Leveraging the synergy between causal knowledge graphs and a large language model (LLM), our study introduces a groundbreaking approach for computational hypothesis generation in psychology. We analyzed 43,312 psychology articles using a LLM to extract causal relation pairs. This analysis produced a specialized causal graph for psychology. Applying link prediction algorithms, we generated 130 potential psychological hypotheses focusing on “well-being”, then compared them against research ideas conceived by doctoral scholars and those produced solely by the LLM. Interestingly, our combined approach of a LLM and causal graphs mirrored the expert-level insights in terms of novelty, clearly surpassing the LLM-only hypotheses (t(59) = 3.34,p = 0.007 andt(59) = 4.32,p < 0.001, respectively). This alignment was further corroborated using deep semantic analysis. Our results show that combining LLM with machine learning techniques such as causal knowledge graphs can revolutionize automated discovery in psychology, extracting novel insights from the extensive literature. This work stands at the crossroads of psychology and artificial intelligence, championing a new enriched paradigm for data-driven hypothesis generation in psychological research.

This review explores the frontiers of large language models (LLMs) in psychological applications. Psychology has undergone several theoretical changes, and the current use of artificial intelligence (AI) and machine learning, particularly LLMs, promises to open up new research directions. We aim to provide a detailed exploration of how LLMs are transforming psychological research. We discuss the impact of LLMs across various branches of psychology—including cognitive and behavioral, clinical and counseling, educational and developmental, and social and cultural psychology—highlighting their ability to model patterns, cognition, and behavior similar to those observed in humans. Furthermore, we explore the ability of such models to generate coherent, contextually relevant text, offering innovative tools for literature reviews, hypothesis generation, experimental designs, experimental subjects, and data analysis in psychology. We emphasize the importance of addressing technical and ethical challenges, including data privacy, the ethics of using LLMs in psychological research, and the need for a deeper understanding of these models’ limitations. Researchers should use LLMs responsibly in psychological studies, adhering to ethical standards and considering the potential consequences of deploying these technologies in sensitive areas. Overall, this review provides a comprehensive overview of the current state of LLMs in psychology, exploring the potential benefits and challenges. We hope it can serve as a call to action for researchers to responsibly leverage LLMs’ advantages while addressing the associated risks.

As psychological research progresses, the issue of concept overlap becomes increasing evident, adding to participant burden and complicating data interpretation. This study introduces an Embedding-based Semantic Analysis Approach (ESAA) for detecting redundancy in psychological concepts, which are operationalized through their respective scales, using natural language processing techniques. The ESAA utilizes OpenAI’s text-embedding-3-large model to generate high-dimensional semantic vectors (i.e., embeddings) of scale items and applies hierarchical clustering to group semantically similar items, revealing potential redundancy. Three preliminary experiments evaluated the ESAA’s ability to (1) identify semantically similar items, (2) differentiate semantically distinct items, and (3) uncover overlap between scales of concepts known for redundancy issues. Additionally, comparative analyses assessed the ESAA’s robustness and incremental validity against the advanced chatbots based on GPT-4. The results demonstrated that the ESAA consistently produced stable outcomes and outperformed all evaluated chatbots. As an objective approach for analyzing relationships between concepts operationalized as scales, the ESAA holds promise for advancing research on theory refinement and scale optimization.

The global population is inevitably aging due to increased life expectancy and declining birth rates, leading to an amplified demand for innovative social and healthcare services. One promising avenue is the introduction of companion robots. These robots are designed to provide physical assistance as well as emotional support and companionship, necessitating effective human–robot interaction (HRI). This study explores the role of cognitive empathy within HRI, focusing on the influence of robot facial animacy and emotional expressions on perspective-taking abilities—a key aspect of cognitive empathy—across different age groups. To this end, a director task involving 60 participants (30 young and 30 older adults) with varying degrees of robot facial animacy (0%, 50%, 100%) and emotional expressions (happy, neutral) was conducted. The results revealed that older adults displayed enhanced perspective-taking with higher animacy faces. Interestingly, while happiness on high-animacy faces improved perspective-taking, the same expression on low-animacy faces reduced it. These findings highlight the importance of considering facial animacy and emotional expressions in designing companion robots for older adults to optimize user engagement and acceptance. The study’s implications are pertinent to the design and development of socially effective service robots, particularly for the aging population.

The tumor microenvironment (TME) plays an important regulatory role in the progression of non-small cell lung cancer (NSCLC). Mesenchymal stem cells (MSCs) in the TME might contribute to the occurrence and development of cancer. This study evaluates the role of differentially expressed genes (DEGs) of MSCs and the development of NSCLC and develops a prognostic risk model to assess the therapeutic responses. The DEGs in MSCs from lung tissues and from normal tissues were analyzed using GEO2R. The functions and mechanisms of the DEGs were analyzed using the Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Additionally, the Cancer Genome Atlas (TCGA) database was used to determine the expression levels of the DEGs of MSCs in the NSCLC tissues. The prognostic factors of NSCLC related to MSCs were screened by survival analysis, meta-analysis, Cox regression analysis, and a prognostic risk model and nomogram was developed. The signaling mechanisms and immune roles that risk model participate in NSCLC development were determined via Gene Set Enrichment Analysis and CIBERSORT analysis. Compared to the normal tissues, 161 DEGs were identified in the MSCs of the lung tissues. These DEGs were associated with mechanisms, such as DNA replication, nuclear division, and homologous recombination. The overexpression of DDIT4, IL6, ITGA11, MME, MSX2, POSTN, and TRPA1 were associated with dismal prognosis of NSCLC patients. A high-risk score based on the prognostic risk model indicated the dismal prognosis of NSCLC patients. The nomogram showed that the age, clinical stage, and risk score affected the prognosis of NSCLC patients. Further, the high-risk model was associated with signaling mechanisms, such as the ECM-receptor interaction pathways, cytokine-cytokine receptor interaction, and MAPK pathways, involved in the progression of NSCLC and was also related to the components of the immune system, such as macrophages M0, T follicular helper cells, regulatory T cells. Therefore, the risk model and nomogram that was constructed on the basis of MSC-related factors such as POSTN, TRPA1, and DDIT4 could facilitate the discovery of target molecules that participate in the progression of NSCLC, which might also serve as new candidate markers for evaluating the prognosis of NSCLC patients.