Ventral temporal cortex (VTC) is the latest stage of the ventral "what" visual pathway, which is thought to code the identity of a stimulus regardless of its position or size [1, 2]. Surprisingly, recent studies show that position information can be decoded from VTC [3-5]. However, the computational mechanisms by which spatial information is encoded in VTC are unknown. Furthermore, how attention influences spatial representations in human VTC is also unknown because the effect of attention on spatial representations has only been examined in the dorsal "where" visual pathway [6-10]. Here, we fill these significant gaps in knowledge using an approach that combines functional magnetic resonance imaging and sophisticated computational methods. We first develop a population receptive field (pRF) model [11, 12] of spatial responses in human VTC. Consisting of spatial summation followed by a compressive nonlinearity, this model accurately predicts responses of individual voxels to stimuli at any position and size, explains how spatial information is encoded, and reveals a functional hierarchy in VTC. We then manipulate attention and use our model to decipher the effects of attention. We find that attention to the stimulus systematically and selectively modulates responses in VTC, but not early visual areas. Locally, attention increases eccentricity, size, and gain of individual pRFs, thereby increasing position tolerance. However, globally, these effects reduce uncertainty regarding stimulus location and actually increase position sensitivity of distributed responses across VTC. These results demonstrate that attention actively shapes and enhances spatial representations in the ventral visual pathway.
|Original language||English (US)|
|Number of pages||6|
|State||Published - Mar 2 2015|
Bibliographical noteFunding Information:
We thank T. Naselaris, A. Rokem, J. Winawer, and E. Zohary for helpful discussions. We also thank A. Stigliani for assistance with behavioral experiments; J. Winawer for providing retinotopic mapping data; and N. Witthoft for assisting in the collection of face photographs. This work was supported by the McDonnell Center for Systems Neuroscience and Arts & Sciences at Washington University (K.N.K.), NEI grant 1R01EY02391501A1 (K.G.-S.), and NEI grant RO1 EY03164 (to Brian Wandell). Computations were performed using the facilities of the Washington University Center for High Performance Computing, which were partially provided through grant NCRR 1S10RR022984-01A1.
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