Heart and brain neural connection

Decoding Emotions: How Your Body Reacts When You Read Someone's Mind

Mira Elwood in Mind & Education August 2025 • 4 min read.

"New research explores the fascinating link between emotion recognition, heart rate variability, and the subtle physiological signals that reveal how we understand each other."

In our daily lives, accurately recognizing emotions is vital for managing relationships. Facial expressions, are powerful tools that convey emotional and cognitive states during interactions with others. New research delves into the physiological responses triggered during emotion recognition tasks, specifically analyzing how our bodies react when we process facial cues.

The study focuses on Heart Rate Variability (HRV) indices collected during the 'Reading the Mind in the Eyes Test' (RMET). This test assesses a person's ability to recognize emotions from images of eyes. Researchers analyzed how HRV is affected by test difficulty, individual performance, demographic factors, and psychological characteristics.

The central idea is that emotion recognition is closely linked to the Autonomic Nervous System (ANS), which in turn, influences Heart Rate Variability. By applying latent class mixed models, this study explores the complex interplay of these measures and their interactions, aiming to understand how our bodies respond when we attempt to understand what others are feeling.

The Science Behind the Signals: How Emotion Recognition Affects Your Heart

The study revealed that correctly recognizing emotions isn't just a mental process – it triggers measurable physiological changes. Researchers used the Reading the Mind in the Eyes Test (RMET) to evaluate participants' emotion recognition abilities and monitored their heart rate variability (HRV) throughout the test.

Here’s what the researchers measured and why it matters:

Mean of beat to beat (R-R) intervals (meanRR): Average length of time between heartbeats.
Standard deviations of beat to beat (R-R) intervals (std): Variation in the time between heartbeats.
Root mean square of successive differences between adjacent beat-to-beat intervals (RMSSD): Measures short-term heart rate variability.
Standard deviation of the successive differences of the R-R intervals (SDSD): Another measure of short-term heart rate variability.
Number of pairs of successive normal-to-normal (NN) intervals that differ by more than 50 ms (NN50): Indicates vagal activity, a component of the parasympathetic nervous system.
Percentage of differences between adjacent NN intervals that are greater than 50 ms (pNN50): Another indicator of vagal activity.
Average heart rate in beats per minute (mean(bpm)): Average number of heartbeats per minute.
Sample asymmetry (R1/R2): Measures the symmetry of heart rate fluctuations.
Dispersion of points perpendicular to the axis of identity in the Poincaré plot (SD1): Reflects short-term variability.
Dispersion of points along the axis of identity in the Poincaré plot (SD2): Reflects long-term variability.

By analyzing these measures, researchers could see how different factors, like the difficulty of the emotion being recognized and the individual's emotional state, influenced the body's physiological response.

Unlocking the Future: Implications for Mental Health and Social Connection

This research paves the way for a deeper understanding of the intricate relationship between our minds and bodies in social interactions. By identifying the physiological signatures of emotion recognition, researchers can develop new tools for assessing and improving social skills, potentially benefiting individuals with conditions like autism or social anxiety. Further exploration in this area can illuminate how we process and understand the emotions of those around us, enriching our relationships and enhancing our overall well-being.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1371/journal.pone.0207123, Alternate LINK

Title: Modeling Physiological Responses Induced By An Emotion Recognition Task Using Latent Class Mixed Models

Subject: Multidisciplinary

Journal: PLOS ONE

Publisher: Public Library of Science (PLoS)

Authors: Federica Cugnata, Riccardo Maria Martoni, Manuela Ferrario, Clelia Di Serio, Chiara Brombin

Published: 2018-11-16

Everything You Need To Know

What is the primary focus of the research on emotion recognition and how does it work?

The research focuses on the connection between emotion recognition, specifically using the Reading the Mind in the Eyes Test (RMET), and Heart Rate Variability (HRV). The study analyzes how the Autonomic Nervous System (ANS) influences HRV during emotion recognition tasks. Researchers examine various HRV indices, including meanRR, std, RMSSD, SDSD, NN50, pNN50, mean(bpm), R1/R2, SD1, and SD2 to understand the physiological responses when recognizing emotions from facial cues.

How does the Reading the Mind in the Eyes Test (RMET) relate to the study of emotion recognition?

The Reading the Mind in the Eyes Test (RMET) is a key tool used in the study to evaluate participants' ability to recognize emotions. It involves identifying emotions from images of eyes. The researchers use the RMET to assess how test difficulty, individual performance, and psychological characteristics affect Heart Rate Variability (HRV). The RMET provides a standardized method to measure emotion recognition abilities, which can then be correlated with physiological data like HRV.

What specific physiological measures are used to study the link between emotion recognition and the body's response, and what do they indicate?

The study uses several physiological measures to examine the body's response during emotion recognition. These include meanRR (average length between heartbeats), std (variation in heartbeat intervals), RMSSD and SDSD (short-term HRV), NN50 and pNN50 (indicators of vagal activity), mean(bpm) (average heart rate), R1/R2 (sample asymmetry), SD1 (short-term variability), and SD2 (long-term variability). These measures collectively provide a detailed view of the Autonomic Nervous System (ANS) activity and how it changes during the process of recognizing emotions, allowing researchers to understand the physiological signatures of social interactions.

In practical terms, why is understanding the connection between emotion recognition and physiological responses important?

Understanding the link between emotion recognition and physiological responses has significant implications for mental health and social connection. By identifying the physiological signatures of emotion recognition, researchers can develop new tools for assessing and improving social skills. This could be particularly beneficial for individuals with conditions like autism or social anxiety. Further research can also improve our understanding of how we process emotions, enhancing relationships and overall well-being.

How does Heart Rate Variability (HRV) play a role in understanding emotion recognition and the body's reactions?

Heart Rate Variability (HRV) is central to understanding emotion recognition as it reflects the influence of the Autonomic Nervous System (ANS) on the body. The study uses HRV indices (like meanRR, std, RMSSD, etc.) to observe how the body reacts during emotion recognition tasks, specifically when using the Reading the Mind in the Eyes Test (RMET). HRV measurements help reveal the physiological responses, providing insights into how different factors, such as test difficulty and an individual's emotional state, influence the body's reaction when we understand emotions.