Subjects: Psychology >> Applied Psychology submitted time 2024-01-09
Abstract： This paper explores the frontiers of large language models (LLMs) in psychology 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 provide a detailed exploration of how LLMs like ChatGPT are transforming psychological research. It discusses 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 potential to simulate aspects of human cognition and behavior. The paper delves into the capabilities of these models to emulate human-like text generation, offering innovative tools for literature review, hypothesis generation, experimental design, experimental subjects, data analysis, academic writing, and peer review in psychology. While LLMs are essential in advancing research methodologies in psychology, the paper also cautions about their technical and ethical challenges. There are issues like data privacy, the ethical implications of using LLMs in psychological research, and the need for a deeper understanding of these models' limitations. Researchers should responsibly use LLMs in psychological studies, adhering to ethical standards and considering the potential consequences of deploying these technologies in sensitive areas. Overall, the article provides a comprehensive overview of the current state of LLMs in psychology, exploring potential benefits and challenges. It serves as a call to action for researchers to leverage LLMs' advantages responsibly while addressing associated risks.
Peer Review Status:
Awaiting Review
Subjects: Psychology >> Social Psychology submitted time 2020-10-19
Abstract： "
Peer Review Status:Awaiting Review
Subjects: Psychology >> Cognitive Psychology submitted time 2019-03-25
Abstract： There are detailed theories and abundant empirical results regarding embodied cognition. However, embodiment effects are undergoing a replication crisis. Based on the hierarchical structure of embodiment tasks and the dual process property of embodiment phenomena, we propose the hierarchical access priority model (HAP). According to HAP, the generation of embodiment effects depends on the access priority of embodied variables to unconscious processes, and embodiment effects from different hierarchy levels show a contravariant relationship between effect size and stability. Theoretically, the stability of an embodiment effect is partly determined by the hierarchy of the embodied variable, and dissociation of the dual process moderates the effect size. Empirically, the hierarchical linear model analytic method should be considered for embodied research; the embodied variable could be designed as a mediating or moderating variable, and other possible masked mediating variables should be considered. HAP offers an insightful theoretical perspective for the embodiment replication crisis.
Peer Review Status:Awaiting Review
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