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.

Further analyses showed that linear probing was the culprit. The linear fit warps each model's original feature space, erasing its unique signature and making all aligned models converge toward a human-like representation.

The key dependent measure is how often the data-generating model actually achieves the highest prediction accuracy. The surprising result: even with massive datasets (millions of trials), the best-performing model is often not the right one.

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.