regularization for Deep Learning Generalizability

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arXiv 2023年

作者： Stolte, Skylar E. Volle, Kyle Indahlastari, Aprinda Albizu, Alejandro Woods, Adam J. Brink, Kevin Hale, Matthew Fang, Ruogu United States Torch Technologies LLC Florida United States Center for Cognitive Aging and Memory McKnight Brain Institute UF United States Department of Clinical and Health Psychology College of Public Health and Health Professions UF United States Department of Neuroscience College of Medicine UF United States United States Air Force Research Laboratory Eglin Air Force BaseFL United States Department of Electrical and Computer Engineering Herbert Wertheim College of Engineering UF United States Department of Computer Information and Science and Engineering Herbert Wertheim College of Engineering UF United States

Out-of-distribution (OOD) generalization poses a serious challenge for modern deep learning (DL). OOD data consists of test data that is significantly different from the model's training data. DL models that perform well on in-domain test data could struggle on OOD data. Overcoming this discrepancy is essential to the reliable deployment of DL. Proper model calibration decreases the number of spurious connections that are made between model features and class outputs. Hence, calibrated DL can improve OOD generalization by only learning features that are truly indicative of the respective classes. Previous work proposed domain-aware model calibration (DOMINO) to improve DL calibration, but it lacks designs for model generalizability to OOD data. In this work, we propose DOMINO++, a dual-guidance and dynamic domain-aware loss regularization focused on OOD generalizability. DOMINO++ integrates expert-guided and data-guided knowledge in its regularization. Unlike DOMINO which imposed a fixed scaling and regularization rate, DOMINO++ designs a dynamic scaling factor and an adaptive regularization rate. Comprehensive evaluations compare DOMINO++ with DOMINO and the baseline model for head tissue segmentation from magnetic resonance images (MRIs) on OOD data. The OOD data consists of synthetic noisy and rotated datasets, as well as real data using a different MRI scanner from a separate site. DOMINO++'s superior performance demonstrates its potential to improve the trustworthy deployment of DL on real clinical data. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance

Detecting brain State Changes via Manifold Mean Shifting

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Detecting Brain State Changes via Manifold Mean Shifting

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhuobin Huang Tingting Dan Yi Lin Jiazhou Chen Hongmin Cai Guorong Wu School of Computer Science and Engineering South China University of Technology Guangzhou China Brain and Affective Cognitive Research Center Pazhou Lab Guangzhou China University of North Carolina at Chapel Hill Chapel Hill NC USA

ISBN: (纸本)9781665429825

The topology of human functional networks is assumed to oscillate during brain states changes. The functional neuroimage is employed to offer a non-invasive window to understand cognition and behaviors by characterizing the functional connections between spatially distinct brain regions. Consequently, identifying the transitions of functional connectivities is the critical step to understanding the mechanism of cognition that might be underlined with neurological disorders. However, little attention has been paid to studying the geometry of the entire functional brain network. To tackle this issue, this paper models the cognition changes on functional brain networks as a set of landmarks residing on a Riemannian manifold. Accordingly, we propose a Riemannian manifold mean shift method to detect cognition changes by identifying the representative function networks of the distribution of functional networks. The manifold mean shift (MMS) method is applied on both simulated data and real functional neuroimaging data, downloaded from Human Connectome Project (HCP). Experimental results demonstrated the MMS achieved highly accurate and consistent cognition change, by comparing three state-of-the-art methods.

关键词： Manifolds Geometry Neurological diseases Network topology Conferences Network analyzers Functional neuroimaging

Author Correction: Inhibitory specificity from a connectomic census of mouse visual cortex

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Nature 2025年 2025 May 19页

作者： Casey M Schneider-Mizell Agnes L Bodor Derrick Brittain JoAnn Buchanan Daniel J Bumbarger Leila Elabbady Clare Gamlin Daniel Kapner Sam Kinn Gayathri Mahalingam Sharmishtaa Seshamani Shelby Suckow Marc Takeno Russel Torres Wenjing Yin Sven Dorkenwald J Alexander Bae Manuel A Castro Akhilesh Halageri Zhen Jia Chris Jordan Nico Kemnitz Kisuk Lee Kai Li Ran Lu Thomas Macrina Eric Mitchell Shanka Subhra Mondal Shang Mu Barak Nehoran Sergiy Popovych William Silversmith Nicholas L Turner William Wong Jingpeng Wu Jacob Reimer Andreas S Tolias H Sebastian Seung R Clay Reid Forrest Collman Nuno Maçarico da Costa Allen Institute for Brain Science Seattle WA USA. caseys@***. Allen Institute for Brain Science Seattle WA USA. Princeton Neuroscience Institute Princeton University Princeton NJ USA. Computer Science Department Princeton University Princeton NJ USA. Electrical and Computer Engineering Department Princeton University Princeton NJ USA. Department of Electrical and Computer Engineering Rice University Houston TX USA. Department of Neuroscience Baylor College of Medicine Houston TX USA. Center for Neuroscience and Artificial Intelligence Baylor College of Medicine Houston TX USA. Brain & Cognitive Sciences Department Massachusetts Institute of Technology Cambridge MA USA. Allen Institute for Brain Science Seattle WA USA. nunod@***.

Computational approaches to analyzing and generating comics 42

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Computational approaches to analyzing and generating comics

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

作者： Upadhyayula, Aditya Martens, Chris Cardona-Rivera, Rogelio E. Hendrickson, Andrew T. Cohn, Neil Department of Psychological and Brain Sciences Johns Hopkins University United States Department of Computer Science North Carolina State University United States School of Computing The Entertainment Arts and Engineering Program University of Utah United States Department of Cognitive Science and Artificial Intelligence Tilburg University Netherlands Department of Cognition and Communication Tilburg University Netherlands