Computational mechanisms underlying cortical responses to the affordance properties of visual scenes
Fig 1
Navigational-affordance information is coded in scene-selective visual cortex.
(A) Examples of natural images used in the fMRI experiment. All experimental stimuli were images of indoor environments with clear navigational paths proceeding from the bottom center of the image. (B) In a norming study, an independent group of raters indicated with a computer mouse the paths that they would take to walk through each scene, starting from the red square at the bottom center of the image (far left panel). These data were combined across subjects to create heat maps of the navigational paths in each image (middle left panel). We summed the values in these maps along one-degree angular bins radiating from the bottom center of the image (middle right panel), which produced histograms of navigational probability measurements over a range of angular directions (far right panel). The gray bars in this histogram represent raw data, and the overlaid line indicates the angular data after smoothing. (C) The navigational histograms were compared pairwise across all images to create a model RDM of navigational-affordance coding (top left panel). Right panel shows a two-dimensional visualization of this representational model, created using t-distributed stochastic neighbor embedding (t-SNE), in which the navigational histograms for each condition are plotted within the two-dimensional embedding. RSA correlations were calculated between the model RDM and neural RDMs for each ROI (bottom left panel). The strongest RSA effect for the coding of navigational affordances was in the OPA. There was also a significant effect in the PPA. Error bars represent bootstrap ±1 s.e.m. a.u. = arbitrary units. **p<0.01, ***p<0.001.