Fig 2. Navigational-affordance information can be extracted by a feedforward computational model.

(A) Architecture of a deep CNN trained for scene categorization. Image pixel values are passed to a feedforward network that performs a series of linear-nonlinear operations, including convolution, rectified linear activation, local max pooling, and local normalization. The final layer contains category-detector units that can be interpreted as signaling the association of the image with a set of semantic labels. (B) RSA of the navigational-affordance model and the outputs from each layer of the CNN. The affordance model correlated with multiple layers of the CNN, with the strongest effects observed in higher convolutional layers and weak or no effects observed in the earliest layers. This is consistent with the findings of the fMRI experiment, which indicate that navigational affordances are coded in mid-to-high-level visual regions but not early visual cortex. (C) RSA of responses in the OPA and the outputs from each layer of the CNN. All layers showed strong RSA correlations with the OPA, and the peak correlation was in layer 5, the highest convolutional layer. Error bars represent bootstrap ±1 s.e.m. *p<0.05, **p<0.01.