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This workshop summary on natural language processing (NLP) markers for psychosis and other psychiatric disorders presents some of the clinical and research issues that NLP markers might address and some of the activities needed to move in that direction. We propose that the optimal development of NLP markers would occur in the context of research efforts to map out the underlying mechanisms of psychosis and other disorders. In this workshop, we identified some of the challenges to be addressed in developing and implementing NLP markers-based Clinical Decision Support Systems (CDSSs) in psychiatric practice, especially with respect to psychosis. Of note, a CDSS is meant to enhance decision-making by clinicians by providing additional relevant information primarily through software (although CDSSs are not without risks). In psychiatry, a field that relies on subjective clinical ratings that condense rich temporal behavioral information, the inclusion of computational quantitative NLP markers can plausibly lead to operationalized decision models in place of idiosyncratic ones, although ethical issues must always be paramount.

Background: Identifying and addressing hotspots is a key element of imaginal exposure in Brief Eclectic Psychotherapy for PTSD (BEPP). Research shows that treatment effectiveness is associated with focusing on these hotspots and that hotspot frequency and characteristics may serve as indicators for treatment success. Objective: This study aims to develop a model to automatically recognize hotspots based on text and speech features, which might be an efficient way to track patient progress and predict treatment efficacy. Method: A multimodal supervised classification model was developed based on analog tape recordings and transcripts of imaginal exposure sessions of 10 successful and 10 non-successful treatment completers. Data mining and machine learning techniques were used to extract and select text (e.g. words and word combinations) and speech (e.g. speech rate, pauses between words) features that distinguish between ‘hotspot’ (N = 37) and ‘non-hotspot’ (N = 45) phases during exposure sessions. Results: The developed model resulted in a high training performance (mean F1-score of 0.76) but a low testing performance (mean F1-score = 0.52). This shows that the selected text and speech features could clearly distinguish between hotspots and non-hotspots in the current data set, but will probably not recognize hotspots from new input data very well. Conclusions: In order to improve the recognition of new hotspots, the described methodology should be applied to a larger, higher quality (digitally recorded) data set. As such this study should be seen mainly as a proof of concept, demonstrating the possible application and contribution of automatic text and audio analysis to therapy process research in PTSD and mental health research in general.

In speech perception research, one is often interested in the relationship between phoneme identification and discrimination of stimuli drawn from the same stimulus continuum. If discrimination performance is completely predictable from identification, perception is often said to be completely categorical: In both tasks, subjects (can) only use phoneme labels. How should one compare a one‐interval identification task with a two‐interval forced‐choice discrimination task in which subjects have to determine the order of the stimuli? Using standard SDT assumptions about optimal processing [D. M. Green and J. A. Swets, Signal Detection Theory and Psychophysics (New York, 1974)], it was found that identification and discrimination of natural vowels were nearly equivalent, but with natural stop consonants identification d′ was, paradoxically, twice as high as discrimination d′. It was concluded that subjects probably use a different strategy for the discrimination of natural stops: They do not subtract the traces of the two stimuli, but the estimated distances between the stimuli and the phoneme prototypes. Such an assumption yields d′ values that are twice as high as the standard values.

In this paper we discuss our research on interactions in a virtual theatre that has been built using VRML and therefore can be accessed through Web pages. In the virtual environment we employ several agents. The virtual theatre allows navigation input through keyboard and mouse, but there is also a navigation agent which listens to typed input and spoken commands. Feedback of the system is given using speech synthesis. We also have an information agent which allows a natural language dialogue with the system where the input is keyboard-driven and the output is both with tables as with template driven natural language generation. In development are several talking faces for the different agents in the virtual world. At this moment an avatar with a cartoon-like talking face driven by a text-to-speech synthesizer can provide users with information about performances in the theatre.

We discuss research on interaction in a virtual theatre that can be accessed through Web pages. In the environment we employ several agents. The virtual theatre allows navigation through keyboard and mouse, but there is also a navigation agent which listens to typed input and spoken commands. We also have an information agent which allows a NL dialogue, where input is keyboard- driven and output is by tables and template driven NL generation. In development are talking faces for the agents. A user’s commitment to this environment is increased by increasing ‘presence’.