Probabilistic prediction is a central process in language comprehension. Properties of probability distributions over predictions are often difficult to study in natural language. To obtain precise control over these distributions, we created artificial languages consisting of sequences of shapes. The languages were constructed to vary the uncertainty of the probability distribution over predictions as well as the probability of the predicted item. Participants were exposed to the languages in a self-paced presentation paradigm, which provides a measure of processing difficulty at each element of a sequence. There was a robust pattern of graded predictability: shapes were processed faster the more predictable they were, as in natural language. Processing times were also affected by the uncertainty (entropy) over predictions at the point at which those predictions were made; this effect was less consistent, however.