Putting Psychology to the Test: Rethinking Model Evaluation Through Benchmarking and Prediction.

Area of Science:

• Psychology
• Scientific Methodology

Background:

• Psychology lacks standardized metrics for evaluating scientific models and theories.
• Current evaluation practices are often inconsistent and fail to assess model generalizability, hindering the reliable discrimination between effective and ineffective models.

Purpose of the Study:

• To advocate for the adoption of common benchmarks in psychology for model evaluation.
• To propose principles for effective benchmark design and implementation.
• To address potential challenges in establishing community-wide evaluation standards.

Main Methods:

• Drawing inspiration from machine learning and statistical genetics.
• Discussing principles for benchmark utility.
• Identifying practical steps for community adoption.
• Addressing implementation concerns.

Main Results:

• Lack of reliable communal metrics for model performance evaluation in psychology.
• Idiosyncratic evaluation practices with shortcomings like poor generalizability assessment.
• Potential for common benchmarks to improve model discrimination and foster progress.

Conclusions:

• Implementing common evaluation benchmarks is crucial for advancing psychological science.
• Consensus on benchmarks will enhance the practical utility and reliability of scientific models.
• Adoption of benchmarks can lead to more robust and cumulative psychological research.