People can rapidly judge the average size of a collection of objects with considerable accuracy. In this study, we tested whether this size-averaging process relies on relatively early object representations or on later object representations that have undergone iterative processing. We asked participants to judge the average size of a set of circles and, in some conditions, presented two additional circles that were either smaller or larger than the average. The additional circles were surrounded by four-dot masks that either lingered longer than the circle array, preventing further processing with object substitution masking (OSM), or disappeared simultaneously with the circle array, allowing the circle representation to reach later visual processing stages. Surprisingly, estimation of average circle size was modulated by both visible circles and circles whose visibility was impaired by OSM. There was also no correlation across participants between the influence of the masked circles and susceptibility to OSM. These findings suggest that relatively early representations of objects can contribute to the size-averaging process despite their reduced visibility.

Traditional models of recognition and categorization proceed from registering low-level features, perceptually organizing that input, and linking it with stored representations. Recent evidence, however, suggests that this serial model may not be accurate, with object and category knowledge affecting rather than following early visual processing. Here, we show that the degree to which an image exemplifies its category influences how easily it is detected. Participants performed a two-alternative forced-choice task in which they indicated whether a briefly presented image was an intact or phase-scrambled scene photograph. Critically, the category of the scene is irrelevant to the detection task. We nonetheless found that participants “see” good images better, more accurately discriminating them from phase-scrambled images than bad scenes, and this advantage is apparent regardless of whether participants are asked to consider category during the experiment or not. We then demonstrate that good exemplars are more similar to same-category images than bad exemplars, influencing behavior in two ways: First, prototypical images are easier to detect, and second, intact good scenes are more likely than bad to have been primed by a previous trial.

In a phenomenon called subitizing, we can immediately generate exact counts of small collections (one to three objects), in contrast to larger collections, for which we must either create rough estimates or serially count. A parsimonious explanation for this advantage for small collections is that noisy representations of small collections are more tolerable, due to the larger relative differences between consecutive numbers (e.g., 2 vs. 3 is a 50 % increase, but 10 vs. 11 is only a 10 % increase). In contrast, the advantage could stem from the fact that small-collection enumeration is more precise, relying on a unique mechanism. Here, we present two experiments that conclusively showed that the enumeration of small collections is indeed "superprecise." Participants compared numerosity within either small or large visual collections in conditions in which the relative differences were controlled (e.g., performance for 2 vs. 3 was compared with performance for 20 vs. 30). Small-number comparison was still faster and more accurate, across both "more-fewer" judgments (Exp. 1), and "same-different" judgments (Exp. 2). We then reviewed the remaining potential mechanisms that might underlie this superprecision for small collections, including the greater diagnostic value of visual features that correlate with number and a limited capacity for visually individuating objects.

Humans efficiently grasp complex visual environments, making highly consistent judgments of entry-level category despite their high variability in visual appearance. How does the human brain arrive at the invariant neural representations underlying categorization of real-world environments? We here show that the neural representation of visual environments in scene-selective human visual cortex relies on statistics of contour junctions, which provide cues for the three-dimensional arrangement of surfaces in a scene. We manipulated line drawings of real-world environments such that statistics of contour orientations or junctions were disrupted. Manipulated and intact line drawings were presented to participants in an fMRI experiment. Scene categories were decoded from neural activity patterns in the parahippocampal place area (PPA), the occipital place area (OPA) and other visual brain regions. Disruption of junctions but not orientations led to a drastic decrease in decoding accuracy in the PPA and OPA, indicating the reliance of these areas on intact junction statistics. Accuracy of decoding from early visual cortex, on the other hand, was unaffected by either image manipulation. We further show that the correlation of error patterns between decoding from the scene-selective brain areas and behavioral experiments is contingent on intact contour junctions. Finally, a searchlight analysis exposes the reliance of visually active brain regions on different sets of contour properties. Statistics of contour length and curvature dominate neural representations of scene categories in early visual areas and contour junctions in high-level scene-selective brain regions.

Images of iconic buildings, such as the CN Tower, instantly transport us to specific places, such as Toronto. Despite the substantial impact of architectural design on people’s visual experience of built environments, we know little about its neural representation in the human brain. In the present study, we have found patterns of neural activity associated with specific architectural styles in several high-level visual brain regions, but not in primary visual cortex (V1). This finding suggests that the neural correlates of the visual perception of architectural styles stem from style-specific complex visual structure beyond the simple features computed in V1. Surprisingly, the network of brain regions representing architectural styles included the fusiform face area (FFA) in addition to several scene-selective regions. Hierarchical clustering of error patterns further revealed that the FFA participated to a much larger extent in the neural encoding of architectural styles than entry-level scene categories. We conclude that the FFA is involved in fine-grained neural encoding of scenes at a subordinate-level, in our case, architectural styles of buildings. This study for the first time shows how the human visual system encodes visual aspects of architecture, one of the predominant and longest-lasting artefacts of human culture.