Evidence map›Paper›PMID 41332764›Full record

ArticlebioRxiv : the preprint server for biology2025

Auditory Cortical Gradients Integrate Bottom-Up and Top-Down Structure During Natural Sound Categorisation.

David Haydock, Robert Leech, Magdalena Kachlicka, Frederic Dick

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

David HaydockDepartment of Experimental Psychology, University College London (UCL), UK.ORCID 0000-0003-1247-0328
Robert LeechInstitute of Psychiatry, Psychology & Neuroscience, King's College London, UK.ORCID 0000-0002-5801-6318
Magdalena KachlickaSchool of Psychological Sciences, Birkbeck, University of London, UK.ORCID 0000-0002-6345-4152
Frederic DickDepartment of Experimental Psychology, University College London (UCL), UK.ORCID 0000-0002-2933-3912

Funding

LEARNING COMPLEX AUDITORY CATEGORIESR01DC004674 · NIDCD · CARNEGIE-MELLON UNIVERSITY · PI HOLT, LORI L, LOTTO, ANDREW J · 2001 to 2015
$3.8M
NIDCD NIH HHS R01 DC004674
6 · The paper itself

Abstract

Understanding how the brain organises natural categories is a central challenge in neuroscience. While prior work has shown that categories can be decoded from distributed activity patterns in auditory cortex, it remains unclear how these categories are globally arranged relative to one another, and how low-level acoustic and higher-level semantic structure jointly shape this organisation. Here, we addressed these questions by deriving low-dimensional functional gradients from high-depth functional magnetic resonance imaging (fMRI) data (three participants, ~4.7 hours each) acquired during a category-specific one-back task. These gradients captured the principal axes of population activity in auditory cortex. Gradient-based models of the auditory cortex explained category structure more accurately than region-of-interest or whole-brain approaches, revealing that category information is distributed across multiple continuous axes rather than aligned with any single organisational dimension. Projecting acoustic (gammatone filter-bank) and behavioural similarity spaces directly into a shared framework with the fMRI functional axes showed that both contribute to the brain's category geometry, with acoustic structure exerting a somewhat stronger influence. However, representational relationships varied across category pairs: some reflected primarily acoustic similarity, others semantic distinctions, and many a combination of both. This pairwise heterogeneity shows how auditory cortex may integrate multiple representational dimensions that define higher-level categories.

Indexed as

Auditory CortexBiological SciencesfMRIFunctional GradientsNeuroscienceRepresentational GeometrySpectral-Semantic Integration

Identifiers

PMID41332764
PMCPMC12667770

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.