Forest before trees: The precedence of global features in visual perception ☆

By D. Navon. 1977

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Citations in the corpus

Relational inductive biases, deep learning, and graph networks (2018, background): “neralization depends critically on our cognitive mechanisms for representing structure and reasoning about relations. We represent complex systems as compositions of entities and their interactions1 (Navon, 1977; McClelland and Rumelhart, 1981; Plaut et al., 1996; Marcus, 2001; Goodwin and Johnson-L”
Viewpoints and keypoints (2014, background): “Our overall approach is motivated by the theory of global precedence - that humans perceive the global structure before the fine level local details [27].”
Learning by Abstraction: The Neural State Machine (2019, background): “see things in context, and build compositional world models to represent objects and understand their interactions and subtle relations, turning raw sensory signals into high-level semantic knowledge [64]; and we deductively draw inferences via conceptual rules and statements to proceed from known f”
The dual pathway to creativity model: Creative ideation as a function of flexibility and persistence (2010, methodology): “Prior to the brainstorming session, the Navon (1977) task was used to manipulate processing mode (also see De Dreu & Nijstad, 2008; Förster et al., 2004).”
Seeing women as objects: The sexual body part recognition bias (2012, methodology): “In his seminal work on this issue, Navon (1977) presented participants with large, global letters formed from small, local letters.”
Attending to the Big Picture: Mood and Global Versus Local Processing of Visual Information (2002, background): “In visual perception research, Navon (1977) showed that global features take precedence over local features, so that focusing on the forest is a more accessible strategy than focusing on the trees.”
Gaze Cueing of Attention (2007, background): “This interaction may relate to the well-documented global processing bias in normal participants (Navon, 1977), with people with autism showing a bias toward processing local information (U. Frith & Happé, 1994; Happé, 1996, 1999; Mottron, Belleville, & Menard, 1999; Mottron, Burack, Iarocci,…”
GLOMOsys: A Systems Account of Global Versus Local Processing (2010, methodology): “This paradigm (hereafter referred to as the Kimchi-Palmer-figures-task) aimed to improve the Navon-letter-task, which was suspected to be confounded with size and conspicuity (e.g., Kimchi, 1983; Kimchi & Palmer, 1982).”
Is vision continuous with cognition? The case for cognitive impenetrability of visual perception. (1999, background): “Attention may be required for the integration or binding of features across separable dimensions (Treisman 1988), but not across integral dimensions, and global processing dominance (global precedence, global interference) is evident in some tasks (Navon 1977).”
Abstract thinking increases one's sense of power (2008, methodology): “We addressed this alternative explanation in Experiment 3 by using a perceptual manipulation of concrete/abstract thought involving hierarchical figures, larger figures made up of an arrangement of smaller figures, such as an O made up of Ts (Navon, 1977).”
Does Emotion Directly Tune the Scope of Attention? (2013, background): “…finding that people in happy moods focus broadly and those in sad moods focus narrowly can be explained by the fact that people generally show a tendency to focus broadly (Bruner, 1957; Eriksen & St. James, 1986; Fiske & Taylor, 2008; Kimchi, 1992; Köhler, 1929; Navon, 1977, 2003; Neisser, 1967).”
A Century of Gestalt Psychology in Visual Perception II. Conceptual and Theoretical Foundations (2012, background): “In the aftermath of Shannon’s (1948) breakthrough in communication theory, psychologists started to rethink the concept of information (e.”
Local or Global? (2010, unknown): “In this traditional view, shape and level are bound throughout visual processing, although one level may be processed before another (Navon, 1977).”
The Origins and Prevalence of Texture Bias in Convolutional Neural Networks (2019, background): “Introduced by psychologist David Navon in the 1970s to study how people process global versus local visual information [69], Navon ﬁgures consist of a large letter (“shape”) rendered in small copies of another letter (“texture”).”