Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction (2023)

Abstract: A proven therapeutic technique to overcome negative thoughts is to replace them with a more hopeful “reframed thought.” Although therapy can help people practice and learn this Cognitive Reframing of Negative Thoughts, clinician shortages and mental health stigma commonly limit people’s access to therapy. In this paper, we conduct a human-centered study of how language models may assist people in reframing negative thoughts. Based on psychology literature, we define a framework of seven linguistic attributes that can be used to reframe a thought. We develop automated metrics to measure these attributes and validate them with expert judgements from mental health practitioners. We collect a dataset of 600 situations, thoughts and reframes from practitioners and use it to train a retrieval-enhanced in-context learning model that effectively generates reframed thoughts and controls their linguistic attributes. To investigate what constitutes a “high-quality” reframe, we conduct an IRB-approved randomized field study on a large mental health website with over 2,000 participants. Amongst other findings, we show that people prefer highly empathic or specific reframes, as opposed to reframes that are overly positive. Our findings provide key implications for the use of LMs to assist people in overcoming negative thoughts.

## introduction negative thoughts are a natural part of human cognition. however, for people experiencing mental health challenges, such thoughts are often entrenched, automatic and emotionally triggering, making it difficult to overcome them in-themoment @xcite ). an evidence-based, wellestablished therapeutic intervention to overcome negative thoughts is cognitive reframing, in which a negative thought is replaced with a more hopeful "reframed thought," which offers an alternative perspective on one's situation @xcite . for example, imagine a person with a situation "i'm submitting a research paper to acl 2023" has a thought "this paper is going to get rejected." a possible way to reframe this thought is to say "this paper has some chance of getting accepted due to its novel methodology and potential impact." psychotherapy research suggests that for a reframed thought to be effective, it must be (a) relatable to the individual, (b) helpful in overcoming the negative thought and (c) memorable to be accessible the next time a similar thought arises @xcite . however, understanding what characterizes a relatable, helpful and memorable reframe is challenging and unknown. professional therapists can support people in coming up with such highly effective reframed thoughts. however, barriers like clinician shortages, lack of insurance coverage and stigma commonly limit access to therapists @xcite . nlpbased methods that assist individuals in reframing negative thoughts, in-the-moment, may provide scaffolding that is easier to engage with and that could be made widely accessible. prior nlp research has developed methods for a range of text reframing tasks like sentiment and empathy rewriting @xcite and more recently, positive reframing @xcite . however, little is known about how to develop cognitive reframing methods that automatically generate relatable, helpful and memorable reframed thoughts. in this paper, we conduct a study of how language models can be used to reframe negative thoughts (figure 1 ). we study ways in which a negative thought can be reframed, how lms can be utilized to perform this reframing and what types of reframes are preferred by people who experience negative thoughts. first, in collaboration with clinical psycholohere, we propose a framework of seven reframing attributes (see gray box). given a reframed thought, we control each attribute (e.g., actionability) to generate reframes that score higher or lower on that attribute (e.g., more or less actionable); (c) we deploy this model on mental health america, a large u.s. national mental health website (bit.ly/changing-thoughts) and conduct a randomized field study with 2,067 participants. we suggest lm-generated reframes to mha visitors and assess which reframing attributes are desirable and what constitutes a relatable, helpful and memorable reframe. we investigate which reframing attributes are desirable and what constitutes a relatable, helpful and memorable reframe. in collaboration (and coauthorship) with mental health experts, and after appropriate ethical review, we deploy a month-long randomized field study on mental health america (mha; a popular website that shares mental health resources and tools online), with 2,067 participants with informed consent. we ask mha visitors to describe situations and negative thoughts they are experiencing and then suggest lm-generated reframed thoughts with varying linguistic attributes. we find that highly specific and highly empathic reframing is the most preferred and highly specific and actionable reframing is considered the most helpful and memorable. however, we find that reframes that are highly positive are less preferred. these findings provide key implications for cognitive reframing of negative thoughts and for the use of human-lm interaction in this process. ## problem definition and goals we work on the task of cognitive reframing. given a situation s i and a negative thought t i , the task is to generate a reframed thought r i . psychotherapy literature @xcite ) highlights three desirable outcomes for a successful reframe: here, we aim to understand what constitutes successful reframing and how language models can assist people in this process. towards this goal, we characterize the linguistic attributes of reframed thoughts ( §3), collect a dataset of situations, thoughts and reframes ( §4), develop methods to generate reframes and to measure and control their attributes ( §5; §6) and investigate which linguistic attributes are related to the reframing outcomes of relatability, helpfulness and memorability ( §7). ## framework of linguistic attributes of reframed thoughts we draw from clinical therapy practices and collaborate with mental health experts (some of whom are co-authors) to develop a framework of linguistic attributes of reframed thoughts. we illustrate these attributes with the following example for all reframes below -situation: "i submitted a research paper and it got rejected;" thought: "i'll never succeed as a researcher." addressing thinking traps. negative thinking often falls into common patterns, called "thinking traps." also called cognitive distortions, these include exaggerated and biased patterns of thinking which cause individuals to perceive reality inaccurately @xcite . common thinking traps include: assuming what others think ("mind reading"), thinking in extremes ("allor-nothing thinking"), focusing on the worst-case scenario ("catastrophizing"), trying to predict the future ("fortune telling"), etc. see appendix d for the full list. a reframe may or may not directly address one or more of the thought's thinking traps. a reframe like "i don't know what the future will bring" directly addresses the thinking trap "fortune telling," whereas a reframe like "i will surely become a successful researcher" does not address this thinking trap but rather continues to express it. ## data collection to facilitate computational methods for cognitive reframing, we collect a dataset of reframed thoughts, annotated with their linguistic attributes. ## method we design automated metrics for measuring linguistic attributes ( §5.1), develop methods to generate reframed thoughts ( §5.2) and to control their attributes ( §5.3). ## randomized field study on a large mental health platform next, we deploy our model on a large mental health platform ( §7.1) and study what types of reframes do people prefer ( §7.2) and what characterizes relatable, helpful and memorable reframes ( §7.3). starting from our collected data samples, we utilize the pattern replication capabilities of gpt-3 to generate 10,000 more examples, similar to @xcite . ## related work several human-lm interaction tools for mental health assist support providers, e.g., clinicians @xcite or peers @xcite . our work provides insights on how human-lm interaction may directly support people struggling with mental health challenges through cognitive reframing. computational work on cognitive reframing has relied on smallscale crowdsourcing studies @xcite . our work develops scalable methods for cognitive reframing and conducts a randomized field study on a large mental health platform. prior text reframing research has developed methods for related tasks including style, sentiment, politeness and empathy transfer @xcite @xcite as well as positive reframing @xcite . our work develops text-reframing methods for cognitive reframing and demonstrates that linguistic attributes of addressing thinking traps, rationality, actionability, specificity and readability are critical to high-quality reframes. more broadly, our work relates to the growing body of research in nlp for mental health and psychological wellbeing @xcite @xcite @xcite @xcite @xcite @xcite @xcite @xcite @xcite @xcite @xcite @xcite . ## conclusion in this paper, we conducted a study of how human-language model interaction may support humans in the cognitive reframing of negative thoughts. we define a framework of seven linguistic attributes of cognitive reframing, develop automatic metrics to measure these attributes and validate their measurements with mental health experts. we collect and share a dataset of 600 situations, thoughts and reframes from mental health experts and use it to train a retrieval-enhanced in-context learning model based on gpt-3. we deploy this model on the mental health america website and conduct a randomized field study with 2,067 participants. we find that people struggling with negative thoughts prefer reframes that are highly empathic or specific, but do not prefer reframes that are highly positive.