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Inferring control objectives in a virtual balancing task in humans and monkeys.
Mohsen Sadeghi1, Reza Sharif Razavian1,2,3, Salah Bazzi1,2,4
1Department of Biology, Northeastern University, Boston, United States.
Elife
|May 13, 2024
Summary
Researchers developed a method to infer control objectives from behavior in humans and monkeys. This approach helps understand sensorimotor coordination by identifying strategies used in natural behaviors.
Area of Science:
- Neuroscience
- Behavioral Science
- Computational Biology
Background:
- Natural behaviors exhibit redundancy, allowing diverse strategies to achieve goals.
- Inferring control objectives from observed behavior is challenging, especially in animals unable to provide verbal feedback.
Purpose of the Study:
- To develop and validate a method for inferring control objectives from behavioral observations.
- To differentiate between distinct control strategies employed during motor tasks.
Main Methods:
- Comparative behavioral analysis of humans and monkeys performing a virtual balancing task.
- Development of a generative model with distinct control objectives to simulate behavior.
- Validation of behavioral signatures for objective inference in humans before applying to animals.
Main Results:
- Humans and monkeys exhibited corresponding behaviors under matched experimental conditions.
- The generative model successfully identified behavioral aspects distinguishing control objectives.
- The method accurately inferred instructed control objectives in human subjects.
Conclusions:
- Observed behaviors can be used to infer underlying control objectives in both humans and animals.
- This approach offers a valuable tool for neurophysiologists studying sensorimotor coordination mechanisms.
- Understanding control objectives from behavior advances research into neural control strategies.
