Category-specific topographies across fast-mapping. A) Mean action vs object word CA cells across areas. Emerging category-specific topographical distributions of CAs across learning steps. The y-axis captures the mean number of cells belonging to CAs of that word type, which are calculated specific to each area, at each learning step indicated on the x-axis. Learning steps run from 1 to 50 (with each vertical white line indicating 10). Error bars reflect SE. The schematic in the top-right corner showing the color-coded areas is adapted from Tomasello et al. 2018. B) Category-specificity between region types. The y-axes reflect the difference in mean number of CA cells between the two word types, which serves as a measure of the degree of category-specificity. The left plot in blue depicts the difference between object and action words in extrasylvian visual regions, with the primary region referring to V1, the secondary to TO, and the hub to AT. The right plot in pink depicts the difference between action and object words in extrasylvian motor regions, with the primary region referring to M1_L, the secondary to PM_L, and the hub to PF_L. The significance indicators (^*) at the legends refer to pairwise comparisons between area types collapsed across learning steps, and P-values have been Bonferroni corrected.