Pitfalls in quantifying exploration in reward-based motor learning: simulated datasets
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'When learning a movement based on binary success information, one is more variablefollowing failure than following success. Theoretically, the additional variability postfailure might reflect exploration of possibilities to obtain success. When averagebehavior is changing (as in learning), variability can be estimated from differencesbetween subsequent movements. Can one estimate exploration reliably from suchtrial-to-trial changes when studying reward-based motor learning? To answer thisquestion, we tried to reconstruct the exploration underlying learning as described byfour existing reward-based motor learning models. We simulated learning for variouslearner and task characteristics. If we simply determined the additional change postfailure, estimates of exploration were sensitive to learner and task characteristics.' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["exploration"]} <|eot_id|>
 Here is the Description of a dataset. Description:'When learning a movement based on binary success information, one is more variablefollowing failure than following success. Theoretically, the additional variability postfailure might reflect exploration of possibilities to obtain success. When averagebehavior is changing (as in learning), variability can be estimated from differencesbetween subsequent movements. Can one estimate exploration reliably from suchtrial-to-trial changes when studying reward-based motor learning? To answer thisquestion, we tried to reconstruct the exploration underlying learning as described byfour existing reward-based motor learning models. We simulated learning for variouslearner and task characteristics. If we simply determined the additional change postfailure, estimates of exploration were sensitive to learner and task characteristics.' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
When learning a movement based on binary success information, one is more variablefollowing failure than following success. Theoretically, the additional variability postfailure might reflect exploration of possibilities to obtain success. When averagebehavior is changing (as in learning), variability can be estimated from differencesbetween subsequent movements. Can one estimate exploration reliably from suchtrial-to-trial changes when studying reward-based motor learning? To answer thisquestion, we tried to reconstruct the exploration underlying learning as described byfour existing reward-based motor learning models. We simulated learning for variouslearner and task characteristics. If we simply determined the additional change postfailure, estimates of exploration were sensitive to learner and task characteristics.
exploration