Classic work has largely focused on simple static two-dimensional geometric targets, patterns, and backgrounds however, the nature of these visual stimuli differs considerably from everyday experience. Understanding how these manipulations affect search performance is useful in characterizing the perceptual limits of the human visual system. For example, the presence of environmental clutter and altering the trajectory of the target being followed can also make search more difficult ( Bravo & Farid, 2004 Matsuno & Tomonaga, 2006 Rosenholtz, Li, & Nakano, 2010). Other non-target-related stimulus features can also affect the visual search process by impacting strategy usage and overall efficiency ( Horowitz, Wolfe, DiMase, & Klieger, 2007 Moran, Zehetleitner, Liesefeld, Müller, & Usher, 2016). In contrast, search time for conjunctions of two or more features increases with the number of distractors ( Palmer, 1994 Treisman, 1988 Wolfe, 1994), but not in all cases ( Nakayama & Silverman, 1986). For example, increasing the number of surrounding elements has little or no effect on search times when the target differs from distractors by a single feature such as shape, size, or color ( Nakayama & Silverman, 1986 Teichner & Krebs, 1974).
Incrementing in matlab 2018b serial#
Visual search performance has been investigated in the context of manipulating task demands and parameters and serves as an important paradigm for exploring serial and parallel deployment of attention ( Treisman & Gelade, 1980). Much work has explored various aspects of visual search based on a variety of behavioral testing paradigms ( Rao, Zelinsky, Hayhoe, & Ballard, 2002 Treisman, 1988 Wolfe, 2007 Zelinsky, Zhang, Yu, Chen, & Samaras, 2006 for review, see Eckstein, 2011 Wolfe & Horowitz, 2017), and the underlying neural correlates associated with various search tasks have also been explored ( Bundesen, Habekost, & Kyllingsbæk, 2005 Desimone & Duncan, 1995 Eimer, 2014 Luck & Hillyard, 1994). For example, the task of finding and following a person in a crowd requires that a specific target be identified and continuously tracked within a complex moving scene. Visual search can be very demanding in our dynamic and ever-changing surroundings. This engaging platform may also have utility in assessing visual search in a variety of clinical populations of interest.
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These results demonstrate how visual search performance can be investigated using VR-based naturalistic dynamic scenes and with high behavioral relevance. In contrast, the presence of visual clutter had no effect. In general, results showed a pattern of worsening performance with increasing crowd density. To assess the effect of task difficulty, we manipulated factors of the visual scene, including crowd density (i.e., number of surrounding distractors) and the presence of environmental clutter. Performance was quantified based on saccade and smooth pursuit ocular motor behavior. Participants were instructed to search for a preselected human target walking in a crowded hallway setting. In this direction, we have developed a first-person perspective VR environment combined with eye tracking for the capture of a variety of objective measures.
Recently, there has been a shift toward investigating visual search in more naturalistic dynamic scenes using virtual reality (VR)-based paradigms. Classic work assessing visual search performance has been dominated by the use of simple geometric shapes, patterns, and static backgrounds. Daily activities require the constant searching and tracking of visual targets in dynamic and complex scenes.
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