Extended Data Figure 2. Additional DCM Analysis With Larger Model Space.

For simplicity, we only compared two DCM models in Fig 4. These two models, however, have limitations, given that they were merely built from preceding PPI results. For instance, the EVC→OFA connectivity is theoretically important but was not significant in the left hemisphere (LH) of PPI results (that’s why we did not included it in the original feed-forward model). In addition, one might also be interested in exploring the relative contribution of each recurrent connection to the right hemisphere (RH) face processing. To address these questions, we built larger model space with seven competing models (feedforward models in red colour and recurrent ones in blue colour). (A) The first two models were the ones we used in Fig 4. Model 1 was feedforward (based on PPI results in LH) and Model 2 was recurrent (based on PPI results in RH). Since Model 1 had no direct feedforward connectivity from EVC to OFA, we next built Model 3 with additional EVC→OFA. Model 4 to 7 were recurrent models modified from Model 2. As there were four recurrent connections in Model 2, we removed one feedback connection each time to examine their respective importance to the recurrent model (i.e. how much the model performance would suffer in the absence of a particular recurrent connection). Model 4 removed EVC←OFA; Model 5 removed EVC←FFA; Model 6 removed EVC←STS; and Model 7 removed OFA←STS. (B) Bayesian model selection (both FFX and RFX) indicates the EVC→OFA connection is important for the feedforward model, as Model 3 performs better than Model 1 in LH. This is consistent with the Haxby model suggesting the critical role of the OFA receiving information from EVC to initiate the face processing. In addition, both feedforward models perform better than any recurrent models in LH, whereas the PPI-derived recurrent model (Model 2) performed the best in RH. These results accord well with our results in Fig 4. Moreover, among all four recurrent connections, two feedback connections (EVC←OFA, OFA←STS) seem to be particularly important for RH recurrent processing, since removal of either one can lead to enormous drop of model performance (i.e. Model 4 and 7). In sum, this additional DCM analysis supports our claims in Fig 4: the LH is dominant with feedforward face processing whereas the RH is dominant with recurrent processing.