Monkey Neurophysiology and RNN Models for Decision-Making Analysis

Juliette Bénon published this dataset in 2026. It contains processed neurophysiological and behavioral data from two macaque monkeys, originally collected by Hunt et al. (2018), alongside outputs from recurrent neural network model training across six procedures. The dataset supports analysis of irrational decisions and robustness constraints in value computations.

Use Cases

• Compare computational models with real neurophysiological data based on processed monkey behavior.
• Analyze the impact of biological constraints like energetic budget on model rationality based on RNN training procedures.
• Study robustness to unit lesions in neural network models based on the specified training procedures.
• Investigate irrational decision patterns based on the combined behavioral and model data.

Strengths

• Includes data from two macaque monkeys, providing a comparative basis.
• Contains outputs from six distinct recurrent neural network training procedures.
• Dataset size is 9.3 GB, indicating substantial content.
• Accompanied by analysis code available on GitHub.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

figshare

Collection Method

Processed from original data collected by Hunt et al. (2018) and outputs from model training.

Freshness

Last updated 2026-05-25 14:04:43; freshness should be verified.