Subjects: Psychology >> Social Psychology submitted time 2023-02-03
Abstract：
As a kind of affect state that individuals ideally want to experience, ideal affect is closely associated with culture. While people from individualistic culture prefer high arousal positive affect (i.e., enthusiastic, excited, elated), people from collectivistic culture prefer low arousal positive affect (i.e., calm, relaxed, peaceful). Society and culture, however, are not static. How would ideal affects shift along with massive sociocultural change? For the first time, we addressed this issue by examining the change of ideal affects in China, a collectivistic nation that has experienced huge social transformation and witnessed a rise in individualism in recent decades. In doing this, we focused on three main kinds of widely studied ideal affects: high arousal positive affects (HAP), low arousal positive affects (LAP) and positive affects (P; i.e., happy, satisfied, content). We conducted three studies, using cross-time comparison, cross-generational comparison and cross-regional comparison in each of the three studies, respectively.
In Study 1, a total of 84 participants who were born before 1966 and have experienced the whole process of Chinese reform and opening-up were recruited. They were asked to assess the extent to which Chinese people prefer each of 9 affections as listed above at beginning of 1980, 2000, 2020. Results showed that the preferences for HAP, LAP and P have been rising among Chinese since 1980.
In Study 2, a total of 1561 college students were asked to assess the extent to which people from each of the three generations (i.e., their grandparents generation, their parents generation and their own generation) prefer the 9 affects. Results showed that the youngest generation manifested higher preferences for HAP, LAP and P than old generations.
In Study 3, a large sample of college students from 31 provinces in China participated in the survey (N = 26209). They were asked to indicate the extent to which they prefer the 9 affects. Their cultural orientations of individualism and collectivism were also assessed as control variables. Results indicated that students from urban areas reported higher preference for HAP, LAP and P than those from rural areas after controlling their main demographic information and cultural orientations; moreover, HAP, LAP and P were positively correlated with each other at both individual and provincial levels.
Together, by using three different comparisons and assessing ideal affects from both inter-subjective (Study 1 and Study 2) and intra-subjective perspectives (Study 3), our three studies convergently showed that preferences for HAP, LAP and P have been rising in recent decades. The simultaneous rises of HAP and LAP as well as the positive correlation between them form a sharp contrast with the existing theoretical conceptualization and empirical findings about HAP and LAP, which suggest that HAP and LAP should be negatively correlated and manifest opposite shifting trends. Our findings, however, dovetail well with Chinese traditional culture of naïve dialecticism, according to which two seemingly contradictory opposites could coexist and even facilitate each other in some circumstances. Hence, theories originated from the West may not be applicable in China and novel theories may be needed.
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Subjects: Psychology >> Social Psychology Subjects: Psychology >> Cognitive Psychology Subjects: Psychology >> Psychological Measurement Subjects: Computer Science >> Natural Language Understanding and Machine Translation submitted time 2023-01-30
Abstract： As a basic technique in natural language processing (NLP), word embedding represents a word with a low-dimensional, dense, and continuous numeric vector (i.e., word vector). Word embeddings can be obtained by using neural network algorithms to predict words from the surrounding words or vice versa (Word2Vec and FastText) or words’ probability of co-occurrence (GloVe) in large-scale text corpora. In this case, the values of dimensions of a word vector denote the pattern of how a word can be predicted in a context, substantially connoting its semantic information. Therefore, word embeddings can be utilized for semantic analyses of text. In recent years, word embeddings have been rapidly employed to study human psychology, including human semantic processing, cognitive judgment, individual divergent thinking (creativity), group-level social cognition, sociocultural changes, and so forth. We have developed the R package “PsychWordVec” to help researchers utilize and analyze word embeddings in a tidy approach. Future research using word embeddings should (1) distinguish between implicit and explicit components of social cognition, (2) train fine-grained word vectors in terms of time and region to facilitate cross-temporal and cross-cultural research, and (3) deepen and expand the application of contextualized word embeddings and large pre-trained language models such as GPT and BERT.
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