Our work reveals a sharp trade-off between predictive accuracy and model identifiability. Flexible mappings maximize predictivity, but blur the distinction between competing computational hypotheses.

Each simulation worked like this: (1) pick one model from 20 candidate NNs and fit it to human responses; (2) sample a synthetic dataset from that model using NEW triplets; (3) test all 20 models on this generated data, measuring cross-validated prediction accuracy.