Examining Affective Valence in Japanese and Brazilian Cultural Products: An Analysis on Emotional Words in Song Lyrics and News Articles

et al. 2022

Perspect Psychol Sci

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Cultural psychologists often treat binary contrasts of West versus East, individualism versus collectivism, and independent versus interdependent self-construal as interchangeable, thus assuming that collectivist societies promote interdependent rather than independent models of selfhood. At odds with this assumption, existing data indicate that Latin American societies emphasize collectivist values at least as strongly as Confucian East Asian societies, but they emphasize most forms of independent self-construal at least as strongly as Western societies. We argue that these seemingly “anomalous” findings can be explained by societal differences in modes of subsistence (herding vs. rice farming), colonial histories (frontier settlement), cultural heterogeneity, religious heritage, and societal organization (relational mobility, loose norms, honor logic) and that they cohere with other indices of contemporary psychological culture. We conclude that the common view linking collectivist values with interdependent self-construal needs revision. Global cultures are diverse, and researchers should pay more attention to societies beyond “the West” and East Asia. Our contribution concurrently illustrates the value of learning from unexpected results and the crucial importance of exploratory research in psychological science.

Section: Making Sense Of the Findings: Latin America Is Not Confucian...mentioning

confidence: 99%

Outside the “Cultural Binary”: Understanding Why Latin American Collectivist Societies Foster Independent Selves

Kryś

Vignoles

et al. 2022

Perspect Psychol Sci

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“…A recent study's result has shown Colombians, European-Americans, and Japanese scoring significantly higher on ideal LAP than HAP, and Japanese scoring significantly lower than the other groups in HAP (Salvador et al, 2020). While questionnairebased studies with participants sometimes show this pattern, behavioral and cultural products-based studies have consistently shown affective cultural values (e.g., Tsai et al, 2006;Tsai, 2007;De Almeida and Uchida, 2019). Therefore, there is the possibility that in the present study, the participants' ideal affect was not a perfect reflection of what is expected in terms of affect in the Brazilian culture.…”

Section: Discussionmentioning

confidence: 99%

“…The function of emotions in each of these cultures as well as how they are valued varies (e.g., Ruby et al, 2012 ; De Almeida and Uchida, 2019 ). Therefore, we expected that each culture would display a specific relationship between social marginalization and affect.…”

Section: Introductionmentioning

confidence: 99%

“…This cultural difference is also reflected in cultural products such as song lyrics and news articles. A study comparing cultural products from Brazil and Japan showed that Brazilian products have more positive words, which can be related to HAP, than neutral and negative words, while Japanese products tend to have proportionally more neutral words, which can be related to LAP, than positive and negative words ( De Almeida and Uchida, 2019 ). Hence, the current paper extends past literature by providing a new way of distinguishing two interdependent cultures, by comparing these cultures using ideal affect valuation.…”

Section: Introductionmentioning

confidence: 99%

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Who Can Buffer Marginalization Risk? Affect Experience, Affect Valuation, and Social Marginalization in Japan and Brazil

Uchida

2021

Front. Psychol.

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Previous research has associated social marginalization with the rejection of mainstream cultural values. Since cultural values reflect affect valuation, the present research investigates the relationships between social marginalization and ideal/actual affect in two different non-WEIRD cultures, Brazil and Japan. As a social marginalization index, we used the NEET-Hikikomori Risk Scale (NHR). We predicted that cultural differences would emerge in the valuation of affective states. Affect valuation theory suggests that in East Asia, individuals are encouraged to pursue and value low arousal positive emotions (LAP: e.g., calmness, serenity) over high arousal positive emotions (HAP: e.g., excitement, elation, etc.) as they can harm social relationships in these societies. In contrast, Latin American cultures value HAP over LAP, because social relationships are promoted through vibrant positive emotional expression in these cultures. Hence, we hypothesized that individuals’ ideal affect, actual affect, and the discrepancy between ideal and actual affect would be associated with higher risk of social marginalization. Participants from Japan (N = 54) and Brazil (N = 54) reported their ideal affect and actual affect and completed the NEET-Hikikomori Risk Scale (NHR). Regression analyses showed that actual HAP and the discrepancy between ideal and actual HAP were negatively associated with NHR in Brazil, but no association was found in the Japanese data. The other variables, including ideal affect, were only minorly or not significantly associated with NHR. Though the study has limitations regarding its small sample size, we can explore future perspectives and discuss the relationships between emotion and cultural marginalization. Socioecological factors that promote actual HAP in Brazilians may encourage other mainstream cultural ideals, which buffers against cultural marginalization.

“…This implies that music consumption behaviour underscores culturally based attitudes, cognitions and emotions that afford preferences for certain congruent types of music. For example, in a cross-cultural comparison between Brazil and Japan, De Almeida & Uchida (2018) found that Brazilian song lyrics contained higher frequencies of positive emotion words and lower frequencies of neutral words than Japanese lyrics. This was consistent and reflective of their respective cultural emphases on emotion expressions (see Triandis et al, 1984 ; Uchida & Kitayama, 2009 ), and showed that comparing music ‘products’ elucidated differences between the collective shared values of different cultures.…”

Section: Introductionmentioning

confidence: 99%

Cultural differences in music features across Taiwanese, Japanese and American markets

Liew

Uchida

2021

PeerJ Computer Science

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Background Preferences for music can be represented through music features. The widespread prevalence of music streaming has allowed for music feature information to be consolidated by service providers like Spotify. In this paper, we demonstrate that machine learning classification on cultural market membership (Taiwanese, Japanese, American) by music features reveals variations in popular music across these markets. Methods We present an exploratory analysis of 1.08 million songs centred on Taiwanese, Japanese and American markets. We use both multiclass classification models (Gradient Boosted Decision Trees (GBDT) and Multilayer Perceptron (MLP)), and binary classification models, and interpret their results using variable importance measures and Partial Dependence Plots. To ensure the reliability of our interpretations, we conducted a follow-up study comparing Top-50 playlists from Taiwan, Japan, and the US on identified variables of importance. Results The multiclass models achieved moderate classification accuracy (GBDT = 0.69, MLP = 0.66). Accuracy scores for binary classification models ranged between 0.71 to 0.81. Model interpretation revealed music features of greatest importance: Overall, popular music in Taiwan was characterised by high acousticness, American music was characterised by high speechiness, and Japanese music was characterised by high energy features. A follow-up study using Top-50 charts found similarly significant differences between cultures for these three features. Conclusion We demonstrate that machine learning can reveal both the magnitude of differences in music preference across Taiwanese, Japanese, and American markets, and where these preferences are different. While this paper is limited to Spotify data, it underscores the potential contribution of machine learning in exploratory approaches to research on cultural differences.