Contextual cueing: implicit learning and memory of visual context guides spatial attention.

By Marvin M. Chun, Yuhong V. Jiang. 1998

Global context plays an important, but poorly understood, role in visual tasks. This study demonstrates that a robust memory for visual context exists to guide spatial attention. Global context was operationalized as the spatial layout of objects in visual search displays. Half of the configurations were repeated across blocks throughout the entire session, and targets appeared within consistent locations in these arrays. Targets appearing in learned configurations were detected more quickly. This newly discovered form of search facilitation is termed contextual cueing. Contextual cueing is driven by incidentally learned associations between spatial configurations (context) and target locations. This benefit was obtained despite chance performance for recognizing the configurations, suggesting that the memory for context was implicit. The results show how implicit learning and memory of visual context can guide spatial attention towards task-relevant aspects of a scene.

Subfields: Cognitive Science & Psychology

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Citations in the corpus

The Dynamic Representation of Scenes (2000, unknown): “For example, if focused attention is occupied with the formation of an object, an unattended stimulus can cause priming (Shapiro, Driver, Ward, & Sorensen, 1997), or enter long-term memory (Chun & Jiang, 1998), even though it is not consciously perceived.”
Task and context determine where you look. (2016, unknown): “, Chun & Jiang , 1998 ) .”
Using real-world scenes as contextual cues for search (2006, background): “Chun and his colleagues have conducted a series of elegant experiments in support of this contextual cueing hypothesis (Chua & Chun, 2003; Chun & Jiang, 1998, 1999, 2003; Jiang & Chun, 2001; Olson & Chun, 2002).”
Bound feature combinations in visual short-term memory are fragile but influence long-term learning (2009, background): “Several researchers have suggested that these contingencies are represented implicitly in long-term memory (e.g., Chun & Jiang, 1998; Geng & Behrmann, 2005; Walthew & Gilchrist, 2006).”
Organization of visual short-term memory. (2000, unknown): “The human visual system efficiently extracts regularities in visual perception and learning (Chun & Jiang, 1998,1999), and visual memory is no exception.”
Statistical learning of higher-order temporal structure from visual shape sequences. (2002, background): “…results suggest that context (e.g., preceding scenes in the sequential presentation of many scenes) implicitly promotes associations across scenes (Chun & Jiang, 1998, 1999; Chun & Nakayama, 2000; Olson & Chun, 2001), and that target priming can facilitate visual search (Maljkovic & Nakayama,…”
Visual search: a retrospective. (2011, background): “Upon repeated presentations, search times are reduced to locate targets in previously viewed conﬁgurations (e.g., Figure 4b; Brockmole, Hambrick, Windisch, & Henderson, 2008; Chun, 2000; Chun & Jiang, 1998; Hollingworth, 2009).”
Prospective Optimization (2014, methodology): “In addition to the ideal-observer theory, the task was also modeled using a temporal-difference algorithm in reinforcement learning [41], which reduces the error of predicted future rewards and is motivated by animal learning and behavioral experiments [42].”
Experience-Dependent Perceptual Grouping and Object-Based Attention (2002, background): “, Ahissar &Hochstein, 1993; Shiu &Pashler, 1992),while Chun and Jiang (1998) have shown that a consistent configuration of distractors can speed visual search when it is task-relevant (indicative of target location).”
Visual search and selective attention (2006, background): “That such mechanisms can also represent the arrangement of items in complex search scenes, is suggested by Chun and Jiang (1998).”
Modeling the influence of task on attention. (2005, background): “…(semantic category) of a scene is very useful in determining scene context, and is known to guide eye movements (Biederman, Mezzanotte, & Rabinowitz , 1982; Chun & Jiang, 1998; De Graef, Christiaens , & dÕYdewalle, 1990; Henderson & Hollingworth, 1999; Palmer, 1975; Rensink, 2000; Torralba, 2003).”
When art meets tech: The role of augmented reality in enhancing museum experiences and purchase intentions (2018, background): “Previous research has found that virtual presence can effectively influence individuals' mental imagery level via contextual cueing (Blascovich et al., 2002; Chun & Jiang, 1998).”
VISUAL SALIENCY DOES NOT ACCOUNT FOR EYE MOVEMENTS DURING VISUAL SEARCH IN REAL-WORLD SCENES (2007, background): “The contextual cueing phenomenon shows that perceptual learning of complex visual images can take place relatively rapidly over multiple encounters (Chun & Jiang, 1998), and this effect has been shown to influence eye movements (Peterson & Kramer, 2001).”