Dynamic representation of multidimensional object properties in the human brain

Our visual world consists of an immense number of unique objects and yet, we are easily able to identify, distinguish, interact, and reason about the things we see within a few hundred milliseconds. This requires that we integrate and focus on a wide array of object properties to support diverse behavioral goals. In the current study, we used a large-scale and comprehensively sampled stimulus set and developed an analysis approach to determine if we could capture how rich, multidimensional object representations unfold over time in the human brain. We modelled time-resolved MEG signals evoked by viewing single presentations of tens of thousands of object images based on millions of behavioral judgments. Extracting behavior-derived object dimensions from similarity judgments, we developed a data-driven approach to guide our understanding of the neural representation of the object space and found that every dimension is reflected in the neural signal. Studying the temporal profiles for different object dimensions we found that the time courses fell into two broad types, with either a distinct and early peak ([~]125 ms) or a slow rise to a late peak ([~]300 ms). Further, early effects were stable across participants, in contrast to later effects which showed more variability, suggesting that early peaks may carry stimulus-specific and later peaks more participant-specific information. Dimensions with early peaks appeared to be primarily visual dimensions and those with later peaks more conceptual, suggesting that conceptual representations are more variable across people. Together, these data provide a comprehensive account of how behavior-derived object properties unfold in the human brain and form the basis for the rich nature of object vision.