Contextual influences in V1 as a basis for pop out and asymmetry in visual search

Abstract

I use a model to show how simple, bottom-up, neural mechanisms in primary visual cortex can qualitatively explain the preattentive component of complex psychophysical phenomena of visual search for a target among distracters. Depending on the image features, the speed of search ranges from fast, when a target pops-out or is instantaneously detectable, to very slow, and it can be asymmetric with respect to switches between the target and distracter objects. It has been unclear which neural mechanisms or even cortical areas control the ease of search, and no physiological correlate has been found for search asymmetry. My model suggests that contextual inf luences in V1 play a significant role.Visual search is an important task associated with visual segmentation, and phenomena such as pop out and asymmetries have been extensively studied (1-5). Examples of pop out are that a red dot can be instantly spotted among a background of green ones or a vertical line among horizontal ones. Not everything pops out-a red ''X'' is much more difficult to spot among green X's and red ''O's'', and locating it may require attentive serial search (1,(4)(5)(6)). An example of search asymmetry is that a long line is easier to spot among shorter lines than vice versa. Other typical target-distractor pairs manifesting the asymmetry include tilted vs. vertical, curved vs. straight, and convergent vs. parallel lines, incomplete vs. complete circles, and ellipses vs. circles (3).A leading psychophysical model (1, 3) of these phenomena accounts for them by considering maps of basic feature dimensions and their interactions. The basic feature dimensions include primitive aspects of inputs such as orientation, color, and brightness. A target is supposed to be instantly detectable against a background provided that its value (or feature) in one of these dimensions (e.g., being oriented vertically or being red) is possessed by none of the distracters. However, if a target is only distinguished from the background by a conjunction of features (e.g., being red and an X, for a target red X against a background of green X's and red O's), and particularly by a conjunction of two orientations (6), then it will be difficult to spot. This model has been extended to account for search asymmetries by suggesting that, for some features (such as line length or curvature), targets with larger values (longer or more curved lines) are inherently more detectable than targets with smaller values (shorter or more straight lines) (3). For features without an obvious order, such as closure vs. openness, the data on search asymmetries themselves are used to decide which feature values are ''larger'' (more detectable) or ''smaller'' (less detectable).The model is powerful, and various related and extended models have been proposed to explain a whole continuum of search efficiencies by combining parallel and serial search (4-6). Further, a related model of pop out (2) assumes a set of special features that constitute textons. In this paper, I complement the...

Theoretical understanding of the early visual processes by data compression and data selection

“…The direction of these slight changes would be difficult to predict much beyond a chance level by an incorrect model or hypothesis without any parameter tuning. Nevertheless, the model predicted (Li 1999b) these directions correctly in all of the five best known examples of asymmetry (Treisman and Gormican 1988) shown in Fig. (10).…”

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“…that they are responsible for the corresponding behavioural pop-out (Li 1999a(Li , 2002. The model links physiology with perception and to demonstrate the feasibility of a single neural circuit implementing a cooperative algorithm for an extended computational goal.…”

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“…The main focus of Li's investigations lies in the study of how V1 horizontal long-range integration could functionally account for the enhancement of co-circular contour arrangements in cluttered scenes (Li, 1998), the enhancement of texture region boundaries (Li, 1999a) and pop-out e ects in visual search (Li, 1999b). Emphasis was devoted to the mathematical analysis and stability behavior of such a network of non-linear coupled oscillators.…”

“…Contour integration and segmentation Li (1998Li ( , 1999aLi ( , 1999b proposed a model of spatial boundary integration in V1 based on a network of coupled oscillators that are de ned by pairs of inhibitory interneurons and excitatory cells. Li's model also utilizes oriented weighting functions for lateral interaction between orientation selective cells.…”