Human Motor Adaptation Data from Mouse Cursor Movements

A collection of empirical data from 12 able-bodied subjects performing goal-directed cursor movements with a computer mouse. It supports research on trial-by-trial adaptation rates and the analysis of steady-state learning phases. The data was used to demonstrate biases in conventional adaptation metrics and to validate a proposed Bayesian estimation approach.

Use Cases

• Analyze trial-by-trial adaptation rates from cursor movement sequences to identify biases in linear regression coefficients.
• Apply the proposed Bayesian model to movement data to estimate more consistent adaptation rates during steady-state learning conditions.
• Compare perturbation adaptation rates from catch trials with trial-by-trial adaptation rates calculated from different learning phases.
• Identify stable learning periods within movement result sequences to decouple concurrent motor learning processes.

Strengths

• Data from 12 human subjects provides a basis for empirical validation.
• Includes results from both simulation and empirical testing to support methodological claims.
• Dataset is associated with a peer-reviewed methodological analysis published in 2020.

Limitations

• Small sample size of 12 subjects limits statistical power and generalizability.
• The dataset scope is focused on a specific methodological critique, not broad motor behavior.
• Specific data structure, column details, and sample size are not provided in the input.

Provenance

Source

Dryad digital repository.

Collection Method

Data collected from an experiment where 12 able-bodied subjects moved a cursor on a screen using a computer mouse.

Freshness

Last updated in June 2020.