Identification of facial images in peripheral vision

Scaling of letter size and contrast equalises perception across eccentricities and set sizes

Double E(2)N(2) scaling, i.e. magnifying size and contrast, allows modelling of the deterioration of face recognition performance with increasing eccentricity (E) and the size (N) of the set from which a target face has to be identified. E(2) and N(2) values represent the eccentricities and set sizes at which stimulus size and contrast must double in order to keep performance unchanged, whilst parameter K represents the multiplicative interaction between E and N. In the current study we investigated whether double E(2)N(2) scaling can model performance deterioration with increasing eccentricity and set size in letter perception too. Contrast sensitivity for letter perception was investigated as a function of letter size at N=1-8 and E=0 degrees -10 degrees. The superimposition of contrast sensitivity functions produced two scaling surfaces, one for letter size and another for contrast, which allowed modelling of the changes in letter perception with increasing E and N. With increasing eccentricity/set size the change of scale was much faster for contrast than letter size. Thus, in letter perception, contrast scaling was more important than spatial scaling. When compared with face perception, the change of spatial scale with increasing eccentricity was slower for letters whereas the change of contrast scale was similar for both. With increasing set size the changes of both spatial and contrast scales are faster for faces. In spatial scaling the interaction between eccentricity and set size was similar for letters and faces whereas in contrast scaling letters showed no interaction. Thus, letter perception was less affected by eccentricity and set size than face perception.

Eccentricity bias as an organizing principle for human high-order object areas

We have recently proposed a center-periphery organization based on resolution needs, in which objects engaging in recognition processes requiring central-vision (e.g., face-related) are associated with center-biased representations, while objects requiring large-scale feature integration (e.g., buildings) are associated with periphery-biased representations. Here we tested this hypothesis by comparing the center-periphery organization with activations to five object categories: faces, buildings, tools, letter strings, and words. We found that faces, letter strings, and words were mapped preferentially within the center-biased representation. Faces showed a hemispheric lateralization opposite to that of letter strings and words. In contrast, buildings were mapped mainly to the periphery-biased representation, while tools activated both central and peripheral representations. The results are compatible with the notion that center-periphery organization allows the optimal allocation of cortical magnification to the specific requirements of various recognition processes.