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arXiv 2023年

作者： Tanner, Jacob C. Mansour, Sina L. Coletta, Ludovico Gozzi, Alessandro Betzel, Richard F. Cognitive Science Program 2 School of Informatics Computing and Engineering Indiana University BloomingtonIN47405 United States National University of Singapore Singapore Fondazione Bruno Kessler Trento Italy Functional Neuroimaging Lab Istituto Italiano di Tecnologia Center for Neuroscience and Cognitive Systems Rovereto Italy Department of Psychological and Brain Sciences Indiana University BloomingtonIN47405 United States

Recent theoretical and experimental work in neuroscience has focused on the representational and dynamical character of neural manifolds –subspaces in neural activity space wherein many neurons coactivate. Importantly, neural populations studied under this "neural manifold hypothesis" are continuous and not cleanly divided into separate neural populations. This perspective clashes with the "modular hypothesis" of brain organization, wherein neural elements maintain an "all- or-nothing" affiliation with modules. In line with this modular hypothesis, recent research on recurrent neural networks suggests that multi-task networks become modular across training, such that different modules specialize for task-general dynamical motifs [1, 2]. If the modular hypothesis is true, then it would be important to use a dimensionality reduction technique that captures modular structure. Here, we investigate the features of such a method. We leverage RNNs as a model system to study the character of modular neural populations, using a community detection method from network science known as modularity maximization to partition neurons into distinct modules. These partitions allow us to ask the following question: do these modular boundaries matter to the system? We find evidence that they do. First, we find that these boundaries neatly divide the representational content and role of neurons. Next, we find that these boundaries can be directly inferred from features of the weight matrix in feed-forward neural networks, and are related to clustering of the Jacobian matrix in recurrent neural networks. We also find that the weights of input neurons to recurrent neurons partially inform their modular structure, an observation that we corroborated using structural and functional imaging data from mice and humans. Finally, we find that the dynamics of these RNNs reflected the boundaries between modules. Collectively, our results suggest that neural populations in RNNs sometimes form modu

关键词： Recurrent neural networks

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Quantifying Collective Identity Online from Self-defining Hashtags

Research Square

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Research Square 2021年

作者： Barron, Alexander T.J. Bollen, Johan Luddy School of Informatics Computing & Engineering Indiana University Bloomington United States Cognitive Science Program Indiana University Bloomington United States

Mass communication over social media can drive rapid changes in our sense of collective identity. Hashtags in particular have acted as powerful social coordinators[1], playing a key role in organizing social movements like the Gezi park protests, Occupy Wall Street, #metoo, and #blacklivesmatter[2, 3, 4]. Here we quantify collective identity from the use of hashtags as self-labels in over 85,000 actively-maintained Twitter user profiles spanning 2017-2019. Collective identities emerge from a graph model of individuals’ overlapping self-labels, producing a hierarchy of graph clusters. Each cluster is bound together and characterized semantically by specific hashtags key to its formation. We define and apply two information-theoretic measures to quantify the strength of identities in the hierarchy. First we measure collective identity coherence to determine how integrated any identity is from local to global scales. Second, we consider the conspicuousness of any identity given its vocabulary versus the global identity map. Our work reveals a rich landscape of online identity emerging from the hierarchical alignment of uncoordinated self-labeling actions. © 2021, CC BY.

关键词： User profile

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Role of simulation models in understanding the generation of behavior in C. elegans

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Current Opinion in Systems Biology 2019年 13卷 93-101页

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

There has been a proliferation of studies using simulation models to achieve a system-level understanding of behavior in Caenorhabditis elegans. Here I discuss the different aims of these modeling approaches and review examples of each in the context of locomotion and spatial navigation in the worm. I argue that as the focus shifts from understanding sensorimotor transformations to understanding neuromechanical instantiations for how behaviors are generated, simulation models will become a more integral part of the experiment–theory cycle. I discuss the insights that simulation modeling provides that complement information obtained from experimental discovery methods. © 2018 Elsevier Ltd

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The content and structure of dreams are coupled to affect

arXiv

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arXiv 2024年

作者： Leckie, Luke Bershad, Anya K. Heppler, Jes McClay, Mason Rappe, Sofiia Foster, Jacob G. School of Biological Sciences University of Bristol United Kingdom Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior UCLA Los AngelesCA United States Department of Philosophy University of California Berkeley BerkeleyCA United States Department of Psychology UCLA Los Angeles United States Department of Philosophy II Chair of Philosophy of Language and Cognition Ruhr University Bochum Germany Department of Informatics Luddy School of Informatics Computing and Engineering Indiana University United States Cognitive Science Program Indiana University Bloomington United States Santa Fe Institute United States

Dreams offer a unique window into the cognitive and affective dynamics of the sleeping and the waking mind. Recent quantitative linguistic approaches have shown promise in obtaining corpus-level measures of dream sentiment and topic occurrence. However, it is currently unclear how the affective content of individual dreams relates to their semantic content and structure. Here, we combine word embedding, topic modeling, and network analysis to investigate this relationship. By applying Discourse Atom Topic Modeling (DATM) to the DreamBank corpus of >18K dream reports, we represent the latent themes arising within dreams as a sparse dictionary of topics and identify the affective associations of those topics. We show that variation in dream affect (valence and arousal) is associated with changes in topical content. By representing each dream report as a network of topics, we demonstrate that the affective content of dreams is also coupled to semantic structure. Specifically, positively valenced dreams exhibit more coherent, structured, and linear narratives, whilst negatively valenced dreams have more narrative loops and dominant topics. Additionally, topic networks of high arousal dreams are structurally dominated by few high arousal topics and incoherent topical connections, whereas low arousal dreams contain more loops. These findings suggest that affective processes are associated with both the content and structure of dreams. Our approach showcases the potential of integrating natural language processing and network analysis with psychology to elucidate the interplay of affect, cognition and narrative in dreams. This methodology has broad applications for the study of narrated experience and psychiatric symptomatology. © 2024, CC BY-NC-ND.

关键词： Semantics

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Partial copying and the role of diversity in social learning performance

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Collective Intelligence 2022年第1期1卷 26339137221081849页

作者： Chelsea M Campbell Eduardo J Izquierdo Robert L Goldstone School of Informatics Computing and Engineering Indiana University USA Cognitive Science Program Indiana University USA Cognitive Science Program Indiana University USA Psychological and Brain Sciences Indiana University USA

One major way that people engage in adaptive problem solving is by imitating others’ solutions. Prominent simulation models have found imperfect imitation advantageous, but the interactions between copying amount and other prevalent aspects of social learning strategies have been underexplored. Here, we explore the consequences for a group when its members engage in strategies with different degrees of copying, solving search problems of varying complexity, in different network topologies that affect the solutions visible to each member. Using a computational model of collective problem solving, we demonstrate that the advantage of partial copying is robust across these conditions, arising from its ability to maintain diversity. Partial copying delays convergence generally but especially in globally connected networks, which are typically associated with diversity loss, allowing more exploration of a problem space. We show that a moderate amount of diversity maintenance is optimal and strategies can be adjusted to find that sweet spot.

关键词： Social learning individual learning copying explore/exploit imitation network topology

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Automating the Practice of science – Opportunities, Challenges, and Implications

arXiv

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arXiv 2024年

作者： Musslick, Sebastian Bartlett, Laura K. Chandramouli, Suyog H. Dubova, Marina Gobet, Fernand Griffiths, Thomas L. Hullman, Jessica King, Ross D. Nathan Kutz, J. Lucas, Christopher G. Mahesh, Suhas Pestilli, Franco Sloman, Sabina J. Holmes, William R. Institute of Cognitive Science Osnabrück University Osnabrück49090 Germany Department of Cognitive Linguistic & Psychological Sciences Brown University ProvidenceRI02912 United States Centre for Philosophy of Natural and Social Science London School of Economics Lakatos Building Houghton Street LondonWC2A 2AE United Kingdom AaltoFI-00076 Finland Department of Computing Science University of Alberta 8900 114 St NW EdmontonABT6G 2S4 Canada Cognitive Science Program Indiana University 1101 E 10th St BloomingtonIN47405 United States School of Psychology University of Roehampton LondonSW15 4JD United Kingdom Departments of Psychology and Computer Science Princeton University PrincetonNJ United States Department of Computer Science Northwestern University IL United States Department of Chemical Engineering and Biotechnology University of Cambridge CambridgeCB3 0AS United Kingdom Department of Computer Science and Engineering Chalmers University of Technology Gothenburg412 96 Sweden Department of Applied Mathematics and Electrical and Computer Engineering University of Washington Seattle98195 United States School of Informatics University of Edinburgh 10 Crichton St. EH8 9AB United Kingdom Department of Materials Science and Engineering University of Toronto Canada Department of Psychology Department of Neuroscience The University of Texas AustinTX United States Department of Computer Science University of Manchester M13 9PL United Kingdom

Automation transformed various aspects of our human civilization, revolutionizing industries and streamlining processes. In the domain of scientific inquiry, automated approaches emerged as powerful tools, holding promise for accelerating discovery, enhancing reproducibility, and overcoming the traditional impediments to scientific progress. This article evaluates the scope of automation within scientific practice and assesses recent approaches. Furthermore, it discusses different perspectives to the following questions: Where do the greatest opportunities lie for automation in scientific practice?;What are the current bottlenecks of automating scientific practice?;and What are significant ethical and practical consequences of automating scientific practice? By discussing the motivations behind automated science, analyzing the hurdles encountered, and examining its implications, this article invites researchers, policymakers, and stakeholders to navigate the rapidly evolving frontier of automated scientific practice. © 2024, CC BY.

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Input matters in the modeling of early phonetic learning 42

Input matters in the modeling of early phonetic learning

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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： Li, Ruolan Schatz, Thomas Matusevych, Yevgen Goldwater, Sharon Feldman, Naomi H. Program in Neuroscience and Cognitive Science Department of Linguistics UMIACS University of Maryland College ParkMD20742 United States Department of Linguistics UMIACS University of Maryland College ParkMD20742 United States School of Informatics University of Edinburgh 10 Crichton St EdinburghEH8 9AB United Kingdom

In acquiring language, differences in input can greatly affect learning outcomes, but which aspects of language learning are most sensitive to input variations, and which are robust, remains debated. A recent modeling study successfully reproduced a phenomenon empirically observed in early phonetic learning-learning about the sounds of the native language in the first year of life-despite using input that differed in quantity and speaker composition from what a typical infant would hear. In this paper, we carry out a direct test of that model's robustness to input variations. We find that, despite what the original result suggested, the learning outcomes are sensitive to properties of the input and that more plausible input leads to a better fit with empirical observations. This has implications for understanding early phonetic learning in infants and underscores the importance of using realistic input in models of language acquisition. © 2020 The Author(s)

关键词： Modeling languages

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Seeds of Stereotypes: A Large-Scale Textual Analysis of Race and Gender Associations with Diseases in Online Sources

arXiv

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arXiv 2024年

作者： Hansen, Lasse Hyldig Andersen, Nikolaj Gallifant, Jack McCoy, Liam G. Stone, James K. Izath, Nura Aguirre-Jerez, Marcela Bitterman, Danielle S. Gichoya, Judy Celi, Leo Anthony Cognitive Science Aarhus University Jens Chr. Skou 2 Aarhus8000 Denmark Laboratory for Computational Physiology Massachusetts Institute of Technology CambridgeMA United States Department of Critical Care Guy’s & St Thomas’ NHS Trust London United Kingdom Division of Neurology University of Alberta AB Canada University of Manitoba Max Rady College of Medicine WinnipegMB Canada Faculty of Computing Mbarara University of Science and Technology Mbarara Uganda Digital Health Department Fundacion Arturo Lopez Perez Santiago Chile Program Mass General Brigham Harvard Medical School BostonMA United States Department of Radiation Oncology Brigham and Women’s Hospital Dana-Farber Cancer Institute BostonMA United States Computational Health Informatics Program Boston Children’s Hospital Harvard Medical School BostonMA United States Department of Radiology Emory University School of Medicine GA United States Division of Pulmonary Critical Care and Sleep Medicine Beth Israel Deaconess Medical Center BostonMA United States Department of Biostatistics Harvard T.H. Chan School of Public Health BostonMA United States

Background Advancements in Large Language Models (LLMs) hold transformative potential in healthcare, however, recent work has raised concern about the tendency of these models to produce outputs that display racial or gender biases. Although training data is a likely source of such biases, exploration of disease and demographic associations in text data at scale has been limited. Methods We conducted a large-scale textual analysis using a dataset comprising diverse web sources, including Arxiv, Wikipedia, and Common Crawl. The study analyzed the context in which various diseases are discussed alongside markers of race and gender. Given that LLMs are pre-trained on similar datasets, this approach allowed us to examine the potential biases that LLMs may learn and internalize. We compared these findings with actual demographic disease prevalence as well as GPT-4 outputs in order to evaluate the extent of bias representation. Results Our findings indicate that demographic terms are disproportionately associated with specific disease concepts in online texts. gender terms are prominently associated with disease concepts, while racial terms are much less frequently associated. We find widespread disparities in the associations of specific racial and gender terms with the 18 diseases analyzed. Most prominently, we see an overall significant overrepresentation of Black race mentions in comparison to population proportions. Conclusions Our results highlight the need for critical examination and transparent reporting of biases in LLM pretraining datasets. Our study suggests the need to develop mitigation strategies to counteract the influence of biased training data in LLMs, particularly in sensitive domains such as healthcare. © 2024, CC BY.

关键词： Large datasets

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Consciousness in Artificial Intelligence: Insights from the science of Consciousness

arXiv

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arXiv 2023年

作者： Butlin, Patrick Long, Robert Elmoznino, Eric Bengio, Yoshua Birch, Jonathan Constant, Axel Deane, George Fleming, Stephen M. Frith, Chris Ji, Xu Kanai, Ryota Klein, Colin Lindsay, Grace Michel, Matthias Mudrik, Liad Peters, Megan A.K. Schwitzgebel, Eric Simon, Jonathan VanRullen, Rufin Future of Humanity Institute University of Oxford United Kingdom Center for AI Safety United States University of Montreal MILA - Quebec AI Institute Canada Centre for Philosophy of Natural and Social Science London School of Economics and Political Science United Kingdom School of Engineering and Informatics The University of Sussex Centre de Recherche en Éthique University of Montreal United Kingdom Department of Philosophy University of Montreal Canada Department of Experimental Psychology Wellcome Centre for Human Neuroimaging University College London United Kingdom Wellcome Centre for Human Neuroimaging University College London Institute of Philosophy University of London United Kingdom Araya Inc. School of Philosophy The Australian National University Australia Department of Psychology Center for Data Science New York University United States Center for Mind Brain and Consciousness New York University United States School of Psychological Sciences and Sagol School of Neuroscience Tel-Aviv University CIFAR Program in Brain Mind and Consciousness Israel Department of Cognitive Sciences University of California Irvine and CIFAR Program in Brain Mind and Consciousness United States Department of Philosophy University of California Riverside United States Centre de Recherche Cerveau et Cognition CNRS Université de Toulouse France

Whether current or near-term AI systems could be conscious is a topic of scientific interest and increasing public concern. This report argues for, and exemplifies, a rigorous and empirically grounded approach to AI consciousness: assessing existing AI systems in detail, in light of our best-supported neuroscientific theories of consciousness. We survey several prominent scientific theories of consciousness, including recurrent processing theory, global workspace theory, higher-order theories, predictive processing, and attention schema theory. From these theories we derive"indicator properties" of consciousness, elucidated in computational terms that allow us to assess AI systems for these properties. We use these indicator properties to assess several recent AI systems, and we discuss how future systems might implement them. Our analysis suggests that no current AI systems are conscious, but also suggests that there are no obvious technical barriers to building AI systems which satisfy these indicators.1 © 2023, CC BY-NC-SA.

关键词： Neurophysiology

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Efficient and scalable neural residual waveform coding with collaborative quantization

arXiv

引用

arXiv 2020年

作者： Zhen, Kai Lee, Mi Suk Sung, Jongmo Beack, Seungkwon Kim, Minje Indiana University Luddy School of Informatics Computing and Engineering BloomingtonIN United States Indiana University Cognitive Science Program BloomingtonIN United States Electronics and Telecommunications Research Institute Daejeon Korea Republic of

Scalability and efficiency are desired in neural speech codecs, which supports a wide range of bitrates for applications on various devices. We propose a collaborative quantization (CQ) scheme to jointly learn the codebook of LPC coefficients and the corresponding residuals. CQ does not simply shoehorn LPC to a neural network, but bridges the computational capacity of advanced neural network models and traditional, yet efficient and domain-specific digital signal processing methods in an integrated manner. We demonstrate that CQ achieves much higher quality than its predecessor at 9 kbps with even lower model complexity. We also show that CQ can scale up to 24 kbps where it outperforms AMR-WB and Opus. As a neural waveform codec, CQ models are with less than 1 million parameters, significantly less than many other generative models. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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