Decoding the phenomenology of spontaneous thought using large language-model ratings on verbal retrospective free reports
Nicolás Bruno, Federico Cavanna, Federico Zamberlán, Tomas D’amelio, Stephanie Müller, Laura Alethia de la Fuente, Jacobo Sitt, Antoni Valero Cabre, Mirta Villarreal, Enzo Tagliazucchi, Carla Pallavicini
bioRxiv Preprint Server April 22, 2026 preprint DOI: 10.64898/2026.04.22.720079 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Experimental study |
|---|---|
| Sample size | 22 |
| Population | Participants who performed an eyes-closed free-thinking task |
| Intervention | eyes-closed free-thinking task |
| Topics | Philosophy of mind |
| Key findings | Machine-learning models achieved above-chance accuracy for predicting emotional valence from EEG features, and LLMs showed higher inter-rater agreement than human raters in evaluating spontaneous thought reports. |
Abstract
Spontaneous thoughts constitute most of everyday inner experience, yet long-standing methodological challenges obscure a thorough exploration of their content and neurophysiological underpinnings. Traditional approaches relying on thought probes impose strict constraints on phenomenological reports, whereas online verbal reports disrupt the natural flow of experience while interfering neural signals with motor artifacts. Here, we designed and tested an alternative approach to assess the neural basis of spontaneous thoughts combining delayed verbal retrospective free reports (RFR) with automated phenomenological ratings generated by large language models (LLMs). Twenty-two participants performed an eyes-closed free-thinking task, providing reports that were evaluated along ten phenomenological dimensions by four state-of-the-art LLMs and a panel of human raters. Machine-learning models (ML) were then trained to decode LLM-derived ratings from EEG spectral, complexity, and connectivity features. Our analyses showed that inter-rater agreement among LLMs exceeded that of human raters whereas ML models achieved above-chance accuracy for the prediction of emotional valence. These findings provide support for the use of LLMs for a scalable phenomenological annotation of spontaneous thoughts and suggest that their affective dimensions can be decoded from concurrent EEG activity.
Comparable studies
Other experimental studies on philosophy of mind, most cited first.
| Study | Year | Design | Participants |
|---|---|---|---|
| A Deep-Dream Virtual Reality Platform for Studying Altered Perceptual Phenomenology | 2017 | Experimental study | |
| New World Tryptamine Hallucinogens and the Neuroscience of Ayahuasca. | 2018 | Review with original neuroimaging and neurophysiological data | |
| Differential contributions of serotonergic and dopaminergic functional connectivity to the phenomenology of LSD Healthy adults | 2022 | Secondary analysis of experimental data | n = 15 |
| TMS-EEG and resting-state EEG applied to altered states of consciousness: oscillations, complexity, and phenomenology. | 2023 | Experimental study | |
| Training novice practitioners to reliably report their meditation experience using shared phenomenological dimensions. Novice meditation practitioners | 2019 | Neurophenomenological experiment |
Citations in the library
Cites 16
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- The Decoupled Mind: Mind-wandering Disrupts Cortical Phase-locking to Perceptual Events Journal of Cognitive Neuroscience April 17, 2014
- Comparing the phenomenological qualities of stimulus-independent thought, stimulus-dependent thought and dreams using experience sampling Philosophical transactions of the Royal Society of London. Series B, Biological sciences December 13, 2020
- Opening the black box: Think Aloud as a method to study the spontaneous stream of consciousness. Consciousness and Cognition February 1, 2025
and 11 more in the library