Abstract
In crowding, neighboring elements impede the perception of a target. Surprisingly, increasing the number of neighboring elements can decrease crowding, i.e., lead to uncrowding (Manassi, 2015). Here, we used fMRI to investigate the cortical locus of (un)crowding. The experiment consisted of seven conditions: (1) target only (eight circular target gratings surrounding a central fixation dot, tilted either clockwise (CW) or counterclockwise (CCW)), (2) 2-flanker (each of the 8 targets was flanked by an inside and outside vertically-oriented grating), (3) annulus-flanker (inside and outside flankers connected into annuli), (4) 4-flanker (one inside and three outside gratings), and (5-7) control conditions corresponding to conditions (2-4) with targets removed. Participants were asked either to indicate whether target gratings were tilted CW or CCW by pushing one of two buttons or to push a button randomly if targets were not present. Target discrimination was highest in the target only condition, followed by the annulus-flanker, 4-flanker and 2-flanker conditions, respectively. As crowding is known to attenuate the BOLD response, we predicted that the percent signal change (PSC) closely reflects the behavioral results (successive decrease in target identification from annulus-flanker to 4-flanker to 2-flanker) in brain areas underlying the crowding effect. The PSC was calculated for each subject, each region of interest (target-activated areas in V1–V4 and LOC) and each of the conditions of interest. In fMRI, crowding and uncrowding effects were present throughout areas V1–V4 and LOC, as indicated by comparisons of PSCs in the target only versus 2-flanker and 4-flanker conditions. However, the more fine-grained differences between the 2-flanker condition and the 4-flanker and annulus-flanker conditions were only present in V1, V4 and LOC. The expected successive decrease in PSC from annulus-flanker to 4-flanker to 2-flanker was only observed in the LOC, reflecting uncrowding.
Meeting abstract presented at VSS 2017