Here, we test whether a model of individual objects—based on combining the HMax computational model of vision with semantic-feature information—can account for and predict time-varying neural activity recorded with magnetoencephalography. We show that combining HMax and semantic properties provides a better account of neural object representations compared with the HMax alone, both through model fit and classification performance. Our results show that modeling and classifying individual objects is significantly improved by adding semantic-feature information beyond ∼200 ms.
Creating concepts from converging features in human cortex→
/In summary, this study has found that the top-down retrieval of object knowledge leads to activation of shape-specific and color-specific codes in relevant specialized visual areas, as well as an object-identity code within left ATL. Moreover, the presence of identity information in left ATL was more likely when both shape and color information was simultaneously present in respective feature regions. The strength of this dependency predicted the correspondence between top-down and bottom-up activity patterns in the ATL. These findings support proposals that the ATL integrates featural information into a less feature-dependent representation of identity.
Semantic advantage for learning new phonological form representations ($)→
/Semantic associations affect MMN responses to consolidated newly-learned words.
