WCP 2025

Introduction. Overgeneral autobiographical memory (OGM) refers to the lack of specific details when individuals recall their own experiences. Rumination refers to the concentration of attention on one's own emotional state without considering how to solve problems. both OGM and rumination play significant roles in the onset and maintenance of depression.The eye movement adjustment technology (EMAT) with eye closed refers to a technology where, in a state of closed eyes, the eyes are guided by the intention to move back and forth in a specific direction. The technique has been applied to alleviate symptoms such as depression, anxiety, and insomnia. However, there is currently no research on the effect of both OGM and rumination in depressed patients.
Object:.This study aims to explore whether the technique can improve the levels of OGM and rumination in depressed patients.
Methods: 60 depressed patients meeting the criteria of DSM-5 for major depressive disorder were randomly divided into experimental group and control group, each group of 30 patients.The experimental group received combination EMAT（2 times per week） and selective serotonin reuptake inhibitors (SSRIs) for 4 weeks, the control group received SSRIs therapy for 4 weeks. Before and after the experiment, all the patients accept the HAMD、the autobiographical memory test（AMT）and the rumination response scale(RRS) test respectively.
Results ①In the experimental group, the total scores of HAMD、the negative OGM and RRS scores were reduceded significantly at the after of experiment than the before of experiment（P﹤0.05）; ②In the control group, the total scores of HAMD and RRS were reduceded significantly at the after of experiment than the before of experiment（P﹤0.05）, but the positive OGM,the negative OGM scores were not reduceded significantly（P＞0.05）; ③After the experiment, the scores of negative OGM and RRS in the experimental group were decreased significantly than those in the control group respectively(P﹤0.05).
Conclusions The EMAT may decrease the negative OGM and rumination for depressed patients.In the future, there is a need to explore the neural mechanisms underlying the effects of EMAT on OGM and rumination in depressed patients.

Introduction This research aims to integrate artificial intelligence technology, particularly automated machine learning methods, into psychological research to address the limitations of traditional frequentist statistical approaches in handling nonlinear relationships of psychological characteristics. Current psychological research predominantly relies on regression-based methods, which often fall short in capturing the complex nonlinear relationships between psychological features. We propose an innovative semi-automated workflow that empowers psychology researchers to leverage machine learning algorithms for intelligent model selection, facilitating the construction of more precise and insightful theoretical frameworks.

Methodology We designed a low-code workflow based on AutoGluon, specifically tailored for psychological research methodologies with high accessibility. This approach encompasses three primary research objectives:
1. Automated hyperparameter tuning to attain optimal models.
2. Identification of important features through interpretability techniques, facilitating feature selection based on calculated importance.
3. Theoretical reconstruction based on important features by integrating exploratory factor analysis with machine learning interpretability.
Using psychological resilience research as an example, we provide a detailed annotated code workflow along with raw data to demonstrate the application of this method.

Results The workflow successfully implements automated machine learning model selection and optimization, effectively identifying key features in psychological resilience research. Through interpretability techniques, we quantify the relative importance of different features, providing a data-driven basis for feature selection. Combined with exploratory factor analysis, we reconstruct theoretical frameworks that more accurately reflect the nonlinear relationships among psychological characteristics.

Conclusion The semi-automated workflow proposed in this study provides psychological researchers with a practical tool to better understand nonlinear relationships between psychological characteristics. This low-code approach lowers the barrier for psychological researchers to utilize advanced machine learning techniques, offering new possibilities for data-driven psychological theory construction. Future research can extend this methodology to additional psychological research domains and integrate more types of data, such as text, audio, and images.