Kinematic data of humans performing the critical stability task

Natural behaviors have redundancy, which implies that humans and animals can achieve their goals with different control objectives. Given only observations of behavior, is it possible to infer the control strategy that the subject is employing? This challenge is particularly acute in animal behavior because we cannot ask or instruct the subject to use a particular control strategy. This study presents a three-pronged approach to infer an animal’s control strategy from behavior. First, both humans and monkeys performed a virtual balancing task for which different control objectives could be utilized. Under matched experimental conditions, corresponding behaviors were observed in humans and monkeys. Second, a generative model was developed that represented two main control strategies to achieve the task goal. Model simulations were used to identify aspects of behavior that could distinguish which control objective was being used. Third, these behavioral signatures allowed us to infer the ..., Refer to the Methods section in https://doi.org/10.7554/eLife.88514.2, , # Kinematic Data of Humans Performing the Critical Stability Task
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The 'main.m' script includes analysis and simulation codes for the paper. The main.m file could be run by specifying the type of analysis as follows:

Case 1: Analysis_human_data.m

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This code generates figures regarding the human data analysis
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Case 2: Simulation.m

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This code generates simulation results.
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Case 3: SensitivityAnalysis_Delay.m

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This code generates simulation results for model sensitivity to

sensory delay
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Case 4: SensitivityAnalysis_Effort.m

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This code generates simulation results for model sensitivity to

Effort cost
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Case 5: SensitivityAnalysis_SDN.m

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This code generates simulation results for model sensitivity to

signal dependent noise
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Each analysis could be run both from the main.m file, or direct...