Verbal memory deficits are some of the most profound neurocognitive deficits associated with schizophrenia and serious mental illness in general. As yet, their measurement in clinical settings is limited to traditional tests that allow for limited administrations and require substantial resources to deploy and score. Therefore, we developed a digital ambulatory verbal memory test with automated ...

The evaluation of verbal memory is a core component of neuropsychological assessment in a wide range of clinical and research settings. Leveraging story recall to assay neurocognitive function could be made more useful if it were possible to administer frequently (i.e., would allow for the collection of more patient data over time) and automatically assess the recalls with machine learning methods. ...

Detecting signs of disorder from listening to spoken words is a core method in psychiatry. Traditionally the interpretation of speech depends on inherently subjective processes. By contrast, digital technology can be leveraged to detect and analyze what words are spoken, timestamp when they are uttered and quantify the manner in which they are expressed. With the use of mobile communication technology, ...

<p><i>Background and Hypothesis:</i> Despite decades of “proof of concept” findings supporting the use of Natural Language Processing (NLP) in psychosis research, clinical implementation has been slow. One obstacle reflects the lack of comprehensive psychometric evaluation of these measures. There is overwhelming evidence that criterion and content validity can be achieved for many purposes, ...

Modern advances in computational language processing methods have enabled new approaches to the measurement of mental processes. However, the field has primarily focused on model accuracy in predicting performance on a task or a diagnostic category. Instead the field should be more focused on determining which computational analyses align best with the targeted neurocognitive/psychological functions ...

Modern advances in computational language processing methods have enabled new approaches to the measurement of mental processes. However, the field has primarily focused on model accuracy in predicting performance on a task or a diagnostic category. Instead the field should be more focused on determining which computational analyses align best with the targeted neurocognitive/psychological functions ...

Evaluating patients’ verbal fluency by counting the number of unique words (e.g., animals) produced in a short-period (e.g., 1–3 min) is one of the most widely employed cognitive tests in psychiatric research. We introduce new methods to analyze fluency output that leverage modern computational language technology. This enables moving beyond simple word counts to charting the temporal dynamics of ...

Speech rate and quantity reflect clinical state; thus automated transcription holds potential clinical applications. We describe two datasets where recording quality and speaker characteristics affected transcription accuracy. Transcripts of low-quality recordings omitted significant portions of speech. An automated syllable counter estimated actual speech output and quantified the amount of ...