Traditional approaches in mental health research apply General Linear Models
(GLM) to describe the longitudinal dynamics of observed psycho-behavioral
measurements (questionnaire summary scores). Similarly, GLMs are also applied
to characterize relationships between neurobiological measurements (regional
fMRI signals) and perceptual stimuli or other regional signals. While these
methods are useful for exploring linear correlations among the isolated signals
of those constructs (i.e., summary scores or fMRI signals), these classical
frameworks fall short in providing insights into the comprehensive system-level
dynamics underlying observable changes. Hidden Markov Models (HMM) are a
statistical model that enable us to describe the sequential relations among
multiple observable constructs, and when applied through the lens of Finite
State Automata (FSA), can provide a more integrated and intuitive framework for
modeling and understanding the underlying controller (the prescription for how
to respond to inputs) that fundamentally defines any system, as opposed to
linearly correlating output signals produced by the controller. We present a
simple and intuitive HMM processing pipeline vcHMM (See Preliminary Data) that
highlights FSA theory and is applicable for both behavioral analysis of
questionnaire data and fMRI data. HMMs offer theoretic promise as they are
computationally equivalent to the FSA, the control processor of a Turing
Machine (TM) The dynamic programming Viterbi algorithm is used to leverage the
HMM model. It efficiently identifies the most likely sequence of hidden states.
The vcHMM pipeline leverages this grammar to understand how behavior and neural
activity relate to depression.

传统的心理健康研究方法包括广义线性模型（GLM）来描述观察到的心理行为测量（问卷摘要得分）的纵向动态。而隐藏马尔可夫模型（HMM）通过有限状态自动机（FSA）的视角应用于行为和神经活动与抑郁之间的关系，以提供更综合和直观的建模和理解框架。

利用情绪处理的有限状态研究晚年心理健康

Leveraging The Finite States of Emotion Processing to Study Late-Life  Mental Health

Recently, there has been a growing interest in utilizing large language
models (LLMs) in mental health research, with studies showcasing their
remarkable capabilities, such as disease detection. However, there is currently
a lack of a comprehensive benchmark for evaluating the capability of LLMs in
this domain. Therefore, we address this gap by introducing the first
comprehensive benchmark tailored to the unique characteristics of the mental
health domain. This benchmark encompasses a total of six sub-tasks, covering
three dimensions, to systematically assess the capabilities of LLMs in the
realm of mental health. We have designed corresponding concise prompts for each
sub-task. And we comprehensively evaluate a total of eight advanced LLMs using
our benchmark. Experiment results not only demonstrate significant room for
improvement in current LLMs concerning mental health but also unveil potential
directions for future model optimization.

目前缺乏对大型语言模型（LLMs）在心理健康领域能力评估的全面基准。因此，我们填补该空白，并引入了首个适用于心理健康领域特点的全面基准，包括六个子任务、三个维度，系统评估 LLMs 在心理健康领域的能力。我们为每个子任务设计了相应的简明提示，并全面评估了八个先进的 LLMs 使用我们的基准。实验结果不仅展示了当前 LLMs 在心理健康方面的改进空间，还揭示了未来模型优化的潜在方向。