Fig. 5

Comparing image-based metrics with object memory. a A comparison of which objects participants drew during Delayed Recall and the objects predicted by a graph-based visual saliency map (GBVS⁴⁰) and Meaning Maps^41,42. The Object Memory Map shows the proportion of participants who drew each object during Delayed Recall; red indicates objects drawn by all participants who remembered a given drawing, whereas white indicates objects drawn by no one, as well as background regions. The Saliency Map shows the saliency scores calculated for each pixel based on the GBVS algorithm, while the Meaning Map shows the average smoothed meaning scores attributed to circular patches taken from each image (see⁴¹). Their corresponding Object Maps show the average saliency and meaning scores across all pixels within a given object region, scaled to a range of 0 to 1. All results reported replicate when Object Maps are instead generated using peak saliency and meaning within an object. b The average maps across all experimental images, averaging (in order) the Object Saliency Maps, Object Meaning Maps, Object Delayed Recall Memory Maps, and Object Immediate Recall Memory Maps. Maps were normalized by number of objects across images at each pixel to take into account the natural spatial distribution of objects. The average vertical value shows the mean pixel value at each y-coordinate (from top pixel 0 to bottom pixel 700) for the four average maps, scaled to a range of 0 to 1. As one can see, while Saliency and Meaning Maps show a central bias, Delayed and Immediate Recall Maps show a tendency towards the lower part of the image