Using fMRI to link crowding to hV4

Burchell, Augustin; Benson, Noah C.; Zhou, Jing; Winawer, Jonathan; Pelli, Denis G.

doi:10.1167/19.10.14a

Cited by 2 publications

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“…Here, we too use letters, because they do not require training, and provide a large number of stimulus alternatives, which speeds threshold estimation in laboratory and clinical testing (Pelli et al, 1988). Whether crowding can be explained by neural computations in any particular cortical location remains unknown, but several candidate areas have been suggested: V1 , V2 (Freeman & Simoncelli, 2011;He et al, 1996), V3 (Bi et al, 2009;Tyler & Likova, 2007), V4 (Burchell et al, 2019;Liu et al, 2009;Motter, 2006;Zhou et al, 2017) and higher order areas (Aghdaee, 2005;Louie et al, 2007). Recently, crowding distances were found to differ when stimulus properties were tuned to stimulate either the parvo or magno cellular pathway (Atilgan et al, 2020).…”

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confidence: 99%

The Bouma law accounts for crowding in fifty observers

Kurzawski

Burchell

Thapa

et al. 2021

Preprint

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Crowding is the failure to recognize an object due to surrounding clutter. Its strength varies across the visual field and individuals. To characterize the statistics of crowding—ultimately to relate psychophysics of crowding to physiology—we measured radial crowding distance and acuity of 105 observers along the four cardinal meridians of the visual field. Fitting the well-known Bouma law — crowding distance depends linearly on radial eccentricity — explains 52% of the variance in log crowding distance, cross-validated. Our enhanced Bourma model, with factors for observer, meridian, and target kind, explains 72% of the variance, again cross-validated. The meridional factors confirm previously reported asymmetries. We find a 0.62 horizontal:vertical advantage, a 0.92 lower:upper advantage, and a 0.82 right:left advantage. Crowding distance and acuity have a correlation of 0.41 at the fovea, which drops to 0.23 at ±5 deg along the horizontal midline. Acuity and crowding represent the size and spacing limits of perception. Since they are dissociated in clinical populations (Song et al., 2014; Strappini et al., 2017) and shown here to be only moderately correlated in our sample of mostly university students, clinical testing to predict real-world performance should consider measuring both. In sum, enhancing the Bouma law with terms for meridian, observer, and target kind provides an excellent fit to our 105-person survey of crowding.

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