Understanding the relationship between patient language and outcomes in internet-enabled cognitive behavioural therapy: A deep learning approach to automatic coding of session transcripts.

Objective: Understanding patient responses to psychotherapy is important in developing effective interventions. However, coding patient language is a resource-intensive exercise and difficult to perform at scale. Our aim was to develop a deep learning model to automatically identify patient utterances during text-based internet-enabled Cognitive Behavioural Therapy and to determine the association between utterances and clinical outcomes. Method: Using 340 manually annotated transcripts we trained a deep learning model to categorize patient utterances into one or more of five categories. The model was used to automatically code patient utterances from our entire data set of transcripts (∼34,000 patients), and logistic regression analyses used to determine the association between both reliable improvement and engagement, and patient responses. Results: Our model reached human-level agreement on three of the five patient categories. Regression analyses revealed that increased counter change-talk (movement away from change) was associated with lower odds of both reliable improvement and engagement, while increased change-talk (movement towards change or self-exploration) was associated with increased odds of improvement and engagement. Conclusions: Deep learning provides an effective means of automatically coding patient utterances at scale. This approach enables the development of a data-driven understanding of the relationship between therapist and patient during therapy.

It is all in the face: carotenoid skin coloration loses attractiveness outside the face.

Recently, the importance of skin colour for facial attractiveness has been recognized. In particular, dietary carotenoid-induced skin colour has been proposed as a signal of health and therefore attractiveness. While perceptual results are highly consistent, it is currently not clear whether carotenoid skin colour is preferred because it poses a cue to current health condition in humans or whether it is simply seen as a more aesthetically pleasing colour, independently of skin-specific signalling properties. Here, we tested this question by comparing attractiveness ratings of faces to corresponding ratings of meaningless scrambled face images matching the colours and contrasts found in the face. We produced sets of face and non-face stimuli with either healthy (high-carotenoid coloration) or unhealthy (low-carotenoid coloration) colour and asked participants for attractiveness ratings. Results showed that, while for faces increased carotenoid coloration significantly improved attractiveness, there was no equivalent effect on perception of scrambled images. These findings are consistent with a specific signalling system of current condition through skin coloration in humans and indicate that preferences are not caused by sensory biases in observers.