Unveiling the computations underlying behavioral heterogeneity across and around the visual field - PROJECT SUMMARY Visual performance (e.g., contrast sensitivity) varies as a function of eccentricity and polar angle: Performance worsens as eccentricity increases and is better along the horizontal than the vertical meridian (horizontal-vertical anisotropy, HVA) and along the lower- than upper-vertical meridian (vertical meridian asymmetry, VMA) at a fixed eccentricity. Contrast sensitivity and the extent of performance heterogeneity correlate with surface area in early occipital areas. A quantitative account relates the better performance at the fovea than peripheral locations to more cones and larger V1 cortical surface area, but does not suffice to explain polar angle asymmetries. A qualitative account posits that performance differences relate to differential system-level computations throughout the visual field, including efficiency –the ability to extract signal information, the amount of internal noise, and tuning –sensitivity to and selectivity for signal features. Here, we test the main hypothesis that performance differences throughout the visual field are attributed to both quantitatively varying surface area and qualitatively distinct representations and computations. We capitalize on the fact that presenting signals in noise enables quantifying internal noise and observers’ ability to extract and represent task-relevant information: We estimate efficiency and internal noise using the equivalent noise method (Aim1), and derive the perceptual representation of task-relevant features using psychophysical reverse correlation (Aim2&3). To link the quantitative and qualitative accounts, we examine whether the observed difference in performance and perceptual representation can be matched across locations by equating the V1 surface area encoding the stimuli for each individual observer (Aim3). Preliminary data suggest that distinct representations and computations underlie the performance heterogeneity throughout the visual field. Whereas the eccentricity effect may be due to lower internal noise at the fovea than perifovea (Aim1), both HVA and VMA may reflect a higher efficiency (Aim1) and better representation of task-relevant orientations (Aim2) at the horizontal than vertical meridian and at the lower- than upper-vertical meridian, whereas HVA and VMA may reflect differential SF-related computations (Aim2). We further evaluate whether differences in neural correlates go beyond the V1 surface area of each individual observer. We hypothesize that matching the V1 cortical area dedicated to processing the stimuli would eliminate the eccentricity effect, but at most reduce polar angle asymmetries (Aim3). By identifying the computations involved in basic visual tasks across eccentricity and polar angle, results will shed light on how the early visual cortex integrates neural resources to give rise to visual performance. Further, knowledge of the computations underlying varying performance across locations could have translational value for our understanding of visual deficits, and potential for developing human factors applications to optimally present information to observers in user interfaces. My long-term goal is to develop a model that takes into account computations and neural properties that can predict visual performance at various visual field locations.
