The temporal evolution of conceptual object representations revealed through models of behavior, semantics and deep neural networks

Neuroimage. 2018 Sep;178:172-182. doi: 10.1016/j.neuroimage.2018.05.037. Epub 2018 May 17.


Visual object representations are commonly thought to emerge rapidly, yet it has remained unclear to what extent early brain responses reflect purely low-level visual features of these objects and how strongly those features contribute to later categorical or conceptual representations. Here, we aimed to estimate a lower temporal bound for the emergence of conceptual representations by defining two criteria that characterize such representations: 1) conceptual object representations should generalize across different exemplars of the same object, and 2) these representations should reflect high-level behavioral judgments. To test these criteria, we compared magnetoencephalography (MEG) recordings between two groups of participants (n = 16 per group) exposed to different exemplar images of the same object concepts. Further, we disentangled low-level from high-level MEG responses by estimating the unique and shared contribution of models of behavioral judgments, semantics, and different layers of deep neural networks of visual object processing. We find that 1) both generalization across exemplars as well as generalization of object-related signals across time increase after 150 ms, peaking around 230 ms; 2) representations specific to behavioral judgments emerged rapidly, peaking around 160 ms. Collectively, these results suggest a lower bound for the emergence of conceptual object representations around 150 ms following stimulus onset.

Trial registration: NCT00001360.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, N.I.H., Intramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Cerebral Cortex / physiology*
  • Concept Formation / physiology*
  • Deep Learning
  • Female
  • Functional Neuroimaging / methods*
  • Generalization, Psychological / physiology*
  • Humans
  • Magnetoencephalography / methods*
  • Male
  • Middle Aged
  • Models, Theoretical
  • Neural Networks, Computer*
  • Pattern Recognition, Visual / physiology*
  • Time Factors
  • Young Adult

Associated data