  Mental illness remains one of the most critical public health issues. Despite
its importance, many mental health professionals highlight a disconnect between
their training and actual real-world patient practice. To help bridge this gap,
we propose PATIENT-{\Psi}, a novel patient simulation framework for cognitive
behavior therapy (CBT) training. To build PATIENT-{\Psi}, we construct diverse
patient cognitive models based on CBT principles and use large language models
(LLMs) programmed with these cognitive models to act as a simulated therapy
patient. We propose an interactive training scheme, PATIENT-{\Psi}-TRAINER, for
mental health trainees to practice a key skill in CBT -- formulating the
cognitive model of the patient -- through role-playing a therapy session with
PATIENT-{\Psi}. To evaluate PATIENT-{\Psi}, we conducted a comprehensive user
study of 13 mental health trainees and 20 experts. The results demonstrate that
practice using PATIENT-{\Psi}-TRAINER enhances the perceived skill acquisition
and confidence of the trainees beyond existing forms of training such as
textbooks, videos, and role-play with non-patients. Based on the experts'
perceptions, PATIENT-{\Psi} is perceived to be closer to real patient
interactions than GPT-4, and PATIENT-{\Psi}-TRAINER holds strong promise to
improve trainee competencies. Our code and data are released at
\url{https://github.com/ruiyiw/patient-psi}.
