Language analysis outperforms standard tests in spotting cognitive issues

Written by Marisa Horak, MS | July 28, 2026

• Language analysis of storytelling detected Parkinson's cognitive issues better than standard tests.
• Parkinson's patients showed reduced storytelling accuracy, organization, and sometimes verbosity.
• This method offers a nuanced look at episodic memory problems in Parkinson's.

Language-based analyses of how people with Parkinson’s disease tell stories may provide a more nuanced look at how the disease affects memory and cognition, according to a study.

In fact, the data suggest that a language-based approach can distinguish between people with or without Parkinson’s more accurately than standard cognitive tests. The researchers called for further studies on how this type of language analysis might be deployed to monitor cognitive changes in patients with Parkinson’s.

An early-access version of the study, “Digitizing episodic memory assessments in Parkinson’s disease via natural language processing,” was published in npj Parkinson’s Disease.

Parkinson’s is a neurological disorder that often causes memory and cognition problems. One of the more common manifestations of Parkinson’s-related cognitive challenges is problems with episodic memory — that is, the ability to recall and recount specific events, such as a trip or a party.

A range of standardized cognitive tests is routinely used to check for memory issues in Parkinson’s. Most of these tests assess how well a person can recall facts — for example, by providing a list of numbers and later asking the person to recite it.

Taking a nuanced approach

But memory isn’t just about recalling discrete facts. This is especially true for episodic memory: A person’s memory of an event includes not only what happened, but also their emotions and sensory experiences. Standard cognitive tests that assess only how well people recall facts may miss key nuances in how memory is affected in people with Parkinson’s.

An international team of scientists tested a different approach, using a computer-based analytical technique called natural language processing. They had patients read two stories — one action-based, focused on movement, and one more focused on internal emotions — and then recount both. A computer then analyzed the patients’ words.

The computer looks at three factors: verbosity, or how many different types of words the person uses; semantic acuity, which refers to how accurately the facts of the story are recalled; and organizational similarity, which evaluates whether the patient’s retelling matches the structure of the original story.

To test this approach, the researchers recruited 35 people with Parkinson’s and 39 control participants without the disease. They found that, for both types of stories, those with Parkinson’s showed reduced semantic acuity and lower organizational similarity compared with controls. Parkinson’s patients were also less verbose than controls on the action story, though verbosity was similar for the emotion-focused story.