Taming the beast: extracting generalizable knowledge from computational models of cognition

- Computational models extract general principles from specific data.
- However, such principles are conditional on modeling assumptions.
- Faulty assumptions can bias findings and mislead data interpretation.
- The modeler's toolkit includes several techniques to avoid this pitfall.
- These techniques are critical to broadly interpret computational findings.

Generalizing knowledge from experimental data requires constructing theories capable of explaining observations and extending beyond them. Computational modeling offers formal quantitative methods for generating and testing theories of cognition and neural processing. These techniques can be used to extract general principles from specific experimental measurements, but introduce dangers inherent to theory: model-based analyses are conditioned on a set of fixed assumptions that impact the interpretations of experimental data. When these conditions are not met, model-based results can be misleading or biased. Recent work in computational modeling has highlighted the implications of this problem and developed new methods for minimizing its negative impact. Here we discuss the issues that arise when data is interpreted through models and strategies for avoiding misinterpretation of data through model fitting.