Institute of Psychology, Chinese Academy of Sciences
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A Study on Recommendation Algorithms Using Graph Convolutional Neural Networks Based on Domain-Specific Proprietary Datasets
No full textTo address the current limitations of recommendation algorithms in the matchmaking social domain, which often rely on relatively rudimentary data and consequently struggle to achieve higher recommendation accuracy, this study investigates the use of a graph convolutional neural network (GCN) with residual connections for training a recommendation model. The objective is to enhance the predictive probability of successful male-female matches. Initially, we constructed the first dataset in this domain, based on expert knowledge in psychology, featuring a high-dimensional dataset derived from 44 psychological traits and real social relationships. Subsequently, we proposed a GCN model with residual connections based on this dataset and validated the model's predictive performance. Evaluation metrics, including the area under the curve (AUC) and accuracy on the validation set, demonstrated the effectiveness of the training results. The model's validation data indicated a strong performance in predicting male-female matches within social networks, with all positive samples being successfully predicted. By statistically analyzing the high-dimensional feature characteristics of paired nodes in the validation set and comparing them with existing research, we found that the statistical results were consistent with current studies. Therefore, this model holds significant practical value.</p
Effects of comorbid alexithymia on cognitive impairment in chronic schizophrenia: a large-sample study on the Han Chinese population
No full textBackground Alexithymia and cognitive dysfunction are common in patients with schizophrenia. However, only a few studies have investigated the cognitive performance of patients with schizophrenia and comorbid alexithymia. This study aimed to investigate the relationship between alexithymia and neurocognitive impairment in patients with schizophrenia. Methods A total of 695 patients who met the DSM-IV diagnostic criteria for schizophrenia were included in this cross-sectional study (male/female = 464/231). Demographic and clinical data were collected using self-reported questionnaires. The severity of alexithymia was assessed using the Toronto Alexithymia Scale (TAS-20), cognitive function was assessed using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) tool, and the severity of psychiatric symptoms was assessed using the Positive and Negative Syndrome Scale (PANSS). Results The prevalence of comorbid alexithymia in patients with chronic schizophrenia was 31.40%, with a male preponderance. Patients with alexithymia had higher PANSS negative symptom subscale scores and PANSS total scores than those without alexithymia (p < 0.05 for all). In addition, patients with alexithymia had more severe deficits in immediate memory, delayed memory, and language and lower RBANS scores than those without alexithymia. Stepwise multivariate regression analysis showed that alexithymia was a risk factor for language deficits and indicated low total RBANS scores in patients with schizophrenia. Conclusion This study suggests that patients with chronic schizophrenia with alexithymia have poorer cognitive function than those without alexithymia. Some demographic characteristics and alexithymia are risk factors for cognitive dysfunction in patients with chronic schizophrenia.</p
Behavioral model of motivation deficiency in rats and role of dopamine receptors in the nucleus accumbens
No full text目的 “躺平”作为现代流行词汇，描述部分人群的心理状态，但其心理学含义仍未明确。一定条件下的动机缺乏可能是关键心理特征之一。方法 将16只雄性SD大鼠随机分配到两种限食水平(分别为基线体重的90%和80%),并进行蔗糖颗粒自我给药任务，尝试建立“躺平”的动机缺乏动物行为模型，模型的建立分为3个阶段，第一阶段学习鼻触一次高回报侧获得一粒糖丸，第二阶段学习多次鼻触低回报侧获得一颗糖丸，第三阶段观察大鼠在高回报侧可以获得不同程度奖励的情况下，还是否愿意多付出努力在低回报侧获得糖丸。结果 结合自给食训练和免疫印迹，结果显示：(1)大鼠在第三阶段出现“躺平”现象，即在高回报侧获得最大奖励的90%及以上，且低回报侧努力程度增大到断点时，大鼠放弃努力获得更多糖丸奖励。(2)与“未躺平”组的大鼠相比，“躺平”组大鼠伏隔核内的多巴胺D1受体表达显著升高，而D2受体表达没有显著差异。结论 大鼠可表现出“躺平”样行为，伏隔核中D1受体表达上调可能是“躺平”动机缺乏的重要分子基础，该模型的建立扩展了对“躺平”的理解，并为其机制研究提供了新范式。</p
Does Stress Help or Harm? The Mediating Role of Cognitive Emotion Regulation Strategies in the Relationship Between Stress, Adolescent Academic Performance, and Depression
No full textIntroduction The Challenge and Hindrance Stress Framework is an influential theoretical model for measuring individuals' perceptions of stress. However, its structure has not been validated among Chinese adolescents, and the effects of different forms of stress on their short-term and long-term outcomes remain unclear. Methods Study 1 validated the Student Version Challenge and Hindrance Stress Scale with a sample of 3,376 adolescents in China (Time 1, September 2023, M-age = 14.57, SD = 1.46). Studies 2a and 2b extended Study 1 by analyzing cross-sectional and longitudinal data from 1,083 participants in China (Time 2, March 2024, M-age = 14.32, SD = 1.01) to examine the effects of various forms stress on academic performance and depression, with cognitive emotion regulation strategies used as mediators. Results The results showed that adaptive strategies mediated the positive effects of challenge stress on academic performance and depression, whereas maladaptive strategies mediated the negative impacts of challenge or hindrance stress on depression. Conclusion These findings emphasize the importance of distinguishing stress forms, offering insights for educators, researchers, and policymakers to enhance adolescent well-being and performance.</p
Cross-domain fault diagnosis of marine diesel engines based on stepwise diffusion and iterative bidirectional optimization
No full textCross-domain fault diagnosis of marine diesel engines presents significant challenges due to variations in data distribution and the limited availability of labeled fault samples under different operating conditions. To address this, an unsupervised domain-adaptive diagnostic framework is proposed, integrating stepwise diffusion and iterative bidirectional optimization to enhance fault identification. First, the quadratic axial attention transformer introduces a fourth weight in the axial computation to effectively capture the long-range spatio-temporal correlations in the time–frequency representations and strengthen the cross-axis contextual dependence. Next, the domain stepwise diffusion bridge utilizes Markov transform to gradually refine the significant distributional differences across domains into continuous sub-distributions, ensuring a smoother adaptation process. Finally, an iterative bidirectional optimization strategy is proposed to dynamically coordinate the interaction between stepwise diffusion and fault classification, where two complementary learning directions are alternately executed to preserve the semantic integrity of features. Experimental validation on a self-constructed dataset covering multiple operating conditions demonstrates the effectiveness of the proposed approach, achieving 93.80% average accuracy, 93.75% precision, and 93.45% recall. This approach not only breaks through the limitations of existing domain alignment methods and provides a brand new solution for cross-domain fault diagnosis, but also provides a wide range of implications for future research and applications in this field.</p
Logistic Regression and XGBoost Model of Multiple factors on Trust
No full textThe trust study was begun in the 1960s. Previous research has been particularly focused on understanding the psychological underpinnings of trust formation and sustenance, with influences from economic interests, social identity, and self-actualization. However, the majority of these studies relied on qualitative approaches, with limited research incorporating covariates and a lack of studies comparing the magnitudes of these variables' effects. To bridge this gap, we conducted an online survey research and collected 268 valid questionnaires. The dependent variable was generalized trust, while the independent variables included a set of personality traits and social preference measures (e.g., self-esteem, self-control, anxiety, cultural Tightness Looseness (TL), and Belief in a Just World (BJW)). Leveraging the advantages of machine learning, this study identified the key factors (i.e., BJW, TL) contributing to Trust. The exploration of these mechanisms has been crucial in advancing our understanding of how trust operates in different contexts and at different levels of human interaction.</p
Gendered Artificial Intelligence in Marketing: Behavioral and Neural Insights Into Product Recommendations
No full textMarketing research consistently demonstrates that gender stereotypes influence the effectiveness of product recommendations. When artificial intelligence (AI) agents are designed with gendered features to enhance anthropomorphism, a follow-up question is whether these agents' recommendations are also shaped by gender stereotypes. To investigate this, the current study employed a shopping task featuring product recommendations (utilitarian vs. hedonic), using both behavioral measures (purchase likelihood, personal interest, and tip amount) and event-related potential components (P1, N1, P2, N2, P3, and late positive potential) to capture explicit and implicit responses to products recommended by male and female humans, virtual assistants, or robots. The findings revealed that gender stereotypes influenced responses at both levels but in distinct ways. Behaviorally, participants consistently favored female recommenders across all conditions. Additionally, female recommenders received more tips than males for hedonic products in the virtual assistant condition and utilitarian products in the robot condition. Implicitly, the N1 and N2 components reflected a classic gender stereotype from prior research: utilitarian products recommended by male humans elicited greater attention and received more inhibition control. We propose that task design and cultural factors may have contributed to the observed discrepancies between explicit (consumer behaviors) and implicit responses. These findings provide insights for mitigating the impact of gender difference when designing the anthropomorphic appearance of AI agents, which would help the development of more effective marketing strategies.</p
Boy's love fans versus non-fans in the sexual identity and neural response in the digital age's young females
No full textWith the omnipresence of online social media, Boys' Love (BL) culture has found a burgeoning audience among young females. However, we know very little about the audience of this online cultural phenomena, also the potential implications of BL culture to female remain under-explored. Study 1 conducted a survey to investigate the BL audience's demography data and attitudes to homosexual ect. The results of the questionnaire analysis showed that the sexual orientation and psychological gender of the female BL audiences are more diverse. In addition, we also find the audience spend a lot of time on BL. Study 2 focused on the BL senior fans to explore the neural and behavioral response of female while looking at Boys' Love(BL) stimuli and Heterosexual love stimuli by fNIRS. Behavioral results showed that there was no main effect of reaction time and accuracy between the BL-fans and non-BL-fans. Neural results confirmed that the Oxy-Hb responses for BL-love stimuli in BL-fans was significantly lower than the non-BL-fans. In addition, the interaction effect showed that the Oxy-Hb responses was significantly higher for BL-love stimuli than for heterosexual love stimuli in non-BL-fans, and no difference was found in BL-fans. This finding, maybe along with the discovery that the more pornography a person was exposed to, the higher the brain dopamine threshold, and the subsequent weakening of the neural response to sexual stimulation. The research leads to the conclusion that long term exposed to Boys' Love may decrease the reward sensitivity to BL stimuli and weakens the brain's response of the right ventrolateral prefrontal cortex (rVLPFC) to BL stimuli
Similarities and differences in core symptoms of problematic smartphone use among Chinese students enrolled in grades 4 to 9: A large national cross-sectional study
No full textChildren and adolescents are highly susceptible to problematic smartphone usage. We employed network analysis to explore the similarities and differences in the core symptoms of problematic smartphone use across grades 4-9, using a large nationwide sample. This study included 8552 children and adolescents (Mage M age = 12.98, SD =1.51) who met the critical value for problematic smartphone use. The results showed that the core symptoms of problematic smartphone use exhibit both similarities and differences between grades 4 and 9. 'Withdrawal symptoms' and 'preoccupation symptoms' were the stable core symptoms of problematic smartphone use across grades 4 to 9, suggesting that problematic smartphone use begin to appear from earlier grades, such as grade 4. 'Feel impatient and fretful', 'never give up' and 'always thinking about' were the core symptoms in grades 4 and 5. 'Longer than I had intended' and 'hard to concentrate' emerged as additional core symptoms in grade 6, with the intensity indicators peaking in grades 8 and 9, suggesting that the issue of problematic smartphone use among Chinese children and adolescents has become intensified and intricate. Symptoms of problematic smartphone use vary across grades and exhibit both continuity and stage specificity. Consequently, to address this issue, the formulation of intervention measures should comprehensively consider both the grade levels and symptoms.</p
A deep learning method for contactless emotion recognition from ballistocardiogram
No full textEmotion recognition is a major research point in the field of affective computing. Existing research on the application of physiological signals to emotion recognition mainly focuses on the processing of contact signals. However, there are issues with contact signal acquisition equipment, such as limited portability and poor user compliance, which make it difficult to promote its use. To explore a new method for emotion recognition based on contactless ballistocardiogram (BCG), we proposed a SE-CNN model with a multi-class focal loss function. To construct the dataset, we used audio-visual stimuli to evoke the subjects' emotions and collected data on the subjects' three discrete emotions, positive, neutral, and negative, through our established BCG signal acquisition system based on a piezoelectric ceramics sensor. Root mean square filter and thresholding were used to detect and eliminate motion artifacts of BCG signals. We did two kinds of preprocessing on BCG signals: wavelet transform and bandpass filtering, to explore the effect of different components of BCG on emotion recognition. Subsequently, we verified the model's performance and cross-time working ability through traditional K-Fold and our proposed K-Session cross-validation methods. The results showed that the band-pass filtering method was more beneficial to the current classification task. Under K-Fold cross-validation, the model's accuracy, precision, and recall were 97.21%, 97.00%, and 97.11%. Under K-Session cross-validation, the model's accuracy, precision, and recall were 94.66%, 93.92%, and 94.86%, respectively, all of which were better than the classification effect of synchronous ECG. The reliability of BCG in contactless emotion recognition was proved.</p
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