Language patterns during memory tests may reveal early cognitive dysfunction
Clinicians and researchers use standardized tests to assess memory and thinking ability. In one commonly used memory test, a short story is read to patients, and then they try to recall it from memory. Their responses are scored by counting the number of story details each patient remembers. However, this scoring ignores a wealth of information about how the person responded, such as the use of…
Clinicians and researchers employ standardized memory tests to evaluate the cognitive abilities of patients. One such test involves reading a short story to the patient and assessing their ability to recall its details. However, current scoring methods overlook crucial aspects of the patient's performance, such as their choice of sentence complexity, repetition of information, or acknowledgment of forgetting certain story elements (e.g., "I forgot her name").
A study conducted by researchers at Boston University Chobanian & Avedisian School of Medicine has revealed that the language patterns exhibited during a memory test can indicate cognitive impairment and even predict future cognitive performance. Lead author Seho Park, a postdoctoral associate at the school, emphasizes that their method goes beyond a simple right-or-wrong scoring system, capturing important nuances in how a person processes and remembers information.
The researchers analyzed digital voice recordings of participants' responses to a story recall test within the Long Life Family Study. By applying natural language processing techniques, they identified subtle differences between the responses of individuals with cognitive impairment and those with normal cognition. These differences included a lower recall of specific story details and the inclusion of unrelated comments.
The researchers then categorized these linguistic markers into a comprehensive speech profile. This speech pattern proved to be a reliable predictor of lower cognitive performance seven years later. The team hopes that this approach, along with other computational methods, will facilitate the widespread adoption of advanced cognitive assessment tools, extending their use beyond specialized clinical and research settings to primary care settings and even smartphone applications for self-assessments of cognitive function.
According to corresponding author Stacy Andersen, Ph.D., associate professor of medicine, these findings pave the way for the development of automated scoring systems that can identify early markers of cognitive dysfunction, allowing for timely interventions and improved patient outcomes. The research was published in the Journal of the International Neuropsychological Society.
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