Distance Modulation of Neural Activity in the Visual Cortex

Dobbins, Allan C.; Jeo, Richard; Fiser, József; Allman, John M.

doi:10.1126/science.281.5376.552

Cited by 109 publications

(73 citation statements)

References 24 publications

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“…Also the area MIP is strongly and reciprocally connected with V6A (Gamberini et al, 2009;Passarelli et al, 2011), and it is known that MIP (also named PRR) hosts neurons with planning activity for reaching modulated by fixation depth (Bhattacharyya et al, 2009) et al, 2011). Modulations of neural activity by fixation depth have been also found in the VIP (ventral intraparietal) area (Colby et al, 1993) and in visual areas V2 and V4 (Dobbins et al, 1998;Rosenbluth and Allman, 2002), all directly connected with V6A. The two extrastriate areas, in particular, send strong inputs to the ventral part of V6A, which in turn is strongly connected with the dorsal part of V6A (Passarelli et al, 2011), so they are likely candidates to provide depthrelated signals to V6A.…”

Section: Comparison With Other Areasmentioning

confidence: 92%

Eye Position Encoding in Three-Dimensional Space: Integration of Version and Vergence Signals in the Medial Posterior Parietal Cortex

et al. 2012

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Section: Comparison With Other Areasmentioning

confidence: 92%

Eye Position Encoding in Three-Dimensional Space: Integration of Version and Vergence Signals in the Medial Posterior Parietal Cortex

et al. 2012

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“…IT receptive fields of variable, attention-controlled spatial scales are obtained when the mechanism is extended to scale dependent attentional gain fields in V4. V4 neurons have been found to be tuned to images of specific sizes and have gain fields that depend on viewing distance (Dobbins, Jeo, Fiser, & Allman, 1998). According to the computational principles of gain modulation, these size-dependent gain fields should correspond with the scaling of receptive fields in IT.…”

Section: Neurocomputational and Neurophysiological Evidencementioning

confidence: 99%

Coordinate transformations in object recognition.

Graf¹

2006

Psychological Bulletin

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A basic problem of visual perception is how human beings recognize objects after spatial transformations. Three central classes of findings have to be accounted for: (a) Recognition performance varies systematically with orientation, size, and position; (b) recognition latencies are sequentially additive, suggesting analogue transformation processes; and (c) orientation and size congruency effects indicate that recognition involves the adjustment of a reference frame. All 3 classes of findings can be explained by a transformational framework of recognition: Recognition is achieved by an analogue transformation of a perceptual coordinate system that aligns memory and input representations. Coordinate transformations can be implemented neurocomputationally by gain (amplitude) modulation and may be regarded as a general processing principle of the visual cortex.Keywords: alignment, coordinate transformations, gain modulation, object recognition, reference frames How can we recognize objects regardless of spatial transformations such as plane and depth rotation, size scaling, and position changes? This ability is often discussed under the label object constancy or shape constancy. Even young children recognize objects so immediately and effortlessly that it seems to be a rather ordinary and simple task. However, changes in the spatial relation between observer and object lead to large changes of the image that is projected onto the retina. Hence, to recognize objects regardless of orientation, size, and position is not a trivial problem. No computational system proposed so far can successfully recognize objects over wide ranges of object categories and contexts.Several different approaches have been proposed over the years (for reviews, see Palmeri & Gauthier, 2004;Ullman, 1996). A number of models rely on abstract object representations, which predict that recognition performance is typically invariant regarding spatial transformations (e.g., structural description models; see Hummel & Biederman, 1992;Marr & Nishihara, 1978). In contrast, image-based or view-based models propose that object representations are close to the format of the perceptual input and therefore depend systematically on image transformations (e.g.,

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“…In the ÿrst step, the entire image is preprocessed in lower (early) cortical areas by dissecting it into its local 3 features. Then, perception is built up by fusing the local features together to a global percept in higher areas [14,6]. Attentional feedback from higher cortical areas would 5 help fusing the corresponding features to form object perceptions in scenes [21].…”

Section: Article In Pressmentioning

confidence: 99%