A language-based generative model framework for behavioral analysis of couples' therapy

Observational studies for psychological evaluations rely on careful assessment of multiple behavioral cues. Recent studies have made good progress in automating the psychological evaluation, which often involved tedious manual annotation of a set of behavioral codes. However, the current methods impose strict and often unnatural assumptions for evaluation. In this work, we specifically investigate two goals: (1) Human behavior changes throughout an interaction and better models of this evolution can improve automated behavioral annotation and (2) Human perception of this evolution can be quite complex and non-linear and better techniques than averaging need to be investigated. For this purpose, we propose a Dynamic Behavior Modeling (DBM) scheme, which models a spouse as undergoing changes in behavioral state within a session, and contrast it against a Static Behavior Model (SBM) which allows only a constant session-long behavioral state. We use Negativity in a couples therapy task as our case study. We present results and analysis on both models for capturing the local behavior information and predicting the session level negativity label.