Advancing research on the human mind: Simulating stress, sadness, and anxiety in large language models

Mental health conditions such as depression and anxiety disorders affect millions of people worldwide, yet the underlying mechanisms are often difficult to study. There is a lack of model systems that adequately capture the complexity of human cognitive and affective processes, such as language or reasoning. An interdisciplinary research team from medicine, psychology, and computer science at the Else Kröner Fresenius Center (EKFZ) for Digital Health at TU Dresden has now shown that large language models (LLMs) can reproduce patterns of human emotions such as anxiety, sadness, or stress. In addition, they exhibit cognitive biases and can be specifically regulated using mindfulness-based strategies. This could establish AI models as a new, complementary method for basic psychological and psychotherapy research. The results were published in The Lancet Digital Health.

In their modelling study, the researchers investigated six large language models (LLMs), including GPT-4o and several Llama versions. They used standardized text prompts to induce seven different affective states in the models: anxiety, fear, anger, disgust, sadness, worry, and stress. These represent emotional states that also play an important role in many mental health conditions. The researchers then assessed the models’ responses using structured rating scales, as commonly applied in psychological research. In a next step, the team showed that these states can be reduced again through mindfulness-based emotion regulation strategies, such as a breathing exercise. In addition, they found that the models exhibited typical cognitive biases, such as a tendency to complete sentences more negatively after sadness had been induced. These biases in language output are well known from depression research in humans. The results suggest that LLMs can model certain cognitive and affective processes of humans in a simplified form.

Potential for research and new approaches in psychotherapy

LLMs could therefore be used in the future as a scalable and controlled model system in research – for example, to investigate specific mechanisms of mental health conditions or to test new therapeutic approaches in silico. Particularly in early stages of research, such models could help refine hypotheses and design studies more efficiently.

“Our results show that large language models can reproduce patterns of human affective and cognitive processes under controlled conditions. For psychology, this opens up the possibility to test hypotheses in a scalable and experimentally controllable system. We can use these models as tools to better understand underlying mechanisms and to explore new approaches – for example in talk-based psychotherapy,” says Dr. Magdalena Wekenborg, biopsychologist and head of the PsychoDigital research group at the EKFZ for Digital Health at TU Dresden.

Limitations of the study and open questions

The authors also emphasize the limitations of this approach: LLMs do not possess real emotions; their responses are based on learned patterns from training data. The intention is not to anthropomorphize artificial intelligence. Open questions therefore include the extent to which the findings can be transferred to human behavior, as well as the underlying mechanisms within the language models and their explainability. The researchers do not see LLMs as a replacement, but rather as a meaningful addition to psychological studies.

“One advantage of experiments with large language models is their reproducibility and scalability: we can repeat identical conditions as often as needed and systematically vary them. This enables new, data-driven experiments in psychological and biomedical research that were previously not possible,” says Prof. Jakob N. Kather, Professor of Clinical Artificial Intelligence at TU Dresden and physician at the University Hospital Dresden.

The publication highlights how collaboration at the interface of psychology, medicine, and computer science can open up new research approaches and reflects the core interdisciplinary strength of the EKFZ for Digital Health at TU Dresden.

Publication

Magdalena K. Wekenborg, Elizabeth A.M. Michels, Georg Kurze, Matti L. Kropp, Fabian Wolf, Josi Harzbecker, Isabella C. Wiest, Jakob N. Kather: Large language models as models of human psychopathology: a modelling study. The Lancet Digital Health, 2026. Link: https://www.sciencedirect.com/science/article/pii/S2589750026000373

Else Kröner Fresenius Center (EKFZ) for Digital Health

The EKFZ for Digital Health at the Faculty of Medicine at TUD Dresden University of Technology and University Hospital Carl Gustav Carus Dresden was established in September 2019. It receives funding of around 40 million euros from the Else Kröner Fresenius Foundation for a period of ten years. The center focuses its research activities on innovative, medical and digital technologies at the direct interface with patients. The aim here is to fully exploit the potential of digitalization in medicine to significantly and sustainably improve healthcare, medical research and clinical practice.

Contact
EKFZ for Digital Health
TUD Dresden University of Technology
Anja Stübner and Dr. Viktoria Bosak
Science Communication
Tel.: +49 351 – 458 11379

digitalhealth.tu-dresden.de