Exemplar Frequency Affects Unsupervised Learning of Shapes

Exposure to the spatiotemporal statistics of the world is thought to have a profound effect on shaping the response properties of the visual cortex and our visual experience. Here we ask whether subjects’ discrimination performance on a set of parameterized shapes changes as a function of the distribution with which the shapes appear in an unsupervised paradigm. During training, subjects performed a fixation task while shapes drawn from a single axis of a parameterized shape space appeared in the background. The frequency with which individual shapes appeared was determined by imposing a normal distribution centered on the middle of the shape axis. Comparison of performance on a shape discrimination task pre and post training showed that subjects' d-prime increased as a function of the frequency with which the exemplars appeared despite the lack of feedback and engagement in a simultaneous task not directed at the shapes. Performance on an untrained set of shapes was largely unchanged across the two testing sessions. This suggests that the visual system may optimize representations by fitting itself to the distribution of experienced exemplars even without feedback, providing the most discriminative power where examples are most likely to occur.