JYUnity

Depression, which affects over 300 million people globally, is considered the primary cause of ill health and disability in the world. In its treatment, the interaction and synchronous dyadic behavior of the client and the therapist have been regarded as robust predictors of treatment success. If we could understand this relationship better, health care providers could better recognize and treat depression.

Martin Hartmann’s ambitious interdisciplinary project attempts to address this need by focusing on how to operationalize and quantify musical, spoken and kinetic interaction in music therapy settings. He will collect and analyze data from sessions between music therapists and clients with a primary diagnosis of depression.

“Music therapy refers to the use of music in clinical settings as an engaging means to address clients’ therapeutic needs,” Hartmann explains. “This form of psychotherapy stimulates non-verbal expression and bodily activity, and allows for mutual interaction between various modes.”

In much psychotherapeutic interaction research, the focus has typically been on verbal communication. Yet, despite the challenges presented by verbal channels as a means of emotional expression, research into paralanguage and nonverbal communication remains scarce.

“My research aims to go beyond the state-of-the-art in psychotherapeutic interaction,” Hartmann says. “I hope to achieve this aim by tackling the multimodal nature of therapeutic interaction and using innovative computational modelling approaches.”

Hartmann’s work has a clear scientific impact, assessing the validity of quantitative techniques for the diagnosis of depression. It has potential for scientific breakthroughs in the study of interpersonal synchronization in psychotherapy because of its novel approach, which jointly and systematically investigates multiple modalities, including music, speech, and movement.

A tool to predict client outcome

Hartmann expects his research to have implications for the possible role of interaction as a means to determine the severity of other conditions, including generalized anxiety disorder, social phobia, strokes, Parkinson’s disease and autism. It may also lead to progress towards the efficient detection of the leading cause of disability worldwide.

“Moreover, my research has potential utilization value in the short term. This is because I will develop innovative methods to analyze dyadic interaction and synchronization that combine musical, speech and movement data,” Hartmann says.

In addition, Hartmann will publish a sector-level computational tool that uses a sequence of videos from a music therapy process as inputs. The output is then an assessment of client–therapist interaction for each modality of music, speech, and movement. The tool also provides predictions for client diagnosis and outcome.

This sounds quite fantastical, but it is not without a scientific basis.

“It is possible that certain patterns of interaction can predict client outcome. However, this important psychotherapeutic issue needs further investigation,” Hartmann points out.

Hartmann’s research project is a collaboration between the Faculty of Information Technology and the Department of Music, Art and Culture Studies. It is funded by the Academy of Finland and it will start in September 2018.