---
title: "Neuro-computational method to approximate sensory qualities of mental images"
id: "biorxiv-0-a-neuro-computational-approximation-of-the-qualities-of-mental-images"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-0-a-neuro-computational-approximation-of-the-qualities-of-mental-images"
content_type: "clinical_feed_article"
specialty: "Neurology"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.08.25.747062v1?rss=1"
published_at: "2026-09-02T07:37:30.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Neuro-computational method to approximate sensory qualities of mental images
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-0-a-neuro-computational-approximation-of-the-qualities-of-mental-images
- **Specialty:** [Neurology](https://medichelpline.com/clinical-feed/neurology.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.08.25.747062v1?rss=1)
- **Published At:** 2026-09-02T07:37:30.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- The authors developed a **neuro-computational** approach to approximate and evaluate the properties of **mental images** without relying on introspective reports. - They collected a large EEG dataset: 10 participants, 10 sessions each, totaling 43,200 trials, in which participants imagined 16 scenes prompted by text. - Using AI image generation, the team produced candidate image sets that approximated the content of the prompted mental images. - Visual cortex responses to those candidate images were computationally simulated and compared to rhythmic EEG signals recorded during imagery to assess representational alignment. - Mid- to high-level features of the AI-generated candidate images showed reliable alignment with human **alpha** activity, consistent with prior work linking imagery to alpha-band signals. - By systematically altering candidate-image qualities, the authors tested which visual properties increased representational alignment with cortical imagery patterns. - Results indicated higher alignment for spatially **blurred** and **low contrast** images, supporting the notion that mental images often have reduced sensory detail. - The analysis also identified a tendency for imagery to align with a “psychedelic” image style characterized by flows and distortions of proportions, suggesting imagery may include global distortions beyond mere degradation of detail. - The approach provides an objective, hypothesis-driven alternative to introspection and complements current reconstruction-based methods for probing mental imagery. - Data, code, and image sets are available via repositories cited in the article (GitHub, OSF, Hugging Face).
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Rico Stecher Justus Liebig University Giessen * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Rico%2BStecher%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Stecher%20R&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ARico%2BStecher%2B) * [ORCID record for Rico Stecher](http://orcid.org/0000-0002-6627-910X "Open in new tab") * For correspondence: rico.stecher@yahoo.de Daniel Kaiser Justus Liebig University Giessen * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Daniel%2BKaiser%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Kaiser%20D&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ADaniel%2BKaiser%2B) * [ORCID record for Daniel Kaiser](http://orcid.org/0000-0002-9007-3160 "Open in new tab") * [Abstract](https://www.biorxiv.org/content/10.64898/2026.08.25.747062v1)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_art/node:5739794/1) * [Info/History](https://www.biorxiv.org/content/10.64898/2026.08.25.747062v1.article-info)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_info/node:5739794/1) * [Metrics](https://www.biorxiv.org/content/10.64898/2026.08.25.747062v1.article-metrics)[](https://www.biorxiv.org/panels_ajax_tab/article_tab_metrics/node:5739794/1) * [Supplementary material](https://www.biorxiv.org/content/10.64898/2026.08.25.747062v1.supplementary-material)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_data/node:5739794/1) * [Data/Code](https://www.biorxiv.org/content/10.64898/2026.08.25.747062v1.external-links)[](https://www.biorxiv.org/panels_ajax_tab/article_tab_data_code/node:5739794/1) * [ Preview PDF](https://www.biorxiv.org/content/10.64898/2026.08.25.747062v1.full.pdf+html)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_pdf/node:5739794/1) ![Loading](https://www.biorxiv.org/sites/all/modules/contrib/panels_ajax_tab/images/loading.gif) ## Abstract Mental images are challenging to study, given that our conscious experience is notoriously hard to access. The currently prevalent introspective methods are inherently subjective and can thus only provide limited access to their qualities. Here, we developed a neuro-computational approach that approximates and assesses the properties of mental images without the need for introspection. To enable this approach, we collected a large-scale EEG dataset (10 participants, 10 sessions each, 43,200 trials total) of participants imagining 16 scenes based on text prompts. We employed AI image generation to create candidate image sets that approximate the content of mental images (based on the imagined text prompts), computationally simulated visual cortex responses to these images and then assessed their representational alignment with rhythmic EEG responses during imagery. In line with previous reports, mid- to high-level features of the AI-generated candidate images yielded reliable alignment with human alpha activity. By manipulating the qualities of the candidate images, we then tested which qualities predisposed higher representational alignment with cortical imagery representations. We found an increased representational alignment for spatially blurred and low contrast images, providing evidence for the prevalent notion of a reduced sensory quality of mental images. We further found that mental imagery may be characterized by a psychedelic image style, which envelops the images in visual flows that distort the image proportions. These results show that our approach can objectively capture qualities of mental images without the need of introspection, providing a hypothesis-based alternative to emerging reconstruction approaches. ### Competing Interest Statement The authors have declared no competing interest. ## Footnotes * * * ## Funder Information Declared European Research Council, https://ror.org/0472cxd90, 101076057 Deutsche Forschungsgemeinschaft, https://ror.org/018mejw64, 533717223 Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a [CC-BY-ND 4.0 International license](http://creativecommons.org/licenses/by-nd/4.0/). bioRxiv and medRxiv thank the following for their generous financial support: > The Chan Zuckerberg Initiative, Cold Spring Harbor Laboratory, the Sergey Brin Family Foundation, California Institute of Technology, Centre National de la Recherche Scientifique, Fred Hutchinson Cancer Center, Imperial College London, Massachusetts Institute of Technology, Stanford University, The University of Edinburgh, University of Washington, and Vrije Universiteit Amsterdam. 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