ORIGINAL RESEARCH article

Front. Comput. Neurosci.

Volume 19 - 2025 | doi: 10.3389/fncom.2025.1578135

This article is part of the Research TopicInterdisciplinary Synergies in Neuroinformatics, Cognitive Computing, and Computational NeuroscienceView all 4 articles

Modeling Autonomous Shifts Between Focus State and Mind-Wandering Using a Predictive-Coding-Inspired Variational Recurrent Neural Network

Provisionally accepted
  • Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan

The final, formatted version of the article will be published soon.

Mind-wandering reflects a dynamic interplay between focused attention and off-task mental states. Despite its relevance in understanding fundamental cognitive processes, such as attention regulation, decision-making, and creativity, previous models have not yet provided an account of the neural mechanisms for autonomous shifts between focus state (FS) and mind-wandering (MW). To address this, we conduct model simulation experiments employing predictive coding as a theoretical framework of perception to investigate possible neural mechanisms underlying these autonomous shifts between the two states. In particular, we modeled perception processes of continuous sensory sequences using our previously proposed variational RNN model under free energy minimization. The current study extends this model by introducing an online adaptation mechanism of a meta-level parameter, referred to as the meta-prior w, which regulates the complexity term in the free energy minimization. Our simulation experiments demonstrated that autonomous shifts between FS and MW take place when w switches between low and high values responding to a decrease and increase of the average reconstruction error over a past time window. Particularly, high w prioritized top-down predictions while low w emphasized bottom-up sensations. In this work, we speculate that self-awareness of MW may occur when the error signal accumulated over time exceeds a certain threshold. Finally, this paper explores how our experiment results align with existing studies and highlights their potential for future research.

Keywords: mind-wandering, predictive coding, Free Energy Principle, variational RNN, Brain-Inspired Modeling

Received: 17 Feb 2025; Accepted: 26 May 2025.

Copyright: © 2025 Oyama and Tani. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Henrique Oyama, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan
Jun Tani, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan

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