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   https://doi.org/10.1002/wat2.1461  

   Wutich, Amber; Brewis, Alexandra; Tsai, Alexander (July 2020, WIREs Water)
2. Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data

   https://doi.org/10.1145/3643540  

   Xu, Xuhai; Yao, Bingsheng; Dong, Yuanzhe; Gabriel, Saadia; Yu, Hong; Hendler, James; Ghassemi, Marzyeh; Dey, Anind K; Wang, Dakuo (March 2024, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   Advances in large language models (LLMs) have empowered a variety of applications. However, there is still a significant gap in research when it comes to understanding and enhancing the capabilities of LLMs in the field of mental health. In this work, we present a comprehensive evaluation of multiple LLMs on various mental health prediction tasks via online text data, including Alpaca, Alpaca-LoRA, FLAN-T5, GPT-3.5, and GPT-4. We conduct a broad range of experiments, covering zero-shot prompting, few-shot prompting, and instruction fine-tuning. The results indicate a promising yet limited performance of LLMs with zero-shot and few-shot prompt designs for mental health tasks. More importantly, our experiments show that instruction finetuning can significantly boost the performance of LLMs for all tasks simultaneously. Our best-finetuned models, Mental-Alpaca and Mental-FLAN-T5, outperform the best prompt design of GPT-3.5 (25 and 15 times bigger) by 10.9% on balanced accuracy and the best of GPT-4 (250 and 150 times bigger) by 4.8%. They further perform on par with the state-of-the-art task-specific language model. We also conduct an exploratory case study on LLMs' capability on mental health reasoning tasks, illustrating the promising capability of certain models such as GPT-4. We summarize our findings into a set of action guidelines for potential methods to enhance LLMs' capability for mental health tasks. Meanwhile, we also emphasize the important limitations before achieving deployability in real-world mental health settings, such as known racial and gender bias. We highlight the important ethical risks accompanying this line of research. 

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3. Digital biomarker of mental fatigue

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   Abstract Mental fatigue is an important aspect of alertness and wellbeing. Existing fatigue tests are subjective and/or time-consuming. Here, we show that smartphone-based gaze is significantly impaired with mental fatigue, and tracks the onset and progression of fatigue. A simple model predicts mental fatigue reliably using just a few minutes of gaze data. These results suggest that smartphone-based gaze could provide a scalable, digital biomarker of mental fatigue. 

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4. Rhesus monkeys manipulate mental images

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   Hassett, Thomas C.; Lord, Victoria K.; Hampton, Robert R. (November 2022, Cognition)
5. Language as a mental travel guide

   https://doi.org/10.1017/S0140525X19003182  

   Davis, Charles P.; Altmann, Gerry T.; Yee, Eiling (January 2020, Behavioral and Brain Sciences)

   Abstract Gilead et al.'s approach to human cognition places abstraction and prediction at the heart of “mental travel” under a “representational diversity” perspective that embraces foundational concepts in cognitive science. But, it gives insufficient credit to the possibility that the process of abstraction produces a gradient, and underestimates the importance of a highly influential domain in predictive cognition: language, and related, the emergence of experientially based structure through time. 

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