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On Logical Inference over Brains, Behaviour, and Artificial neural Networks

computational Brain and Behavior 2023年第2期6卷 213-227页

作者： Guest, Olivia Martin, Andrea E. Donders Institute for Brain Cognition and Behaviour Radboud University Nijmegen Netherlands Language and Computation in Neural Systems Group Donders Centre for Cognitive Neuroimaging Radboud University Nijmegen Netherlands Language and Computation in Neural Systems Group Max Planck Institute for Psycholinguistics Nijmegen Netherlands

In the cognitive, computational, and neuro-sciences, practitioners often reason about what computational models represent or learn, as well as what algorithm is instantiated. The putative goal of such reasoning is to generalize claims about the model in question, to claims about the mind and brain, and the neurocognitive capacities of those systems. Such inference is often based on a model’s performance on a task, and whether that performance approximates human behavior or brain activity. Here we demonstrate how such argumentation problematizes the relationship between models and their targets;we place emphasis on artificial neural networks (ANNs), though any theory-brain relationship that falls into the same schema of reasoning is at risk. In this paper, we model inferences from ANNs to brains and back within a formal framework — metatheoretical calculus — in order to initiate a dialogue on both how models are broadly understood and used, and on how to best formally characterize them and their functions. To these ends, we express claims from the published record about models’ successes and failures in first-order logic. Our proposed formalization describes the decision-making processes enacted by scientists to adjudicate over theories. We demonstrate that formalizing the argumentation in the literature can uncover potential deep issues about how theory is related to phenomena. We discuss what this means broadly for research in cognitive science, neuroscience, and psychology;what it means for models when they lose the ability to mediate between theory and data in a meaningful way;and what this means for the metatheoretical calculus our fields deploy when performing high-level scientific inference. © 2023, The Author(s).

关键词： Cognitive computational neuroscience Cognitive science Logic Metascience Metatheory Philosophy of science Scientific inference

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Rethinking Tokenization: Crafting Better Tokenizers for Large language Models

arXiv

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arXiv 2024年

作者： Yang, Jinbiao Language and Computation in Neural Systems Group Max Planck Institute for Psycholinguistics Netherlands

Tokenization significantly influences language models(LMs)' performance. This paper traces the evolution of tokenizers from word-level to subword-level, analyzing how they balance tokens and types to enhance model adaptability while controlling complexity. Despite subword tokenizers like Byte Pair Encoding (BPE) overcoming many word tokenizer limitations, they encounter difficulties in handling non-Latin languages and depend heavily on extensive training data and computational resources to grasp the nuances of multiword expressions (MWEs). This article argues that tokenizers, more than mere technical tools, should drawing inspiration from the cognitive science about human language processing. This study then introduces the "Principle of Least Effort" from cognitive science, that humans naturally seek to reduce cognitive effort, and discusses the benefits of this principle for tokenizer development. Based on this principle, the paper proposes that the Less-is-Better (LiB) model could be a new approach for LLM tokenizer. The LiB model can autonomously learn an integrated vocabulary consisting of subwords, words, and MWEs, which effectively reduces both the numbers of tokens and types. Comparative evaluations show that the LiB tokenizer outperforms existing word and BPE tokenizers, presenting an innovative method for tokenizer development, and hinting at the possibility of future cognitive science-based tokenizers being more efficient. © 2024, CC BY.

关键词： computational linguistics

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From Empirical Problem-Solving to Theoretical Problem-Finding Perspectives on the Cognitive Sciences

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computational Brain and Behavior 2024年第4期7卷 572-587页

作者： Adolfi, Federico van de Braak, Laura Woensdregt, Marieke Ernst Strüngmann Institute for Neuroscience in Cooperation with Max-Planck Society Frankfurt Germany School of Psychological Science University of Bristol Bristol United Kingdom Donders Institute for Brain Cognition and Behaviour Radboud University Nijmegen Netherlands Language and Computation in Neural Systems Max Planck Institute for Psycholinguistics Nijmegen Netherlands

Meta-theoretical perspectives on the research problems and activities of (cognitive) scientists often emphasize empirical problems and problem-solving as the main aspects that account for scientific progress. While certainly useful to shed light on issues of theory-observation relationships, these conceptual analyses typically begin when empirical problems are already there for researchers to solve. As a result, the role of theoretical problems and problem-finding remain comparatively obscure. How do the scientific problems of Cognitive Science arise, and what do they comprise, empirically and theoretically? Here, we attempt to understand the research activities that lead to adequate explanations through a broader conception of the problems researchers must attend to and how they come about. To this end, we bring theoretical problems and problem-finding out of obscurity to paint a more integrative picture of how these complement empirical problems and problem-solving to advance cognitive science. © The Author(s) 2024.

关键词： Cognitive science Explanation Meta-theory Problem-finding Theoretical constraints Theory development

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language universals rely on social cognition: computational models of the use of this and that to redirect the receiver's attention 44

Language universals rely on social cognition: Computational ...

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44th Annual Meeting of the Cognitive Science Society: Cognitive Diversity, CogSci 2022

作者： Woensdregt, Marieke Jara-Ettinger, Julian Rubio-Fernandez, Paula Language and Computation in Neural Systems Max Planck Institute for Psycholinguistics Netherlands Department of Psychology Yale University United States Department of Philosophy Classics History of Art & Ideas University of Oslo Norway

Demonstratives-simple referential devices like this and that-are linguistic universals, but their meaning varies cross-linguistically. In languages like English and Italian, demonstratives are thought to encode the referent's distance from the producer (e.g., that one means "the one far away from me"), while in others, like Portuguese and Spanish, they encode relative distance from both producer and receiver (e.g., aquel means "the one far away from both of us"). Here we propose that demonstratives are also sensitive to the receiver's focus of attention, hence requiring a deeper form of social cognition than previously thought. We provide initial empirical and computational evidence for this idea, suggesting that producers use demonstratives to redirect the receiver's attention towards the intended referent, rather than only to indicate its physical distance. © 2022 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY)

关键词： Modeling languages

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Electrophysiological signatures of second language multimodal comprehension 43

Electrophysiological signatures of second language multimoda...

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43rd Annual Meeting of the Cognitive Science Society: Comparative Cognition: Animal Minds, CogSci 2021

作者： Zhang, Ye Ding, Rong Frassinelli, Diego Tuomainen, Jyrki Klavinskis-Whiting, Sebastian Vigliocco, Gabriella Department of Experimental Psychology University College London United Kingdom Language and Computation in Neural Systems Group Max Planck Institute for Psycholinguistics Nijmegen Netherlands Department of Linguistics University of Konstanz Konstanz Germany Department of Speech Hearing and Phonetic Science University College London United Kingdom Experimental Psychology Oxford University United Kingdom

language is multimodal: non-linguistic cues, such as prosody, gestures and mouth movements, are always present in face-to-face communication and interact to support processing. In this paper, we ask whether and how multimodal cues affect L2 processing by recording EEG for highly proficient bilinguals when watching naturalistic materials. For each word, we quantified surprisal and the informativeness of prosody, gestures, and mouth movements. We found that each cue modulates the N400: prosodic accentuation, meaningful gestures, and informative mouth movements all reduce N400. Further, effects of meaningful gestures but not mouth informativeness are enhanced by prosodic accentuation, whereas effects of mouth are enhanced by meaningful gestures but reduced by beat gestures. Compared with L1, L2 participants benefit less from cues and their interactions, except for meaningful gestures and mouth movements. Thus, in real-world language comprehension, L2 comprehenders use multimodal cues just as L1 speakers albeit to a lesser extent. © Cognitive Science Society: Comparative Cognition: Animal Minds, CogSci *** rights reserved.

关键词： Electrophysiology

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Lessons for Theory from Scientific Domains Where Evidence is Sparse or Indirect

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computational Brain and Behavior 2024年第4期7卷 588-607页

作者： Woensdregt, Marieke Fusaroli, Riccardo Rich, Patricia Modrák, Martin Kolokolova, Antonina Wright, Cory Warlaumont, Anne S. Department of Cognitive Science and Artificial Intelligence Radboud University Nijmegen Netherlands Donders Institute for Brain Cognition and Behaviour Radboud University Nijmegen Netherlands Language and Computation in Neural Systems Max Planck Institute for Psycholinguistics Nijmegen Netherlands Department of Linguistics Cognitive Science and Semiotics School of Communication and Culture Aarhus University Aarhus Denmark Interacting Minds Center School of Culture and Society Aarhus University Aarhus Denmark Linguistic Data Consortium University of Pennsylvania Philadelphia PA United States Department of Philosophy University of Bayreuth Bayreuth Germany Institute of Microbiology of the Czech Academy of Sciences Prague Czech Republic Second Faculty of Medicine Charles University Prague Czech Republic Department of Computer Science Memorial University of Newfoundland St. John’s NL Canada Department of Philosophy California State University Long Beach Long Beach CA United States Department of Communication University of California Los Angeles Los Angeles CA United States

In many scientific fields, sparseness and indirectness of empirical evidence pose fundamental challenges to theory development. Theories of the evolution of human cognition provide a guiding example, where the targets of study are evolutionary processes that occurred in the ancestors of present-day humans. In many cases, the evidence is both very sparse and very indirect (e.g., archaeological findings regarding anatomical changes that might be related to the evolution of language capabilities);in other cases, the evidence is less sparse but still very indirect (e.g., data on cultural transmission in groups of contemporary humans and non-human primates). From examples of theoretical and empirical work in this domain, we distill five virtuous practices that scientists could aim to satisfy when evidence is sparse or indirect: (i) making assumptions explicit, (ii) making alternative theories explicit, (iii) pursuing computational and formal modelling, (iv) seeking external consistency with theories of related phenomena, and (v) triangulating across different forms and sources of evidence. Thus, rather than inhibiting theory development, sparseness or indirectness of evidence can catalyze it. To the extent that there are continua of sparseness and indirectness that vary across domains and that the principles identified here always apply to some degree, the solutions and advantages proposed here may generalise to other scientific domains. © The Author(s) 2024.

关键词： Cognitive science Evidence Explanation Theoretical virtues Theory development Underdetermination

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A theory of relation learning and cross-domain generalization

arXiv

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arXiv 2019年

作者： Doumas, Leonidas A.A. Puebla, Guillermo Martin, Andrea E. Hummel, John E. School of Philosophy Psychology and Language Sciences The University of Edinburgh United Kingdom Language and Computation in Neural Systems Group Max Planck Institute for Psycholinguistics Netherlands Donders Centre for Cognitive Neuroimaging Radboud University Netherlands Department of Psychology University of Illinois United States

People readily generalize knowledge to novel domains and stimuli. We present a theory, instantiated in a computational model, based on the idea that cross-domain generalization in humans is a case of analogical inference over structured (i.e., symbolic) relational representations. The model is an extension of the LISA (Hummel & Holyoak, 1997) and DORA (Doumas et al., 2008) models of relational inference and learning. The resulting model learns both the content and format (i.e., structure) of relational representations from non-relational inputs without supervision, when augmented with the capacity for reinforcement learning, leverages these representations to learn individual domains, and then generalizes to new domains on the first exposure (i.e., zero-shot learning) via analogical inference. We demonstrate the capacity of the model to learn structured relational representations from a variety of simple visual stimuli, and to perform cross-domain generalization between video games (Breakout and Pong) and between several psychological tasks. We demonstrate that the model's trajectory closely mirrors the trajectory of children as they learn about relations, accounting for phenomena from the literature on the development of children's reasoning and analogy making. The model's ability to generalize between domains demonstrates the flexibility afforded by representing domains in terms of their underlying relational structure, rather than simply in terms of the statistical relations between their inputs and outputs. © 2019, CC BY-NC-SA.

关键词： computation theory

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Author Correction: Spatial communication systems across languages reflect universal action constraints

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Nature human behaviour 2024年第1期8卷 181页

作者： Kenny R Coventry Harmen B Gudde Holger Diessel Jacqueline Collier Pedro Guijarro-Fuentes Mila Vulchanova Valentin Vulchanov Emanuela Todisco Maria Reile Merlijn Breunesse Helen Plado Juergen Bohnemeyer Raed Bsili Michela Caldano Rositsa Dekova Katharine Donelson Diana Forker Yesol Park Lekhnath Sharma Pathak David Peeters Gabriella Pizzuto Baris Serhan Linda Apse Florian Hesse Linh Hoang Phuong Hoang Yoko Igari Keerthana Kapiley Tamar Haupt-Khutsishvili Sara Kolding Katri Priiki Ieva Mačiukaitytė Vaisnavi Mohite Tiina Nahkola Sum Yi Tsoi Stefan Williams Shunei Yasuda Angelo Cangelosi Jon Andoni Duñabeitia Ramesh Kumar Mishra Roberta Rocca Jurģis Šķilters Mikkel Wallentin Eglė Žilinskaitė-Šinkūnienė Ozlem Durmaz Incel School of Psychology University of East Anglia Norwich UK. k.coventry@uea.ac.uk. School of Psychology University of East Anglia Norwich UK. Helmholtz Institute Department of Experimental Psychology Utrecht University Utrecht the Netherlands. Hanse-Wissenschaftskolleg Delmenhorst Germany. Department of English Friedrich-Schiller-Universität Jena Jena Germany. Department of Spanish Modern and Classic Philology University of the Balearic Islands Palma Spain. Language Acquisition and Language Processing Lab Department of Language and Literature Norwegian University of Science and Technology Trondheim Norway. Department of Spanish Language Linguistics and Literature Theory University of Seville Seville Spain. Institute of Estonian and General Linguistics University of Tartu Tartu Estonia. Centre for the Arts in Society Leiden University Leiden the Netherlands. Võro Institute Võru Estonia. Department of Linguistics University at Buffalo Buffalo NY USA. Danieli Telerobot Srl Genoa Italy. Istituto Italiano di Tecnologia (IIT) Genoa Italy. Paisii Hilendarski University of Plovdiv Plovdiv Bulgaria. Department of English University of Nevada Reno NV USA. Department of Slavonic Languages and Caucasus Studies Friedrich-Schiller-Universität Jena Jena Germany. Cognitive Science Department of Humanities Social and Political Sciences ETH Zürich Zürich Switzerland. Cognitive Science and Psycholinguistics Lab Central Department of Linguistics Tribhuvan University Kathmandu Nepal. Centre for Neural and Cognitive Sciences School of Medical Sciences University of Hyderabad Hyderabad India. Department of Communication and Cognition TiCC Tilburg University Tilburg the Netherlands. Max Planck Institute for Psycholinguistics Nijmegen the Netherlands. Department of Chemistry University of Liverpool Liverpool UK. Department of Computer Science University of Manchester Manchester UK. Laboratory for Perceptual and Cognitive Systems Faculty of Computing University o

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