Citizen scientists contribute to motor learning research

A new study examined the results of data generated by citizen scientists using a simple web-based motor test. The big data approach offers researchers a unique way to explore how people correct motor control errors. The resulting insights could one day pave the way for personalized physical therapy or tailor an athlete’s training routine. The results are available in Human behavior.

“This exploratory approach does not replace laboratory studies, but complements them, asking whether motor behavior can generalize to the entire population,” said Jonathan Tsay, assistant professor in the university’s psychology department. Carnegie Mellon and first author of the article. “I see this large-scale approach as a way to democratize motor learning research.”

Traditionally, motor learning scientists studied how people acquired motor skills in the laboratory using expensive equipment to capture subtle changes in a person’s movement in response to movement errors. These studies often involve a small number of participants. It is unknown whether these results generalize to a broader population.

Tsay wanted to explore motor skills from a new angle, using Big Data. To collect the data, he developed a simple motor learning assessment that people could complete online from the comfort of their homes. The result is a dataset of over 2,000 sessions from a diverse participant population.

The study can also assess different processes underlying motor learning, that is, the relative contribution of subconscious and implicit motor learning and conscious and explicit motor learning. With the data available, Tsay was able to examine how demographic variables affect the relative contribution of these two learning styles.

The short at-home test lasted about eight minutes, compared to a normal 80-minute lab experience. Many participants reconnected and contributed multiple sessions to the database, allowing the research team to effectively track changes in motor learning.

The potential of Big Data lies in better understanding the variables, such as gender, age, visual impairment, and even video game experience, that can impact motor adaptation.

Tsay cites age as an example. It may seem obvious that age would be an important factor affecting motor adaptation, but the effect of age has been mixed in laboratory studies. Confusion may be due in part to the small sample size and focus on extreme age groups (very young and very old).

Using big data, Tsay and her colleagues were able to examine age as a continuous variable. The results showed how participants changed their strategies to correct a motor error throughout life, with adaptation peaking between ages 35 and 45. These adaptations have been missed by previous studies with only a limited sample.

“Using machine learning and other techniques (this approach allowed us) to predict who would succeed in motor learning and which properties (movement speed and reaction time) are good predictors of motor learning success over the course of ‘one session,’ Tsay said. “The results we found through this exploratory big data method can be brought back to the lab to perform more hypothesis-driven studies to find the mechanism behind the results we see online.”

The simple motor learning task was only able to predict about 15% of the variance in the study, limiting the information that can be gleaned from these results. Additionally, the motor task was not performed under the supervision of an experimenter or under specific control of parameters, such as technology type and Internet speed, which could have increased noise in the data. Despite these limitations, Tsay still believes that this large-scale approach is capable of examining this variability in detail, thereby deriving insights that can be valuable to the automotive research community.

“There are so many questions in psychology that lend themselves to online testing, but there are few motor studies,” said Richard Ivry, distinguished professor of psychology at the University of California, Berkeley and co-author of the study. . “THE (Human behavior) this study further strengthens our confidence that online studies can be very useful for studying motor control, and I know that many laboratories around the world have taken advantage of these tools.

Tsay and Ivry were joined by Hrach Asmerian and Ken Nakayama of the University of California, Berkeley, Laura Germine of Harvard Medical School, and Jeremy Wilmer of Wellesley College in the study, “Large-scale citizen science reveals predictors of sensorimotor adaptation.