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Toward stressor-free stress estimation: The integrated information theory explains the information dynamics of stress.

ABSTRACT: Recent neurological studies have revealed several detailed stress mechanisms. However, the latent variables behind stress study still interpret stress responses as difficult. Therefore, we propose a stressor-free method of stress evaluation using Integrated Information Theory (IIT) to address these issues. We conducted experiments inducing acute stress responses against tasks with three levels of difficulty, easy, moderate, and difficult, to verify the IIT in stress study. The moderate condition was related to active coping with stress. By contrast, the easy and difficult conditions are related to passive stress coping. Especially, the easy condition seemed to cause boredom. Our results revealed that the degree of entangled system fluctuation was associated with the subjective ratings for the tasks. Interestingly, our method could also evaluate stress as a state of boredom that had not received much research attention. Our method can be an alternative stress estimation method to overcome the latent variable problem.

SUBMITTER: Niizato T

PROVIDER: S-EPMC11357877 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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Toward stressor-free stress estimation: The integrated information theory explains the information dynamics of stress.

Niizato Takayuki T Nishiyama Yuta Y Oka Yuta Y Aung Poe Thinzar PT Nomura Shusaku S

iScience 20240726 8

Recent neurological studies have revealed several detailed stress mechanisms. However, the latent variables behind stress study still interpret stress responses as difficult. Therefore, we propose a stressor-free method of stress evaluation using Integrated Information Theory (IIT) to address these issues. We conducted experiments inducing acute stress responses against tasks with three levels of difficulty, easy, moderate, and difficult, to verify the IIT in stress study. The moderate condition ...[more]

PMID: 39211542

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Project description:The Free Energy Principle and Active Inference Framework (FEP-AI) begins with the understanding that persisting systems must regulate environmental exchanges and prevent entropic accumulation. In FEP-AI, minds and brains are predictive controllers for autonomous systems, where action-driven perception is realized as probabilistic inference. Integrated Information Theory (IIT) begins with considering the preconditions for a system to intrinsically exist, as well as axioms regarding the nature of consciousness. IIT has produced controversy because of its surprising entailments: quasi-panpsychism; subjectivity without referents or dynamics; and the possibility of fully-intelligent-yet-unconscious brain simulations. Here, I describe how these controversies might be resolved by integrating IIT with FEP-AI, where integrated information only entails consciousness for systems with perspectival reference frames capable of generating models with spatial, temporal, and causal coherence for self and world. Without that connection with external reality, systems could have arbitrarily high amounts of integrated information, but nonetheless would not entail subjective experience. I further describe how an integration of these frameworks may contribute to their evolution as unified systems theories and models of emergent causation. Then, inspired by both Global Neuronal Workspace Theory (GNWT) and the Harmonic Brain Modes framework, I describe how streams of consciousness may emerge as an evolving generation of sensorimotor predictions, with the precise composition of experiences depending on the integration abilities of synchronous complexes as self-organizing harmonic modes (SOHMs). These integrating dynamics may be particularly likely to occur via richly connected subnetworks affording body-centric sources of phenomenal binding and executive control. Along these connectivity backbones, SOHMs are proposed to implement turbo coding via loopy message-passing over predictive (autoencoding) networks, thus generating maximum a posteriori estimates as coherent vectors governing neural evolution, with alpha frequencies generating basic awareness, and cross-frequency phase-coupling within theta frequencies for access consciousness and volitional control. These dynamic cores of integrated information also function as global workspaces, centered on posterior cortices, but capable of being entrained with frontal cortices and interoceptive hierarchies, thus affording agentic causation. Integrated World Modeling Theory (IWMT) represents a synthetic approach to understanding minds that reveals compatibility between leading theories of consciousness, thus enabling inferential synergy.

Project description:The effective reproduction number, Rt, is a key time-varying prognostic for the growth rate of any infectious disease epidemic. Significant changes in Rt can forewarn about new transmissions within a population or predict the efficacy of interventions. Inferring Rt reliably and in real-time from observed time-series of infected (demographic) data is an important problem in population dynamics. The renewal or branching process model is a popular solution that has been applied to Ebola and Zika virus disease outbreaks, among others, and is currently being used to investigate the ongoing COVID-19 pandemic. This model estimates Rt using a heuristically chosen piecewise function. While this facilitates real-time detection of statistically significant Rt changes, inference is highly sensitive to the function choice. Improperly chosen piecewise models might ignore meaningful changes or over-interpret noise-induced ones, yet produce visually reasonable estimates. No principled piecewise selection scheme exists. We develop a practical yet rigorous scheme using the accumulated prediction error (APE) metric from information theory, which deems the model capable of describing the observed data using the fewest bits as most justified. We derive exact posterior prediction distributions for infected population size and integrate these within an APE framework to obtain an exact and reliable method for identifying the piecewise function best supported by available epidemic data. We find that this choice optimises short-term prediction accuracy and can rapidly detect salient fluctuations in Rt, and hence the infected population growth rate, in real-time over the course of an unfolding epidemic. Moreover, we emphasise the need for formal selection by exposing how common heuristic choices, which seem sensible, can be misleading. Our APE-based method is easily computed and broadly applicable to statistically similar models found in phylogenetics and macroevolution, for example. Our results explore the relationships among estimate precision, forecast reliability and model complexity.

Dataset Information

Toward stressor-free stress estimation: The integrated information theory explains the information dynamics of stress.

Publications

Toward stressor-free stress estimation: The integrated information theory explains the information dynamics of stress.

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