Happiness feels light, sadness feels heavy: introducing valence-related bodily sensation maps of emotions

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.

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Maps of subjective feelings

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1807390115 ◽

2018 ◽

Vol 115 (37) ◽

pp. 9198-9203 ◽

Cited By ~ 40

Author(s):

Lauri Nummenmaa ◽

Riitta Hari ◽

Jari K. Hietanen ◽

Enrico Glerean

Keyword(s):

Positive Emotions ◽

Human Life ◽

Meta Analysis ◽

Emotional Valence ◽

Nonlinear Dimensionality Reduction ◽

Central Feature ◽

Feeling States ◽

Mental Experience ◽

Bodily Sensation ◽

Bodily Sensations

Subjective feelings are a central feature of human life. We defined the organization and determinants of a feeling space involving 100 core feelings that ranged from cognitive and affective processes to somatic sensations and common illnesses. The feeling space was determined by a combination of basic dimension rating, similarity mapping, bodily sensation mapping, and neuroimaging meta-analysis. A total of 1,026 participants took part in online surveys where we assessed (i) for each feeling, the intensity of four hypothesized basic dimensions (mental experience, bodily sensation, emotion, and controllability), (ii) subjectively experienced similarity of the 100 feelings, and (iii) topography of bodily sensations associated with each feeling. Neural similarity between a subset of the feeling states was derived from the NeuroSynth meta-analysis database based on the data from 9,821 brain-imaging studies. All feelings were emotionally valenced and the saliency of bodily sensations correlated with the saliency of mental experiences associated with each feeling. Nonlinear dimensionality reduction revealed five feeling clusters: positive emotions, negative emotions, cognitive processes, somatic states and illnesses, and homeostatic states. Organization of the feeling space was best explained by basic dimensions of emotional valence, mental experiences, and bodily sensations. Subjectively felt similarity of feelings was associated with basic feeling dimensions and the topography of the corresponding bodily sensations. These findings reveal a map of subjective feelings that are categorical, emotional, and embodied.

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The OpenLAV video database for affect induction: Analyzing the uniformity of video stimuli effects

10.31234/osf.io/vhmbq ◽

2021 ◽

Author(s):

Laura Israel ◽

Philipp Paukner ◽

Lena Schiestel ◽

Klaus Diepold ◽

Felix D. Schönbrodt

Keyword(s):

Personality Traits ◽

Predictive Power ◽

Emotional Experience ◽

Learning Approach ◽

Video Database ◽

Emotion Induction ◽

Online Study ◽

Machine Learning Approach ◽

Emotion Labels ◽

Big 5 Personality

The Open Library for Affective Videos (OpenLAV) is a new video database for experimental emotion induction. The 188 videos (mean duration: 40 s; range: 12–71 s) have a CC-BY license. Ratings for valence, arousal, several appraisals, and emotion labels were assessed from 434 US-American participants in an online study (on average 70 ratings per video), along with personality traits from the raters (Big 5 personality dimensions and several motive dispositions). The OpenLAV is able to induce a large variety of different emotions, but the videos vary in uniformity of emotion induction. Based on different variability metrics, we recommend videos for the most uniform induction of different emotions. Moreover, the predictive power of personality traits on emotion ratings was analyzed using a machine-learning approach. In contrast to previous research, no effects of personality on the emotional experience were found.

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Predicting the effect of entrepreneurial stressors and resultant strain on entrepreneurial behaviour: an SEM-based machine-learning approach

International Journal of Entrepreneurial Behaviour & Research ◽

10.1108/ijebr-08-2020-0529 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Tahseen Anwer Arshi ◽

Sardar Islam ◽

Nirmal Gunupudi

Keyword(s):

Machine Learning ◽

United Arab Emirates ◽

Psychological Capital ◽

Causal Effect ◽

Self Report ◽

Learning Approach ◽

Content Type ◽

Entrepreneurial Behaviour ◽

Machine Learning Approach ◽

The Impact

PurposeConsiderable evidence suggests that although they overlap, entrepreneurial and employee stressors have different causal antecedents and outcomes. However, limited empirical data explain how entrepreneurial traits, work and life drive entrepreneurial stressors and create entrepreneurial strain (commonly called entrepreneurial stress). Drawing on the challenge-hindrance framework (CHF), this paper hypothesises the causal effect of hindrance stressors on entrepreneurial strain. Furthermore, the study posits that entrepreneurial stressors and the resultant strain affect entrepreneurial behaviour.Design/methodology/approachThe study adopts an SEM-based machine-learning approach. Cross-lagged path models using SEM are used to analyse the data and train the machine-learning algorithm for cross-validation and generalisation. The sample consists of 415 entrepreneurs from three countries: India, Oman and United Arab Emirates. The entrepreneurs completed two self-report surveys over 12 months.FindingsThe results show that hindrances to personal and professional goal achievement, demand-capability gap and contradictions between aspiration and reality, primarily due to unique resource constraints, characterise entrepreneurial stressors leading to entrepreneurial strain. The study further asserts that entrepreneurial strain is a significant predictor of entrepreneurial behaviour, significantly affecting innovativeness behaviour. Finally, the finding suggests that psychological capital moderates the adverse impact of stressors on entrepreneurial strain over time.Originality/valueThis study contributes to the CHF by demonstrating the value of hindrance stressors in studying entrepreneurial strain and providing new insights into entrepreneurial coping. It argues that entrepreneurs cope effectively against hindrance stressors by utilising psychological capital. Furthermore, the study provides more evidence about the causal, reversed and reciprocal relationships between stressors and entrepreneurial strain through a cross-lagged analysis. This study is one of the first to evaluate the impact of entrepreneurial strain on entrepreneurial behaviour. Using a machine-learning approach is a new possibility for using machine learning for SEM and entrepreneurial strain.

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