Inverse Optimal Control Data for Depression and Motor Performance

2020 data from a computational study by He Huang using an inverse optimal control framework to analyze depression's effects. The dataset supports factorization of performance deficits into sensorimotor speed, goal setting, and motivation components based on a computer-simulated driving experiment. It was published under a CC0 license.

Use Cases

• Apply inverse optimal control models to quantify sensorimotor speed deficits associated with depression severity.
• Analyze motivational function data to identify patterns specific to moderately and severely depressed individuals.
• Investigate goal setting functions using the recorded distance from a stop sign to study aversive learning effects.
• Build computational frameworks to disambiguate sensorimotor from motivational dysfunctions on an individual basis.

Strengths

• Data originates from a peer-reviewed computational study published in 2020.
• The framework quantitatively factors performance into three distinct components: sensorimotor speed, goal setting, and motivation.
• Findings link specific computational components (e.g., goal setting via distance from stop sign) to clinical depression severity.

Limitations

• The dataset size, number of participants, and specific column structure are not provided.
• Data is from a single, novel computer-simulated driving experiment, which may limit generalizability to real-world behaviors.
• Temporal coverage is limited to the time of the original study, with no indication of ongoing updates.

Provenance

Source

Dryad digital repository.

Collection Method

Data collected from a computer-simulated driving experiment as part of a research study using inverse optimal control.

Time Range

Data corresponds to the 2020 publication date; specific experiment dates are unknown.

Freshness

Last updated in June 2020.