title: How best to unify crowding?
abstract: In crowding, the perception of an object deteriorates in the presence of nearby elements. Obviously, crowding is a ubiquitous phenomenon, as elements are rarely seen in isolation. One of the main characteristics of crowding is that the elements themselves are not rendered invisible, but their features are averaged[1] or substituted[2] with those of neighboring elements. Recently, Harrison and Bex [3] presented "A Unifying Model of Orientation Crowding in Peripheral Vision", which elegantly explains these two characteristics of crowding with one unifying mechanism. They tested their model using a new crowding paradigm and demonstrated an excellent match between human and model results. A key prediction of their model is that a higher number of flankers leads to stronger crowding, simply because more non-target features contribute to the model's output and thus deteriorate performance. However, several recent studies have shown that increasing the number of flankers can actually improve performance [4-9]. Using the same experimental design as Harrison and Bex [3], we report here that adding more flankers can also improve performance in their paradigm, whereas their model predicts the opposite result. We propose that a truly unified model of crowding must include a grouping stage.
authors.affiliation: Laboratory of Psychophysics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland. Electronic address: matt.pachai@epfl.ch.
authors.forename: Matthew V
authors.initials: MV
authors.lastname: Pachai
authors.affiliation: Laboratory of Psychophysics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
authors.forename: Adrien C
authors.initials: AC
authors.lastname: Doerig
authors.affiliation: Laboratory of Psychophysics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
authors.forename: Michael H
authors.initials: MH
authors.lastname: Herzog
mesh_terms: D015350:Contrast Sensitivity; D004192:Discrimination, Psychological; D006801:Humans; D010364:Pattern Recognition, Visual; D010470:Perceptual Masking; D013028:Space Perception; D014794:Visual Fields