Subjects: Psychology >> Social Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： As a fundamental technique in natural language processing (NLP), word embedding quantifies a word as a low-dimensional, dense, and continuous numeric vector (i.e., word vector). This process is based on machine learning algorithms such as neural networks, through which semantic features of a word can be extracted automatically. There are two types of word embeddings: static and dynamic. Static word embeddings aggregate all contextual information of a word in an entire corpus into a fixed vectorized representation. The static word embeddings can be obtained by predicting the surrounding words given a word or vice versa (Word 2Vec and FastText) or by predicting the probability of co-occurrence of multiple words (GloVe) in large-scale text corpora. Dynamic or contextualized word embeddings, in contrast, derive a word vector based on a specific context, which can be generated through pre-trained language models such as ELMo, GPT, and BERT. Theoretically, the dimensions of a word vector reflect the pattern of how the word can be predicted in contexts; however, they also connote substantial semantic information of the word. Therefore, word embeddings can be used to analyze semantic meanings of text. In recent years, word embeddings have been increasingly applied to study human psychology. In doing this, word embeddings have been used in various ways, including the raw vectors of word embeddings, vector sums or differences, absolute or relative semantic similarity and distance. So far, the Word Embedding Association Test (WEAT) has received the most attention. Based on word embeddings, psychologists have explored a wide range of topics, including human semantic processing, cognitive judgment, divergent thinking, social biases and stereotypes, and sociocultural changes at the societal or population level. Particularly, the WEAT has been widely used to investigate attitudes, stereotypes, social biases, the relationship between culture and psychology, as well as their origin, development, and cross-temporal changes. As a novel methodology, word embeddings offer several unique advantages over traditional approaches in psychology, including lower research costs, higher sample representativeness, stronger objectivity of analysis, and more replicable results. Nonetheless, word embeddings also have limitations, such as their inability to capture deeper psychological processes, limited generalizability of conclusions, and dubious reliability and validity. Future research using word embeddings should address these limitations by (1) distinguishing between implicit and explicit components of social cognition, (2) training fine-grained word vectors in terms of time and region to facilitate cross-temporal and cross-cultural research, and (3) applying contextualized word embeddings and large pre-trained language models such as GPT and BERT. To enhance the application of word embeddings in psychological research, we have developed the R package “PsychWordVec”, an integrated word embedding toolkit for researchers to study human psychology in natural language.
Subjects: Psychology >> Social Psychology submitted time 2023-03-27 Cooperative journals: 《心理学报》
Abstract： The purpose of this research is to examine the change of ideal affect of Chinese people since 1980s, in particular, high arousal positive affects (HAP), low arousal positive affects (LAP) and positive affects (P). By employing diverse methods, three studies were conducted. In Study 1, a total of 84 participants born before 1966 were asked to assess the ideal affect of Chinese people at beginnings of 1980, 2000, 2020. Results showed that the preferences for HAP, LAP and P have been rising among Chinese people since 1980. In Study 2, a total of 1561 college students were asked to assess the ideal affect of people of three generations: their grandparents generation, their parents generation and their own generation. 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 were surveyed (N =26209). Results indicated that students from urban areas manifest higher preference for HAP, LAP and P than those from rural areas after controlling basic demographic information and cultural orientations; moreover, HAP, LAP and P were positively correlated with each other. Taken together, findings from three studies convergently suggest that preferences for HAP, LAP and P have been rising since 1980, with modernization as a potential driver.
submitted time 2023-03-25 Cooperative journals: 《心理科学进展》
Abstract： As a fundamental technique in natural language processing (NLP), word embedding quantifies a word as a low-dimensional, dense, and continuous numeric vector (i.e., word vector). This process is based on machine learning algorithms such as neural networks, through which semantic features of a word can be extracted automatically. There are two types of word embeddings: static and dynamic. Static word embeddings aggregate all contextual information of a word in an entire corpus into a fixed vectorized representation. The static word embeddings can be obtained by predicting the surrounding words given a word or vice versa (Word 2Vec and FastText) or by predicting the probability of co-occurrence of multiple words (GloVe) in large-scale text corpora. Dynamic or contextualized word embeddings, in contrast, derive a word vector based on a specific context, which can be generated through pre-trained language models such as ELMo, GPT, and BERT. Theoretically, the dimensions of a word vector reflect the pattern of how the word can be predicted in contexts; however, they also connote substantial semantic information of the word. Therefore, word embeddings can be used to analyze semantic meanings of text. In recent years, word embeddings have been increasingly applied to study human psychology. In doing this, word embeddings have been used in various ways, including the raw vectors of word embeddings, vector sums or differences, absolute or relative semantic similarity and distance. So far, the Word Embedding Association Test (WEAT) has received the most attention. Based on word embeddings, psychologists have explored a wide range of topics, including human semantic processing, cognitive judgment, divergent thinking, social biases and stereotypes, and sociocultural changes at the societal or population level. Particularly, the WEAT has been widely used to investigate attitudes, stereotypes, social biases, the relationship between culture and psychology, as well as their origin, development, and cross-temporal changes. As a novel methodology, word embeddings offer several unique advantages over traditional approaches in psychology, including lower research costs, higher sample representativeness, stronger objectivity of analysis, and more replicable results. Nonetheless, word embeddings also have limitations, such as their inability to capture deeper psychological processes, limited generalizability of conclusions, and dubious reliability and validity. Future research using word embeddings should address these limitations by (1) distinguishing between implicit and explicit components of social cognition, (2) training fine-grained word vectors in terms of time and region to facilitate cross-temporal and cross-cultural research, and (3) applying contextualized word embeddings and large pre-trained language models such as GPT and BERT. To enhance the application of word embeddings in psychological research, we have developed the R package “PsychWordVec”, an integrated word embedding toolkit for researchers to study human psychology in natural language.
submitted time 2023-03-16 Cooperative journals: 《心理学报》
Abstract： The purpose of this research is to examine the change of ideal affect of Chinese people since 1980s, in particular, high arousal positive affects (HAP), low arousal positive affects (LAP) and positive affects (P). By employing diverse methods, three studies were conducted. In Study 1, a total of 84 participants born before 1966 were asked to assess the ideal affect of Chinese people at beginnings of 1980, 2000, 2020. Results showed that the preferences for HAP, LAP and P have been rising among Chinese people since 1980. In Study 2, a total of 1561 college students were asked to assess the ideal affect of people of three generations: their grandparents generation, their parents generation and their own generation. 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 were surveyed (N =26209). Results indicated that students from urban areas manifest higher preference for HAP, LAP and P than those from rural areas after controlling basic demographic information and cultural orientations; moreover, HAP, LAP and P were positively correlated with each other. Taken together, findings from three studies convergently suggest that preferences for HAP, LAP and P have been rising since 1980, with modernization as a potential driver.
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