Abstract
Emotions are expressed by physical expressions such as body posture. Physical cues play a crucial role in recognizing emotional states. We hypothesized that bodily expressions are stored in long-term memory in association with emotion and that such memory representation, knowledge of emotion, enables us to recognize mental states as a certain emotion. The present study focused on sadness as the target emotion and aimed to clarify how bodily expressions are associated with sadness. We decomposed bodily expressions into body-trunk and hand-arm postures and created body-expression photographs by combining these bodily postures. The 44 participants assessed 16 body-expression photographs to evaluate the extent to which they expressed four major emotions (sadness, anger, fear, and happiness), sadness-related body-expression properties (e.g., duration of physical expression), and social situations (e.g., loss of loved one). Sadness was more associated with the two types of body-trunk postures (deep and shallow forward-bent) and the two types of hand-arm postures (overall-face and around-eye). We subsequently classified the bodily expressions based on three kinds of assessment and specified three main groups associated with sadness. Each sadness-related body-expression group was differently associated with body-expression properties and sadness-related situations; for instance, one sadness-related body-expression group was assessed as an activated body-expression property with short-term duration and was associated with failure situations. These findings suggest that nonverbal bodily expressions play a key part in memory representations of sadness in association with body-expression properties and social situations.
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Acknowledgement
We gratefully acknowledge the work of participants. We are also grateful to the reviewers for their valuable comments. We would like to thank Rika Yanase for her support and Editage for English language editing. This work was financially supported by JSPS KAKENHI Grant Number JP 20K14143. The first author (MS) recently moved to Division of Psychology, Faculty of Arts, Shinshu University, Matsumoto, Nagano, Japan. The second author (TS) now belongs to National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan.
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This work was financially supported by JSPS KAKENHI Grant Number JP 20K14143.
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MS and TS designed the experiment. MS performed the survey. MS and TS analyzed data. MS wrote the manuscript in consultation with TS. All authors discussed the results and contributed to the final manuscript.
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Appendices
Appendix 1
Inclination change of the body-trunk postures at the four levels in the sagittal plane.
Appendix 2
Network Analysis to Cluster the Body Expressions
To conduct a network graph analysis, rating scores for each measure were first averaged across the participants, yielding the summary data matrix [16 stimuli \(\times\) 15 evaluation items (four emotions, five body-expression properties, and six situations]. Next, Pearson’s correlation coefficients were calculated between 120 pairs of the body-expression stimuli (16 stimuli \(\times\) 16 stimuli – 16 stimuli)/2). More positive coefficients indicate more adjacency between items. Based on the FWE-corrected α level p < 0.05/120, the significant positive coefficient was designated as “1” for existence of an edge which connects between body expressions. No significant positive coefficients and all negative coefficients were designated as “0”, which means non-edge between body expressions. This transformation created a symmetrical adjacency matrix (16 stimuli \(\times\) 16 stimuli) represented by 1 or 0, which means an unweighted property (i.e., existence or nonexistence of connection between body expression nodes) and non-directionality between connected body-expression nodes. We subsequently conducted a network analysis for this adjacency matrix to depict edges between the 16 body-expression nodes to produce a network structure.
There are various methods for grouping nodes. For example, a hierarchical clustering analysis classifies items into clusters by locally, successively pairing most similar items. Numbers of clusters are determined based on an arbitrary distance between clusters and therefore, were not unambiguously fixed. On the other hand, this study used a community-detection analysis using the information of connection of edges between body expressions. The modularity is a quality index for determining a partition of a network into communities. The optimized modularity method adopts the boundary of communities that maximizes a modularity index (range from 0 to 1), which is calculated by subtraction of index values of random connections within communities from those of observed connections. This index unambiguously determines numbers of communities, once edge information is obtained based on the threshold of path. Typically, modularity indexes range from 0.3 and 0.7 (Newman and Girvan 2004).
Appendix 3
The results for the follow-up two-way repeated measures ANOVAs for happiness, anger, and fear ratings.
Emotion | Body-trunk posture | Hand-arm posture | Interaction | Post-hoc comparison (Bonferroni) | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
F(3, 129) | ε | ηp2 | F(3, 129) | ε | ηp2 | F(9, 387) | ε | ηp2 | Body-trunk posture | Hand-arm posture | |
Happiness | 27.44* | 0.74 | 0.39 | 16.21* | 0.81 | 0.27 | 1.46 | – | 0.03 | Backward > Neutral > Deep, Shallow | Face > Neutral, Head, Eye |
Anger | 3.29* | – | 0.07 | 13.46* | 0.77 | 0.24 | 3.40* | 0.73 | 0.07 | Backward > Neutral | Head, Face, Neutral > Eye Head > Face |
Fear | 15.89* | – | 0.27 | 54.82* | 0.78 | 0.56 | 1.81 | – | 0.04 | Deep, Shallow > Neutral | Head > Face > Neutral, Eye |
Appendix 4
Supplementary analysis for the emotion effect in the body-trunk and hand-arm postures.
The additional follow up one-way repeated measures ANOVAs were conducted for each body-trunk posture with emotion as the independent variable. The main effects of emotion were significant for all postures [backward: F(3, 129) = 25.41, ε = 0.79, ηp2 = 0.37; neutral: F(3, 129) = 53.87, ηp2 = 0.56; shallow: F(3, 129) = 139.88, ηp2 = 0.76; deep: F(3, 129) = 131.51, ηp2 = 0.75, all ps < 0.05]. Post-hoc multiple comparisons revealed that sadness was higher than happiness, anger, and fear in all postures (see the other results in Table 8 ).
Similar analyses were conducted for each hand-arm posture with the emotion as the independent variable. The main effects of similar analyses were conducted for each hand emotion were significant for all hand-arm postures [neutral: F(3, 129) = 7.15, ε = 0.91, ηp2 = 0.14; around-eye: F(3, 129) = 141.88, ε = 0.87, ηp2 = 0.77; overall-face: F(3, 129) = 89.94, ε = 0.84, ηp2 = 0.68; head: F(3, 129) = 71.75, ηp2 = 0.63, all ps < 0.05]. Post-hoc multiple comparisons indicated that sadness ratings were higher than the other emotions for the overall-face and around-eye hand-arm posture. On the other hand, no significant difference between sadness and fear was observed for the head posture (see the other results in Table 9). These combined findings indicate that the four types of body postures (body-trunk posture: deep and shallow forward-bent; hand-arm posture: overall-face and around-eye) are highly associated with sadness in between-emotion comparisons.
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Shirai, M., Soshi, T. The Role of Bodily Expression in Memory Representations of Sadness. J Nonverbal Behav 45, 367–387 (2021). https://doi.org/10.1007/s10919-021-00360-8
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DOI: https://doi.org/10.1007/s10919-021-00360-8