arXiv subjects

Naotsugu Tsuchiya

Publications and source records attributed to Naotsugu Tsuchiya.

10 recordsLinked to original sources

arXiv2026

The Rosetta Stone and Levels of Principled Inference to the Experience of Another Mind

The classical problem of Other Minds has dogged philosophers for millennia; asking if we have any way to truly understand the experience of another mind. We know our own intrinsic experience by acquaintance, but can only ever hope to possess an extrinsic description of another's, with the two separated by an acquaintance gap. Structural approaches aim to characterise experience in terms of a mathematical structure, and promise a 'Rosetta Stone'; that is, a principled method to translate the contents of experience into a mathematical structure. In this chapter, we examine two such approaches - the Qualia Structure Paradigm (Qstr) and Integrated Information Theory (IIT) - to ask what, if anything, they allow us to infer about another mind should they possess the Rosetta Stone they seek. Qstr proceeds inter-phenomenally; aiming to exhaustively characterise an experience by its relations to all other experiences and providing a necessary condition on the sameness of experience. IIT proceeds intra-phenomenally; conjecturing that a single experience is accounted for by the cause-effect structure unfolded from a substrate in a state, an explanatory identity that is both necessary and sufficient for the sameness of experience. While neither approach can cross the acquaintance gap to another's mind, they provide a common formal medium through which minds may be compared and in turn reduce the size of this gap. We identify the strength of inference with levels of structural correspondence in Category Theory. These descend from isomorphism through strong and weak adjunction, to a principled limit where inference runs out. We conclude that these structural approaches and the existence of their respective Rosetta Stones would not solve the problem of Other Minds, but instead provide a system of principled constraints on what we may infer, which is far more than what has been justifiable before.

q-bio.NC↗

arXiv2025

Towards a (meta-)mathematical theory of consciousness: universal (mapping) properties of experience

Conscious experience permeates our daily lives, yet general consensus on a theory of consciousness remains elusive. In the face of such difficulty, an alternative strategy is to address a more general (meta-level) version of the problem for insights into the original problem at hand. Category theory was developed for this purpose, i.e. as an axiomatic (meta-)mathematical theory for comparison of mathematical structures, and so affords a (formally) formal approach towards a theory of consciousness. In this way, category theory is used for comparison with Information Integration Theory (IIT) as a supposed axiomatic theory of consciousness, which says that every conscious state involves six axiomatic properties: the IIT axioms for consciousness. All six axioms are shown to follow from the categorical notion of a universal mapping property: a unique-existence condition for all instances in the domain of interest. Accordingly, this categorical approach affords a formal basis for further development of a (meta-)mathematical theory of consciousness, whence the slogan, ``Consciousness is a universal property.''

q-bio.NC↗

arXiv2025

Incorporating episodic memory into quantum models of judgment and decision

An important challenge for quantum theories of cognition and decision concerns the incorporation of memory for recently made judgments and their effects on later judgments. First, we review a general approach to measurement based on system plus environment representations of states and measurement instruments. These more general measurement models provide ways to incorporate effects of recent judgments on later judgments. Then we compare three different measurement models that are based on these more general measurement operations to a puzzling collection of question order effect findings.

physics.soc-ph↗

arXiv2025

Constructive Approach to Bidirectional Influence between Qualia Structure and Language Emergence

This perspective paper explores the bidirectional influence between language emergence and the relational structure of subjective experiences, termed qualia structure, and lays out a constructive approach to the intricate dependency between the two. We hypothesize that the emergence of languages with distributional semantics (e.g., syntactic-semantic structures) is linked to the coordination of internal representations shaped by experience, potentially facilitating more structured language through reciprocal influence. This hypothesized mutual dependency connects to recent advancements in AI and symbol emergence robotics, and is explored within this paper through theoretical frameworks such as the collective predictive coding. 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 influence 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.

cs.CL↗

arXiv2024

Gromov-Wasserstein unsupervised alignment reveals structural correspondences between the color similarity structures of humans and large language models

Large Language Models (LLMs), such as the General Pre-trained Transformer (GPT), have shown remarkable performance in various cognitive tasks. However, it remains unclear whether these models have the ability to accurately infer human perceptual representations. Previous research has addressed this question by quantifying correlations between similarity response patterns of humans and LLMs. Correlation provides a measure of similarity, but it relies pre-defined item labels and does not distinguish category- and item- level similarity, falling short of characterizing detailed structural correspondence between humans and LLMs. To assess their structural equivalence in more detail, we propose the use of an unsupervised alignment method based on Gromov-Wasserstein optimal transport (GWOT). GWOT allows for the comparison of similarity structures without relying on pre-defined label correspondences and can reveal fine-grained structural similarities and differences that may not be detected by simple correlation analysis. Using a large dataset of similarity judgments of 93 colors, we compared the color similarity structures of humans (color-neurotypical and color-atypical participants) and two GPT models (GPT-3.5 and GPT-4). Our results show that the similarity structure of color-neurotypical participants can be remarkably well aligned with that of GPT-4 and, to a lesser extent, to that of GPT-3.5. These results contribute to the methodological advancements of comparing LLMs with human perception, and highlight the potential of unsupervised alignment methods to reveal detailed structural correspondences. This work has been published in Scientific Reports, DOI: https://doi.org/10.1038/s41598-024-65604-1.

q-bio.NC↗

arXiv2023

Unifying Pairwise Interactions in Complex Dynamics

Scientists have developed hundreds of techniques to measure the interactions between pairs of processes in complex systems. But these computational methods, from correlation coefficients to causal inference, rely on distinct quantitative theories that remain largely disconnected. Here we introduce a library of 237 statistics of pairwise interactions and assess their behavior on 1053 multivariate time series from a wide range of real-world and model-generated systems. Our analysis highlights new commonalities between different mathematical formulations, providing a unified picture of a rich interdisciplinary literature. Using three real-world case studies, we then show that simultaneously leveraging diverse methods from across science can uncover those most suitable for addressing a given problem, yielding interpretable understanding of the conceptual formulations of pairwise dependence that drive successful performance. Our framework is provided in extendable open software, enabling comprehensive data-driven analysis by integrating decades of methodological advances.

physics.data-an↗

arXiv2020

General anesthesia reduces complexity and temporal asymmetry of the informational structures derived from neural recordings in Drosophila

We apply techniques from the field of computational mechanics to evaluate the statistical complexity of neural recording data from fruit flies. First, we connect statistical complexity to the flies' level of conscious arousal, which is manipulated by general anesthesia (isoflurane). We show that the complexity of even single channel time series data decreases under anesthesia. The observed difference in complexity between the two states of conscious arousal increases as higher orders of temporal correlations are taken into account. We then go on to show that, in addition to reducing complexity, anesthesia also modulates the informational structure between the forward- and reverse-time neural signals. Specifically, using three distinct notions of temporal asymmetry we show that anesthesia reduces temporal asymmetry on information-theoretic and information-geometric grounds. In contrast to prior work, our results show that: (1) Complexity differences can emerge at very short timescales and across broad regions of the fly brain, thus heralding the macroscopic state of anesthesia in a previously unforeseen manner, and (2) that general anesthesia also modulates the temporal asymmetry of neural signals. Together, our results demonstrate that anesthetized brains become both less structured and more reversible.

q-bio.NC↗

arXiv2017

Geometry of Information Integration

Information geometry is used to quantify the amount of information integration within multiple terminals of a causal dynamical system. Integrated information quantifies how much information is lost when a system is split into parts and information transmission between the parts is removed. Multiple measures have been proposed as a measure of integrated information. Here, we analyze four of the previously proposed measures and elucidate their relations from a viewpoint of information geometry. Two of them use dually flat manifolds and the other two use curved manifolds to define a split model. We show that there are hierarchical structures among the measures. We provide explicit expressions of these measures.

cs.IT↗

arXiv2015

A unified framework for information integration based on information geometry

We propose a unified theoretical framework for quantifying spatio-temporal interactions in a stochastic dynamical system based on information geometry. In the proposed framework, the degree of interactions is quantified by the divergence between the actual probability distribution of the system and a constrained probability distribution where the interactions of interest are disconnected. This framework provides novel geometric interpretations of various information theoretic measures of interactions, such as mutual information, transfer entropy, and stochastic interaction in terms of how interactions are disconnected. The framework therefore provides an intuitive understanding of the relationships between the various quantities. By extending the concept of transfer entropy, we propose a novel measure of integrated information which measures causal interactions between parts of a system. Integrated information quantifies the extent to which the whole is more than the sum of the parts and can be potentially used as a biological measure of the levels of consciousness.

q-bio.NC↗

arXiv2015

Measuring integrated information from the decoding perspective

Accumulating evidence indicates that the capacity to integrate information in the brain is a prerequisite for consciousness. Integrated Information Theory (IIT) of consciousness provides a mathematical approach to quantifying the information integrated in a system, called integrated information, $Φ$. Integrated information is defined theoretically as the amount of information a system generates as a whole, above and beyond the sum of the amount of information its parts independently generate. IIT predicts that the amount of integrated information in the brain should reflect levels of consciousness. Empirical evaluation of this theory requires computing integrated information from neural data acquired from experiments, although difficulties with using the original measure $Φ$ precludes such computations. Although some practical measures have been previously proposed, we found that these measures fail to satisfy the theoretical requirements as a measure of integrated information. Measures of integrated information should satisfy the lower and upper bounds as follows: The lower bound of integrated information should be 0 when the system does not generate information (no information) or when the system comprises independent parts (no integration). The upper bound of integrated information is the amount of information generated by the whole system and is realized when the amount of information generated independently by its parts equals to 0. Here we derive the novel practical measure $Φ^*$ by introducing a concept of mismatched decoding developed from information theory. We show that $Φ^*$ is properly bounded from below and above, as required, as a measure of integrated information. We derive the analytical expression $Φ^*$ under the Gaussian assumption, which makes it readily applicable to experimental data.

q-bio.NC↗