The revolution of generative artificial intelligence in psychology: The interweaving of behavior, consciousness, and ethics

生成语法 意识 心理学 认知科学 认识论 社会心理学 人工智能 哲学 计算机科学 神经科学
作者
Dian Chen,Ying Liu,Yiting Guo,Yulin Zhang
出处
期刊:Acta Psychologica [Elsevier BV]
卷期号:251: 104593-104593 被引量:27
标识
DOI:10.1016/j.actpsy.2024.104593
摘要

In recent years, there have been unparalleled prospects for psychological study due to the swift advancement of generative artificial intelligence (AI) in natural language processing, shown by ChatGPT. This review article looks into the uses and effects of generative artificial intelligence in psychology. We employed a systematic selection process, encompassing papers published between 2015 and 2024 from databases such as Google Scholar, PubMed, and IEEE Xplore, using keywords like "Generative AI in psychology" "ChatGPT and behavior modeling" and "AI in mental health". First, the paper goes over the fundamental ideas of generative AI and lists its uses in data analysis, behavior modeling, and social interaction simulation. A detailed comparison table has been added to contrast conventional research methodologies with GenAI-based approaches in psychology studies. Next, analyzing the theoretical and ethical issues that generative AI raises for psychological research, it highlights how crucial it is to develop a coherent theoretical framework. This study illustrates the benefits of generative AI in handling vast amounts of data and increasing research efficiency by contrasting traditional research methods with AI-driven methodologies. Regarding particular uses, the study explores how generative AI might be used to simulate social interactions, analyze massive amounts of text, and learn about cognitive processes. Section 5 has been expanded to include discussions on political biases, geographic biases, and other biases. In conclusion, the paper looks forward to the future development of generative AI in psychology research and suggests techniques for improving it. We have included methodological solutions such as the Retrieval Augmented Generation (RAG) approach and human-in-the-loop systems, as well as data privacy solutions like open-source local LLMs. In summary, generative AI has the potential to revolutionize psychological research, but in order to maintain the moral and scientific integrity of the field, ethical and theoretical concerns must be carefully considered before applying the technology.
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