Visual search in scenes involves selective and nonselective pathways

Abstract

How do we find objects in scenes? For decades, visual search models have been built on experiments in which observers search for targets, presented among distractor items, isolated and randomly arranged on blank backgrounds. Are these models relevant to search in continuous scenes? This paper argues that the mechanisms that govern artificial, laboratory search tasks do play a role in visual search in scenes. However, scene-based information is used to guide search in ways that had no place in earlier models. Search in scenes may be best explained by a dual-path model: A "selective" path in which candidate objects must be individually selected for recognition and a "non-selective" path in which information can be extracted from global / statistical information. Searching and experiencing a sceneIt is an interesting aspect of visual experience that we can look for an object that is, literally, right in front of our eyes, yet not find it for an appreciable period of time. It is clear that we are seeing something at the object's location before we find it. What is that something and how do we go about finding that desired object? These questions have occupied visual search researchers for decades. While visual search papers have conventionally described search as an important real-world task, the bulk of research had observers looking for targets among some number of distractor items, all presented in random configurations on otherwise blank backgrounds. In the last decade, there has been a surge of work using more naturalistic scenes as stimuli and this has raised the issue of the relationship of the search to the structure of the scene. This paper will briefly summarize some of the models and solutions developed with artificial stimuli and then describe what happens when these ideas confront search in real-world scenes. We will argue that the process of object recognition, required for most search tasks, involves the selection of individual candidate objects because all objects cannot be recognized at once. At the same time, the experience of a continuous visual field tell us that some aspects of a scene reach awareness without being limited by the selection bottleneck in object recognition. Work in the past decade has revealed how this non-selective processing is put to use when we search in real scenes. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. . As will be briefly reviewed below, it holds that search is necessary because object recognition processes are limited to one or, perhaps, a very few objects at one time. The selection of candidate objects for subseq...

Object grouping based on real-world regularities facilitates perception by reducing competitive interactions in visual cortex

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“…For example, we look above the sink when searching for a mirror. Such contextual guidance can stem from implicit or explicit memory for specific target locations within a specific context (38)(39)(40), global scene properties (41,42), and also from relations between target and nontarget objects (43,44). At a general level, the current results might similarly reflect the learning of real-world correlational structure.…”

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