期刊
PSYCHOLOGICAL REVIEW
卷 113, 期 1, 页码 57-83出版社
AMER PSYCHOLOGICAL ASSOC
DOI: 10.1037/0033-295X.113.1.57
关键词
model comparison; model complexity; MCMC; connectionist modeling
资金
- NIMH NIH HHS [R01-MH57472, R01 MH057472] Funding Source: Medline
- NATIONAL INSTITUTE OF MENTAL HEALTH [R01MH057472] Funding Source: NIH RePORTER
To model behavior, scientists need to know how models behave. This means learning what other behaviors a model can produce besides the one generated by participants in an experiment. This is a difficult problem because of the complexity of psychological models (e.g., their many parameters) and because the behavioral precision of models (e.g., interval-scale performance) often mismatches their testable precision in experiments, where qualitative, ordinal predictions are the norm. Parameter space partitioning is a solution that evaluates model performance at a qualitative level. There exists a partition on the model's parameter space that divides it into regions that correspond to each data pattern. Three application examples demonstrate its potential and versatility for studying the global behavior of psychological models.
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