Resumen de Calibration of Constraint Promotion Does Not Help with Learning Variation in Stochastic Optimality Theory

Giorgio Magri, Benjamin Storme

• The Calibrated Error-Driven Ranking Algorithm (CEDRA; Magri 2012) is shown to fail on two test cases of phonologically conditioned variation from Boersma and Hayes 2001. The failure of the CEDRA raises a serious unsolved challenge for learnability research in stochastic Optimality Theory, because the CEDRA itself was proposed to repair a learnability problem (Pater 2008) encountered by the original Gradual Learning Algorithm. This result is supported by both simulation results and a detailed analysis whereby a few constraints and a few candidates at a time are recursively “peeled off” until we are left with a “core” small enough that the behavior of the learner is easy to interpret.