title: Spatial biases and computational constraints on the encoding of complex local image structure.
abstract: The decomposition of visual scenes into elements described by orientation and spatial frequency is well documented in the early cortical visual system. How such 2nd-order elements are sewn together to create perceptual objects such as corners and intersections remains relatively unexplored. The current study combines information theory with structured deterministic patterns to gain insight into how complex (higher-order) image features are encoded. To more fully probe these mechanisms, many subjects (N = 24) and stimuli were employed. The detection of complex image structure was studied under conditions of learning and attentive versus preattentive visual scrutiny. Strong correlations (R(2) > 0.8, P < 0.0001) were found between a particular family of spatially biased measures of image information and human sensitivity to a large range of visual structures. The results point to computational and spatial limitations of such encoding. Of the extremely large set of complex spatial interactions that are possible, the small subset perceivable by humans were found to be dominated by those occurring along sets of one or more narrow parallel lines. Within such spatial domains, the number of pieces of visual information (pixel values) that may be simultaneously considered is limited to a maximum of 10 points. Learning and processes involved in attentive scrutiny do little if anything to increase the dimensionality of this system.
authors.affiliation: ARC Centre of Excellence in Vision Science and Centre for Visual Sciences, Research School of Biological Sciences, Australian National University, Canberra, Australia. ryan.taylor@anu.edu.au
authors.forename: Ryan R L
authors.initials: RR
authors.lastname: Taylor
authors.forename: Ted
authors.initials: T
authors.lastname: Maddess
authors.forename: Yoshinori
authors.initials: Y
authors.lastname: Nagai
mesh_terms: D001288:Attention / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D007858:Learning / Q000502:physiology; D010364:Pattern Recognition, Visual / Q000502:physiology*; D014793:Visual Cortex / Q000502:physiology*