HIGHLIGHTS
- What: The authors examined the use of theory-independent models specifically recurrent neural networks (RNN) to classify the source of a predictive gap in the observed data of a single individual. While this approach has already led to substantial scientific findings (Montague et_al, 2012; Rescorla, 1972 ), it still faces a major challenge since the true underlying model is always left unknown (Box, 1979 ). The authors aim to leverage the network`s high flexibility and predictive capability to address the problem of identifying misspecification in theoretical computational models, using two different estimates: 1. The authors propose that the . . .
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