Numeral terms and the predictive potential of Bayesian updating

In the experiment described in the paper Noah Goodman & Andreas Stuhlmüller. 2013. Knowledge and im-plicature: Modeling language understanding as social cognition. 5(1). 173–184, empirical support was provided for the predictive power of the Rational Speech Act (RSA) model concerning the interpretation of utterances employing numerals in uncertainty contexts. The RSA predicts a Bayesian interdependence between beliefs about the probability distribution of the occurrence of an event prior to receiving information and the updated probability distribution after receiving information. In this paper we analyze whether the RSA is a descriptive or a normative model. We present the results of two analogous experiments carried out in Polish. The first experiment does not replicate the original empirical results. We find that this is due to different answers on the prior probability distribution. However, the model predicts the different results on the basis of different collected priors: Bayesian updating predicts human reasoning. By contrast, the second experiment, where the answers on the prior probability distribution are as predicted, is a replication of the original results. In light of these results we conclude that the RSA is a robust, descriptive model, however, the experimental assumptions pertaining to the experimental setting adopted by Goodman and Stuhlmüller are normative.