Local response heterogeneity indexes experience-based neural differentiation in reading.

The ability to read requires learning letter-string representations whose neural codes would be expected to vary depending on the amount of experience that an individual has with reading them. Motivated by sparse coding theories (e.g., Rolls and Tovee, 1995; Olshausen and Field, 1996), recent work has demonstrated that better-learned relative to less well-learned neural representations are associated with more strongly differentiated, locally heterogeneous blood oxygenation level dependent (BOLD) responses (e.g., Jiang et al., 2013). Here we report a novel analysis method we call local heterogeneity regression (Local-Hreg) that quantifies the cross-voxel heterogeneity of BOLD responses, thereby providing a sensitive and methodologically flexible method for quantifying the local neural differentiation of neural representations. In a study of literate adults, we applied Local-Hreg to fMRI data obtained when participants read letter strings that varied in their frequency of occurrence in the written language. Consistent with previous research identifying the left ventral occipitotemporal cortex (vOTC) as a key site for orthographic representation in reading and spelling, we found that the cross-voxel heterogeneity of neural responses in this region varies according to the frequency with which the written letter strings have been experienced. This work provides a novel approach for examining the local differentiation of neural representations, and demonstrates that well-learned words have greater representational differentiation than less well-learned or unfamiliar words.