A Supervised Machine Learning Approach to Classify Brain Morphology of Professional Visual Artists versus Non-Artists.

Sensors (Basel) · 2023 · PMC10181039 · PMID 37177406

Europe PMC DOI

hedged sentences

0.0520

closest p · 1.0× alpha

0.0700

boldest claim

The sentences

a trend toward significancep = 0.052so close (0.05 < p ≤ 0.1)

This correlation returned a trend toward significance (rho = −0.401, p = 0.052). 4.

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a positive trend toward significancep = 0.07so close (0.05 < p ≤ 0.1)

The results showed a positive trend toward significance (rho = 0.376, p = 0.07), partially confirming a relation between the circuit that predicts being an artist and the ability of imagery.

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a trend towards significanceno p-value reported

There was also a trend towards significance between the circuit and a measure of vividness of imagery, further supporting the idea that these brain regions may be related to the imagery abilities involved in the artistic process. magnetic resonance imaging (MRI) visual arts creativity supervised machine learning artists gray matter multi-ker

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approaching significanceno p-value reported

Precisely, we found a correlation approaching significance between mental imagery scores and the circuit.

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