An overview of quantitative approaches in Gestalt perception.

By Frank Jäkel, Manish Singh, Felix Wichmann, Michael H. Herzog. 2016

72 citations recorded by the source corpus.

Gestalt psychology is often criticized as lacking quantitative measurements and precise mathematical models. While this is true of the early Gestalt school, today there are many quantitative approaches in Gestalt perception and the special issue of Vision Research "Quantitative Approaches in Gestalt Perception" showcases the current state-of-the-art. In this article we give an overview of these current approaches. For example, ideal observer models are one of the standard quantitative tools in vision research and there is a clear trend to try and apply this tool to Gestalt perception and thereby integrate Gestalt perception into mainstream vision research. More generally, Bayesian models, long popular in other areas of vision research, are increasingly being employed to model perceptual grouping as well. Thus, although experimental and theoretical approaches to Gestalt perception remain quite diverse, we are hopeful that these quantitative trends will pave the way for a unified theory.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology

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Citations in the corpus

On the Binding Problem in Artificial Neural Networks (2020, background): “However, note that Gestalt Psychology has been criticized for its emphasis on subjective experience and the lack of successful physiological or mechanistic predictions (e.g. Ohlsson, 1984; Treisman and Gelade, 1980; but see Jäkel et al., 2016).”