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Identifying such patterns is particularly relevant in heart rate (HR) responses to threat, where subtle variations may reflect distinct coping mechanisms such as orienting or defense. Machine learning techniques that learn latent representations, particularly variational autoencoders (VAEs), offer powerful tools for revealing such hidden structures. This methodological report introduces a simple VAE\u2010based approach for characterizing HR responses to threat pictures in 165 participants. To validate the method, simulations first demonstrated that the model accurately separated simulated HR waveforms. The VAE was then applied to empirical HR responses, mapping them into a three\u2010dimensional latent space for subsequent cluster analysis, which was compared to clustering based directly on raw HR waveforms. The VAE revealed three distinct response profiles: (1) strong decelerators (cardiac orienting response), (2) weak decelerators with late acceleration, and (3) immediate accelerators without a decelerative phase. In contrast, clustering raw HR waveforms identified only two groups. Clusters derived from the latent space were more coherent and exhibited greater within\u2010group consistency. Finally, applying the pre\u2010trained autoencoder to a small fear\u2010conditioning dataset enabled characterization of distinct HR response patterns despite limited sample size. These findings show that even a basic autoencoder enhances the categorization of psychophysiological response patterns, offering a framework for linking individual autonomic variability to broader models of affective and defensive behavior.<\/jats:p>","DOI":"10.1111\/psyp.70314","type":"journal-article","created":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T04:51:21Z","timestamp":1778475081000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Uncovering Heart Rate Response Patterns to Threat Pictures Through Deep Latent Representation Learning with a Variational Autoencoder"],"prefix":"10.1111","volume":"63","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0824-8759","authenticated-orcid":false,"given":"Stephan","family":"Moratti","sequence":"first","affiliation":[{"name":"Department of Experimental Psychology, Cognitive Processes and Speech Therapy Complutense University of Madrid  Madrid Spain"},{"name":"Emotional Processing Laboratory (EPL), Center for Cognitive and Computational Neuroscience C3N, Complutense University of Madrid  Madrid Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7928-2248","authenticated-orcid":false,"given":"Sergio Felipe Calvo","family":"Garc\u00eda","sequence":"additional","affiliation":[{"name":"Department of Experimental Psychology, Cognitive Processes and Speech Therapy Complutense University of Madrid  Madrid Spain"},{"name":"Emotional Processing Laboratory (EPL), Center for Cognitive and Computational Neuroscience C3N, Complutense University of Madrid  Madrid Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,5,10]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"crossref","unstructured":"Abdelaziz Dahou Djilali Y. 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