Table 4.

Differences in the group-average classification accuracies between the full-model minus the left-out model (together analysis)

	Average	Faces	Fruits	Letters	Vehicles
R FFA	0.45–0.39 = 0.052	0.58–0.46 = 0.124	0.34–0.31 = 0.027	0.48–0.44 = 0.045	0.38–0.37 = 0.011
PPA	0.45–0.43 = 0.012	0.58–0.56 = 0.019	0.34–0.33 = 0.006	0.48–0.49 = −0.004	0.38–0.35 = 0.028
LOC	0.45–0.44 = 0.009	0.58–0.58 = −0.003	0.34–0.31 = 0.033	0.48–0.49 = −0.008	0.38–0.36 = 0.012
L VWFA	0.45–0.44 = 0.007	0.58–0.57 = 0.014	0.34–0.33 = 0.008	0.48–0.48 = 0.002	0.38–0.37 = 0.006
V1	0.45–0.45 = −0.006	0.58–0.58 = 0	0.34–0.35 = −0.009	0.48–0.49 = −0.009	0.38–0.38 = −0.005
M1/2	0.45–0.45 = −0.002	0.58–0.58 = −0.001	0.34–0.34 = 0.003	0.48–0.49 = −0.004	0.38–0.38 = −0.008
A1/2	0.45–0.45 = −0.005	0.58–0.59 = −0.013	0.34–0.32 = 0.017	0.48–0.51 = −0.028	0.38–0.37 = 0.004

Values are indicated for each region (rows) and category (columns). See Table 2 for abbreviation definitions.