Rectilinear Edge Selectivity Is Insufficient to Explain the Category Selectivity of the Parahippocampal Place Area

Abstract

The parahippocampal place area (PPA) is one of several brain regions that respond more strongly to scenes than to non-scene items such as objects and faces. The mechanism underlying this scene-preferential response remains unclear. One possibility is that the PPA is tuned to low-level stimulus features that are found more often in scenes than in less-preferred stimuli. Supporting this view, Nasr et al. (2014) recently observed that some of the stimuli that are known to strongly activate the PPA contain a large number of rectilinear edges. They further demonstrated that PPA response is modulated by rectilinearity for a range of non-scene images. Motivated by these results, we tested whether rectilinearity suffices to explain PPA selectivity for scenes. In the first experiment, we replicated the previous finding of modulation by rectilinearity in the PPA for arrays of 2-d shapes. However, two further experiments failed to find a rectilinearity effect for faces or scenes: high-rectilinearity faces and scenes did not activate the PPA any more strongly than low-rectilinearity faces and scenes. Moreover, the categorical advantage for scenes vs. faces was maintained in the PPA and two other scene-selective regions—the retrosplenial complex (RSC) and occipital place area (OPA)—when rectilinearity was matched between stimulus sets. We conclude that selectivity for scenes in the PPA cannot be explained by a preference for low-level rectilinear edges.

Object representations in the human brain reflect the co-occurrence statistics of vision and language

“…In the case of scene-selective cortex, previous work has shown that scene regions respond to several high-level properties of objects, including contextual-association strength, real-world size, landmark suitability, spatial definition, and interaction envelope 11,21,[48][49][50] . Scene-selective regions also respond to several mid-level properties of images, including high spatial frequencies, rectilinearity, and cardinal orientations [51][52][53][54] . Many of these high-level object properties appear to covary in the visual environment and explain similar variance in fMRI responses 20 , and they may also covary with mid-level image properties 55,56 .…”

Making Sense of Real-World Scenes

“…Much of the research on humans has focused on establishing what specific properties each scene-selective region is sensitive to. For example, responses in PPA have been reported to reflect a wide range of properties, including, i) low-level properties, such as spatial frequency [87–90], orientation [91], texture [92], rectilinearity [93] [, but see, 94], and contour junctions [95]; ii) object properties, such as identity [96], size [97], space diagnosticity [98], co-occurrence [99], and object ensembles [92]; iii) 3D layout, such as size of a space [100], spatial expanse [i.e., open or closed, 96,101,102], distance [102], and boundaries [103]; and iv) high-level properties, such as semantic category [104,105], contextual associations [106,107], and knowledge of scene correspondences [108]. Sensitivity to some of these properties is shared by both OPA and RSC, but in contrast to PPA, they show greater sensitivity to egocentric distance [109] and sense [i.e., left versus right mirror views, 110].…”

Two distinct scene processing networks connecting vision and memory

“…Even low-level manipulations of spatial frequency Kauffmann et al, 2015;Watson et al, 2016) or rectilinearity (Nasr et al, 2014) can drive responses in these regions. Higher-level visual features also drive response patterns in these regions (Bryan et al, 2016), and they are hypothesized to be involved in extracting visual environmental features that can be used for navigation (Marchette et al, 2015;Julian et al, 2016;Kamps et al, 2016). However, neither OPA nor posterior PPA show reliable familiarity effects (Epstein et al, 2007b; see further discussion below).…”

“…Since the anterior scene network overlaps with default mode regions, while the posterior scene network does not, we predict that the anterior network should be more connected to the hippocampus (Buckner et al, 2008). To test this hypothesis, we measured the functional correlation at rest between mean hippocampal activity and the mean activity in each parcel within the posterior and anterior scene networks.…”