Constructive Approach to Bidirectional Causation between Qualia Structure and Language Emergence

• URL: http://arxiv.org/abs/2409.09413v1
• Date: Sat, 14 Sep 2024 11:03:12 GMT
• Title: Constructive Approach to Bidirectional Causation between Qualia Structure and Language Emergence
• Authors: Tadahiro Taniguchi, Masafumi Oizumi, Noburo Saji, Takato Horii, Naotsugu Tsuchiya,
• Abstract summary: This paper presents a novel perspective on the bidirectional causation between language emergence and relational structure of subjective experiences. We hypothesize that languages with distributional semantics, e.g., syntactic-semantic structures, may have emerged through the process of aligning internal representations among individuals.
• Score: 5.906966694759679
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a novel perspective on the bidirectional causation between language emergence and relational structure of subjective experiences, termed qualia structure, and lays out the constructive approach to the intricate dependency between the two. We hypothesize that languages with distributional semantics, e.g., syntactic-semantic structures, may have emerged through the process of aligning internal representations among individuals, and such alignment of internal representations facilitates more structured language. This mutual dependency is suggested by the recent advancements in AI and symbol emergence robotics, and collective predictive coding (CPC) hypothesis, in particular. Computational studies show that neural network-based language models form systematically structured internal representations, and multimodal language models can share representations between language and perceptual information. This perspective suggests that language emergence serves not only as a mechanism creating a communication tool but also as a mechanism for allowing people to realize shared understanding of qualitative experiences. The paper discusses the implications of this bidirectional causation in the context of consciousness studies, linguistics, and cognitive science, and outlines future constructive research directions to further explore this dynamic relationship between language emergence and qualia structure.

Related papers

• Analyzing The Language of Visual Tokens [48.62180485759458]
  We take a natural-language-centric approach to analyzing discrete visual languages. We show that higher token innovation drives greater entropy and lower compression, with tokens predominantly representing object parts. We also show that visual languages lack cohesive grammatical structures, leading to higher perplexity and weaker hierarchical organization compared to natural languages.
  arXiv  Detail & Related papers  (2024-11-07T18:59:28Z)
• The Problem of Alignment [1.2277343096128712]
  Large Language Models produce sequences learned as statistical patterns from large corpora. After initial training models must be aligned with human values, prefer certain continuations over others. We examine this practice of structuration as a two-way interaction between users and models.
  arXiv  Detail & Related papers  (2023-12-30T11:44:59Z)
• The Acquisition of Semantic Relationships between words [0.0]
  The study of semantic relationships has revealed a close connection between semantic relationships and the morphological characteristics of a language. By delving into the relationship between semantic relationships and language morphology, we can gain deeper insights into how the underlying structure of words contributes to the interpretation and comprehension of language.
  arXiv  Detail & Related papers  (2023-07-12T19:18:55Z)
• Agentivit\`a e telicit\`a in GilBERTo: implicazioni cognitive [77.71680953280436]
  The goal of this study is to investigate whether a Transformer-based neural language model infers lexical semantics. The semantic properties considered are telicity (also combined with definiteness) and agentivity.
  arXiv  Detail & Related papers  (2023-07-06T10:52:22Z)
• From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought [124.40905824051079]
  We propose rational meaning construction, a computational framework for language-informed thinking. We frame linguistic meaning as a context-sensitive mapping from natural language into a probabilistic language of thought. We show that LLMs can generate context-sensitive translations that capture pragmatically-appropriate linguistic meanings. We extend our framework to integrate cognitively-motivated symbolic modules.
  arXiv  Detail & Related papers  (2023-06-22T05:14:00Z)
• Feature Interactions Reveal Linguistic Structure in Language Models [2.0178765779788495]
  We study feature interactions in the context of feature attribution methods for post-hoc interpretability. We work out a grey box methodology, in which we train models to perfection on a formal language classification task. We show that under specific configurations, some methods are indeed able to uncover the grammatical rules acquired by a model.
  arXiv  Detail & Related papers  (2023-06-21T11:24:41Z)
• Interactive Natural Language Processing [67.87925315773924]
  Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within the field of NLP. This paper offers a comprehensive survey of iNLP, starting by proposing a unified definition and framework of the concept.
  arXiv  Detail & Related papers  (2023-05-22T17:18:29Z)
• A Knowledge-Enhanced Adversarial Model for Cross-lingual Structured Sentiment Analysis [31.05169054736711]
  Cross-lingual structured sentiment analysis task aims to transfer the knowledge from source language to target one. We propose a Knowledge-Enhanced Adversarial Model (textttKEAM) with both implicit distributed and explicit structural knowledge. We conduct experiments on five datasets and compare textttKEAM with both the supervised and unsupervised methods.
  arXiv  Detail & Related papers  (2022-05-31T03:07:51Z)
• Low-Dimensional Structure in the Space of Language Representations is Reflected in Brain Responses [62.197912623223964]
  We show a low-dimensional structure where language models and translation models smoothly interpolate between word embeddings, syntactic and semantic tasks, and future word embeddings. We find that this representation embedding can predict how well each individual feature space maps to human brain responses to natural language stimuli recorded using fMRI. This suggests that the embedding captures some part of the brain's natural language representation structure.
  arXiv  Detail & Related papers  (2021-06-09T22:59:12Z)
• Structured Attention for Unsupervised Dialogue Structure Induction [110.12561786644122]
  We propose to incorporate structured attention layers into a Variational Recurrent Neural Network (VRNN) model with discrete latent states to learn dialogue structure in an unsupervised fashion. Compared to a vanilla VRNN, structured attention enables a model to focus on different parts of the source sentence embeddings while enforcing a structural inductive bias.
  arXiv  Detail & Related papers  (2020-09-17T23:07:03Z)
• Language (Re)modelling: Towards Embodied Language Understanding [33.50428967270188]
  This work proposes an approach to representation and learning based on the tenets of embodied cognitive linguistics (ECL) According to ECL, natural language is inherently executable (like programming languages) This position paper argues that the use of grounding by metaphoric inference and simulation will greatly benefit NLU systems.
  arXiv  Detail & Related papers  (2020-05-01T10:57:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.