• Science of Emotion and Sensibility
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• v.26 no.1
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• pp.33-42
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• 2023

Previous emotion studies employing facial expressions have focused on the differences between age groups for each of the emotion categories. Instead, Kim (2021) has compared representations of facial expressions in the lower-dimensional emotion space. However, he reported descriptive comparisons without statistical significance testing. This research used representational similarity analysis (Kriegeskorte et al., 2008) to directly compare empirical datasets from young, middle-aged, and old groups and conceptual models. In addition, individual differences multidimensional scaling (Carroll & Chang, 1970) was conducted to explore individual weights on the emotional dimensions for each age group. The results revealed that the old group was the least similar to the other age groups in the empirical datasets and the valence model. In addition, the arousal dimension was the least weighted for the old group compared to the other groups. This study directly tested the differences between the three age groups in terms of empirical datasets, conceptual models, and weights on the emotion dimensions.

• Science of Emotion and Sensibility
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• v.26 no.2
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• pp.25-38
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• 2023

Among the discussions on affective representation, the first is to explain the affective representation in the dimensions, and the second is to explain the affective representation according to the modality. In previous studies, to explain affective representation, valence models (signed valence, unsigned valence) and Modality-generality models (modality-general, modality-specific) were presented. In this study, we compared models presented in the previous study using the recently published ASMR to confirm which models explain affective representation well. The data used in this study were behavioral rating values collected by Kim & Kim (2022), and these were obtained for ASMR stimuli that were divided into three affective types (negative, neutral, and positive) and two modalities (auditory and audiovisual). Then, a multidimensional scaling, a representational similarity analysis with a two-way repeated measures ANOVA, and a multiple regression analysis with a two-way repeated measures ANOVA were performed. The results revealed that signed valence and modality-general distinguished between affective types of stimuli better than unsigned valence and modality-specific. Similar to the results of multidimensional scaling, the results of a representational similarity analysis and a multiple regression also showed that the signed valence and modality-general significantly explained affective representation better than the unsigned valence and the modality-specific. These results suggest that the model in which positive and negative are located at the opposite ends of the one dimension explains the affective representation of ASMR well, and that the affective representation was consistent regardless of modality.

• Science of Emotion and Sensibility
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• v.26 no.3
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• pp.3-14
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• 2023

This study used intersubject representational similarity analysis (IS-RSA) to identify participant-response consistency patterns in previously published data. Additionally, analysis of variance (ANOVA) was utilized to detect any variations in the conditions of each experiment. In each experiment, a combination of ASMR stimulation, visual and auditory stimuli, and time-series emotional video stimulation was employed, and emotional ratings and physiological measurements were collected in accordance with the respective experimental conditions. Every pair of participants' measurements for each stimulus in each experiment was correlated using Pearson correlation coefficient as part of the IS-RSA. The results of study revealed a consistent response pattern among participants exposed to ASMR, visual, and auditory stimuli, in contrast to those exposed to time-series emotional video stimulation. Notably, the ASMR experiment demonstrated a high level of response consistency among participants in positive conditions. Furthermore, both auditory and visual experiments exhibited remarkable consistency in participants' responses, especially when subjected to high arousal levels and visual stimulation. The findings of this study confirm that IS-RSA serves as a valuable tool for summarizing and presenting multidimensional data information. Within the scope of this study, IS-RSA emerged as a reliable method for analyzing multidimensional data, effectively capturing and presenting comprehensive information pertaining to the participants.

• Science of Emotion and Sensibility
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• v.26 no.4
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• pp.3-14
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• 2023

Recently, the effect of using social media on psychological well-being has been highlighted. However, studies exploring factors that may predict the quality of social media relationships are relatively rare. The present study investigated whether social media activity and psychological states, such as loneliness and depression, can predict the quality of social media relationships during the COVID-19 quarantine period using a machine learning technique. Ninety-five participants completed a self-report survey on loneliness, Instagram activity, quality of social media relationships, and depression at different time points (during the self-isolation and after the release of self-isolation). Similarity analyses, including multidimensional scaling (MDS), representational similarity analysis (RSA), and classification analyses, were conducted separately at each point in time. The results of MDS revealed that time spent on social media and depression were distinguished from others in the first dimension, and loneliness and passive use were distinguished from others in the second dimension. We divided the data into two groups based on the quality of social media relationships (high and low), and we conducted RSA on each group. Findings indicated an interaction between the quality of the social media relationships and the situation. Specifically, the effect of self-isolation on the high-quality social media relationship group is more pronounced than that on the low-quality group. The classification results also revealed that the predictors of social media relationships depend on whether or not they are isolated. Overall, the results of this study imply that social media relationship could be well predicted when people are not in isolated situations.

• Journal of the Economic Geographical Society of Korea
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• v.11 no.4
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• pp.564-579
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• 2008

It is often challenging to evaluate local spatial association among onedimensional vectors generally representing paired-location events where two points are physically or functionally connected. This is largely because of complex process of such geographic phenomena itself and partially representational complexity. This paper addresses an alternative way to identify spatially autocorrelated paired-location events (or vectors) at a local scale. In doing so, we propose a statistical algorithm combining univariate point pattern analysis for evaluating local clustering of origin-points and similarity measure of corresponding vectors. For practical use of the suggested method, we present an empirical application using transactions data in a local housing market, particularly recorded from 2004 to 2006 in Franklin County, Ohio in the United States. As a result, several locally characterized similar transactions are identified among a set of vectors showing various local moves associated with communities defined.