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Katrine Sand, Thomas Habekost, Anders Petersen, Randi Starrfelt; Is word recognition crowded in pure alexia?. Journal of Vision 2017;17(10):1037. doi: 10.1167/17.10.1037.
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Pure alexia is a selective deficit in reading, which arises following damage to the left ventral occipito-temporal cortex. Crowding, the inability to recognise objects in a clutter, has recently been hypothesised to be the underlying deficit of apperceptive visual agnosia1. Crowding normally occurs in peripheral vision, and we therefore tested whether the performance with words at the centre of fixation in a pure alexic patient (LK) is indeed similar to the performance of matched controls in the peripheral visual field. Using an accuracy-based word recognition task with brief, masked exposures, we tested word processing in LK and 24 matched controls. LK was tested in central vision, while the controls were tested in central vision and 4.6 degrees to the right of fixation. LK was significantly impaired on visual word recognition in the central visual field but there was no significant difference when comparing LK's performance in central vision and the performance of the controls at 4.6 degrees. The equivalent eccentricity1 was calculated by estimating the linear relationship between the mean performance across the exposure durations in the central and peripheral condition in the control group. Based on this, we found that the equivalent eccentricity of LK corresponds to 7.4 visual degrees, indicating that her visual word recognition is as bad as normal word recognition would be with stimuli presented 7-8 degrees from fixation. Our findings indicate that word recognition in pure alexia may be impaired by the same processing limitations that makes normal vision less efficient in the periphery than at fixation. Leaning on a recent proposal from Martelli et al.1, we suggest that central vision is crowded in pure alexia, and that this may at least partly explain the reading deficit. 1) Martelli et al. (2015). Journal of vision. 15(12): 921
Meeting abstract presented at VSS 2017
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