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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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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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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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