Bodily sensation mapping (BSM) is a recently developed self-report tool for the assessment of emotions in which people draw their sensations of activation in a body silhouette. Following the circumplex model of affect, activity and valence are the underling dimensions of every emotional experience. The aim of this study was to introduce the neglected valence dimension in BSM. We found that participants systematically report valence-related sensations of bodily lightness for positive emotions (happiness, love, pride), and sensations of bodily heaviness in response to negative emotions (e.g., anger, fear, sadness, depression) with specific body topography (Experiment 1). Further experiments showed that both computers (using a machine learning approach) and humans recognize emotions better when classification is based on the combined activity- and valence-related BSMs compared to either type of BSM alone (Experiments 2 and 3), suggesting that both types of bodily sensations reflect distinct parts of emotion knowledge. Importantly, participants found it clearer to indicate their bodily sensations induced by sadness and depression in terms of bodily weight than bodily activity (Experiment 2 and 4), suggesting that the added value of valence-related BSMs is particularly relevant for the assessment of emotions at the negative end of the valence spectrum.

身体感觉映射 (BSM) 是最近开发的一种自我报告工具，用于评估人们在身体轮廓中绘制激活感觉的情绪。遵循情感、活动和效价的环形模型是每一种情感体验的基础维度。本研究的目的是介绍 BSM 中被忽略的价态维度。我们发现参与者系统地报告了积极情绪（快乐、爱、自豪）的身体轻盈的效价相关感觉，以及对具有特定身体形态的消极情绪（例如，愤怒、恐惧、悲伤、抑郁）的身体沉重感。实验1）。进一步的实验表明，与单独使用任何一种 BSM 相比（实验 2 和 3），当分类基于与活动和效价相关的组合 BSM 时，计算机（使用机器学习方法）和人类都能更好地识别情绪，这表明这两种类型身体感觉反映了情绪知识的不同部分。重要的，