Neurons in cortical sensory areas respond selectively to sensory stimuli, and the preferred stimulus typically varies among neurons so as to continuously span the sensory space. However, some neurons reflect sensory features that are learned or task dependent. For example, neurons in the lateral intraparietal area (LIP) reflect learned associations between visual stimuli. One might expect that roughly even numbers of LIP neurons would prefer each set of associated stimuli. However, in two associative learning experiments and a perceptual decision experiment, we found striking asymmetries: nearly all neurons recorded from an animal had a similar order of preference among associated stimuli. Behavioral factors could not account for these neuronal biases. A recent computational study proposed that population-firing patterns in parietal cortex have one-dimensional dynamics on long timescales, a possible consequence of recurrent connections that could drive persistent activity. One-dimensional dynamics would predict the biases in selectivity that we observed.