Human Behavioral Inhibition Data from Virtual Anxiety Experiments

Experimental data from four human studies using a virtual computer game to model anxiety-like behavioral inhibition. The data supports analysis of how inhibition depends on threat probability and magnitude, and its correlation with individual trait anxiety scores. The dataset is associated with a 2019 publication applying Bayesian Decision Theory to rodent and human anxiety models.

Use Cases

• Analyze the linear dependence of behavioral inhibition on threat probability and magnitude across four human experiments.
• Model individual differences by correlating behavioral inhibition measures with participant trait anxiety scores.
• Investigate the timing of inhibition relative to motor execution using pre-motor response data from the virtual game.
• Test computational models of cost-minimization strategies in temporally correlated threat-reward environments using the experimental trial data.

Strengths

• Data is derived from four controlled human experiments, providing a basis for testing specific computational hypotheses.
• Findings are validated by showing individual trait anxiety scores predict behavioral inhibition.
• The dataset is openly available under a CC0 1.0 license, facilitating reuse and analysis.

Limitations

• The specific data structure, column names, and sample size (rows) are not provided in the input description.
• The dataset's temporal coverage and geographic scope for the human participants are unspecified.
• As the data is from a 2019 publication, it may not reflect the most recent research methodologies.

Provenance

Source

Dryad digital repository.

Collection Method

Data harvested from a standard rodent anxiety model (operant conflict test) and four human experiments using a virtual computer game.

Freshness

Last updated in June 2019.