Evidence map›Paper›PMID 42303808›Full record

ArticleBehavior research methods2026

Mind Melodies: An NLP platform to examine music cognition.

Adolfo M García, Alejandro Sosa Welford, Joaquín Ponferrada, Franco J Ferrante, Camilo Avendaño, Gonzalo Pérez, Agustina Birba, Ivan Caro, Juan Francisco Saavedra, Daniel Escobar Grisales and 1 more

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Article in Behavior research methods, 2026. 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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0citing papers in PubMed
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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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

11 authors.

Adolfo M GarcíaCentro de Neurociencias Cognitivas, Departamento de Ciencias de la Vida y del Comportamiento, Universidad de San Andrés, Buenos Aires, Argentina. adolfo.garcia@gbhi.org.
Alejandro Sosa WelfordCentro de Neurociencias Cognitivas, Departamento de Ciencias de la Vida y del Comportamiento, Universidad de San Andrés, Buenos Aires, Argentina.
Joaquín PonferradaCentro de Neurociencias Cognitivas, Departamento de Ciencias de la Vida y del Comportamiento, Universidad de San Andrés, Buenos Aires, Argentina.
Franco J FerranteCentro de Neurociencias Cognitivas, Departamento de Ciencias de la Vida y del Comportamiento, Universidad de San Andrés, Buenos Aires, Argentina.
Camilo AvendañoConsejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Ciudad Autónoma de Buenos Aires, Argentina.
Gonzalo PérezCentro de Neurociencias Cognitivas, Departamento de Ciencias de la Vida y del Comportamiento, Universidad de San Andrés, Buenos Aires, Argentina.
Agustina BirbaCentro de Neurociencias Cognitivas, Departamento de Ciencias de la Vida y del Comportamiento, Universidad de San Andrés, Buenos Aires, Argentina.
Ivan CaroCentro de Neurociencias Cognitivas, Departamento de Ciencias de la Vida y del Comportamiento, Universidad de San Andrés, Buenos Aires, Argentina.
Juan Francisco SaavedraCentro de Neurociencias Cognitivas, Departamento de Ciencias de la Vida y del Comportamiento, Universidad de San Andrés, Buenos Aires, Argentina.
Daniel Escobar GrisalesFacultad de Ingeniería, Universidad de Antioquia, Medellín, Colombia.
Bruno MeszInstituto de Investigación en Arte y Cultura (IIAC), Universidad Nacional de Tres de Febrero (UNTREF), Buenos Aires, Argentina.

Funding

An automated machine learning approach to language changes in Alzheimer’s disease and frontotemporal dementia across Latino and English-speaking populationsR01AG075775 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI MARIA LUISA GORNO TEMPINI, Adolfo Martin Garcia · 2023 to 2026
$7.2M
Agencia Nacional de Promoción Científica y Tecnológica 01-PICTE-2022-05-00103Fondo Nacional de Desarrollo Científico y Tecnológico 1250091Fondo Nacional de Desarrollo Científico y Tecnológico 1250317NIA NIH HHS 2P01AG019724-21A1NIA NIH HHS R01AG075775
6 · The paper itself

Abstract

Music is a fundamental semiotic system that shapes our daily experience by evoking thoughts and emotions. Across genres, a key driver of these effects is musical mode (the scale variation employed in a given piece or passage). To provide quantitative, ecologically valid insights on these phenomena, we developed Mind Melodies, an interactive platform that combines art, science, and technology to reveal individual and collective effects triggered by each Greek mode (Ionian, Dorian, Phrygian, Lydian, Mixolydian, Aeolian, and Locrian). Here we aim to (i) describe the project's rationale; (ii) outline the platform's interfaces for data collection, feature extraction, and visualization; and (iii) report preliminary findings from a pilot study. In Mind Melodies, participants listen to highly controlled musical compositions in different modes and to type out the mental scenes thus elicited. Natural language processing and deep learning algorithms are used to identify and visualize the main concepts, emotions, and imagery elicited by each mode. The resulting metrics are subjected to statistical analysis to capture individual and collective patterns. In a pilot study with 142 participants, we found that each mode was distinctly associated with a different semantic field and partly dissimilar emotions (e.g., Ionian was linked to 'life' and 'joy'; Phrygian to 'solitude', 'sadness', and 'fear'; Lydian to 'nature' and 'joy'; and Aeolian to 'mystery' and 'sadness'). Also, specific modes (e.g., Dorian and Mixolydian, Phrygian and Locrian) presented greater similarity than others. Overall, Mind Melodies represents a scalable transdisciplinary framework to inform science-first initiatives in the field of music cognition.

Indexed as

Auditory PerceptionCognitionMusicNatural Language ProcessingAdultEmotionsFemaleHumansMalePilot ProjectsConceptsEmotionsGreek modesImageryMusic cognitionNatural language processing

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Registered trials

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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.