【摘要】A growing body of research in phonology addresses the representation and learning of variable processes and exceptional, lexically conditioned processes. Linzen et al. (2013) present a MaxEnt model with additive lexical scales to account for data exhibiting both variation and exceptionality. In this paper, we implement a learning model for lexically scaled MaxEnt grammars which we show to be successful across a range of data containing patterns of variation and exceptionality. We also explore how the model's parameters and the rate of exceptionality in the data influence its performance and predictions for novel forms.