Figure 1
From: Visual features are processed before navigational affordances in the human brain
(A) EEG paradigm. Participants viewed 50 images of indoor scenes and were asked to mentally plan possible exit paths through the scenes. On interspersed catch trials participants had to respond whether the exit path displayed on the screen corresponded to any of the exit paths from the previous trial. (B) EEG RDMs. We computed RDMs for each EEG time point (every 10 ms from − 200 to + 800 ms with respect to image onset). (C) DNN RDMs. We calculated RDMs from the activations extracted from the 4th block and output layer of a ResNet50 DNN trained on 2D, 3D and semantic tasks. (D) NAM model and RDM5. (E) Variance partitioning. We calculated the unique EEG variance explained by each of the models, revealing different temporal activation patterns. Lines below the plots indicate significant times using t-test (FDR corrected p < 0.05). (F) Peak latencies of different models. Error bars indicate the \(95\%\) confidence interval. For significance testing we applied bootstrapping followed by FDR correction. We found no significant differences between the correlation peak latency between 2D and 3D models, or 3D and semantic models. However there were significant differences between 2D and semantic models (\(p=0.0015\)), 2D and NAM models (\(p=0.0015\)), 3D and NAM models (\(p=0.045\)), and semantic and NAM models (\(P=0.0015\)).
