Learned categorical perception (CP) occurs when judgments of stimulus similarity or discriminability are altered as a resultof learning to categorize the stimuli; for example, there may be enhanced discrimination of items straddling a categoryboundary or of differences anywhere along a category-relevant dimension. Typical visual learned CP experiments donot test for both kinds of effects or employ control groups receiving exposure to the stimuli comparable to that receivedby category learning groups, rendering the results ambiguous in multiple ways. We will present results from a newexperimental paradigm that is designed to achieve the following important goals: (a) test for and clearly distinguishall known types of boundary and dimension-wide effects considered variants of learned CP and (b) determine whetherobserved changes in performance are actually due to categorization training by comparing them to the changes caused bycomparable stimulus exposure in the absence of category learning.

A preliminary meta-analysis using the p-curve method(Simonsohn, Nelson, & Simmons, 2014) was performed on asubset of the learned categorical perception literature toexplore the robustness of the phenomenon. Only studies usingnovel visual categories and behavioral measures wereincluded. The results strongly suggest that the phenomenon isrobust but that the studies are somewhat underpowered. Weargue that this is problematic because it renders bothstatistically significant and nonsignificant results verydifficult to interpret, which impedes progress inunderstanding the learned CP phenomenon, for example, whyexpansion vs. compression is observed, or boundary vs.dimensional effects. Fortunately, there is a clear solution:conduct studies with greater statistical power.