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Pattern Categorization and Learning

Approach


Human recognition of patterns and objects relies on previously acquired categories that guide perceptual classification. Our psychophysical approach to categorization employs sets of so-called compound Gabor patterns, grey-level patterns that are defined within a two-dimensional evenness-oddness Fourier feature space. As illustrated in the figure, this space provides a low-dimensional continuum of form, in which each point uniquely determines the structure of a pattern, and clusters of points may be used to define classes to be learned by an observer. 

Our approach offers a controlled way to study category learning
  • of unfamiliar patterns (thus minimizing confounding effects of prior knowledge)
  • with continuous (rather than just discrete) category boundaries

  • for structure-only stimuli, i.e. patterns that only differ in the spatial arrangement of their constituent parts rather than the shape of individual parts.
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Applications


Above paradigm has been used to investigate

  • pattern category learning in central and peripheral vision (Jüttner & Rentschler 1996, 2000; Strasburger, Rentschler & Jüttner, 2011)
  • the dissociation between discrimination and category learning (Jüttner  & Rentschler, 2000)
  • the role of strategy in category learning (Unzicker, Jüttner, & Rentschler, 1999)
  • the acquisition of visual expertise (Rentschler & Jüttner, 2007)
  • the generalization of pattern categories with regard to space (Jüttner & Rentschler, 2008)  and appearance (Jüttner, Caelli & Rentschler, 1997; Jüttner, Langguth & Rentschler, 2004)
  • hemispheric lateralization in pattern category learning (Jüttner & Rentschler, 2008; Langguth, Jüttner, Landis, Regard & Rentschler, 2009)
  • the impact of category learning on aesthetic judgments (Rentschler, Jüttner, Unzicker & Landis, 1999)
  • computational approaches to human category learning (Jüttner, Caelli & Rentschler, 1997; Unzicker, Jüttner & Rentschler, 1998, 1999; Jüttner, Langguth & Rentschler, 2004; Rentschler & Jüttner, 2007)

Main collaborators

Ingo Rentschler (Institute for Medical Psychology, University of Munich, Germany)

Terry Caelli (National Information and Communications Technology Centre of Excellence (NICTA), Canberra, Australia)

Theodor Landis (Service de Neurologie, University Hospital Geneva, Switzerland)

Publications 

  • Jüttner, M. & Rentschler, I. (1996) Reduced perceptual dimensionality in extrafoveal vision. Vision Research 36, 1007-1021. [pdf]
  • Jüttner, M. & Rentschler, I. (2000) Scale invariant superiority of foveal vision in perceptual categorization. European Journal of Neuroscience 12, 353-359. [pdf]
  • Jüttner, M. & Rentschler, I. (2008) Category learning induces position invariance of pattern recognition across the visual field. Proceedings of the Royal Society B 275, 403-410. [abstract] [pdf] 
  • Jüttner, M., Caelli, T. & Rentschler, I. (1997) Evidence-based pattern classification: A structural approach to human perceptual learning and generalization. Journal of Mathematical Psychology 41, 244-259. [pdf]
  • Jüttner, M., Langguth, B. & Rentschler, I. (2004) The impact of context on category learning and representation. Visual Cognition 11, 921-945. [abstract] [pdf] 
  • Langguth, B., Jüttner, M., Landis, T., Regard, M. & Rentschler, I. (2009). Differential impact of posterior lesions in the left and right hemisphere on visual category learning and generalization to contrast reversal. Neuropsychologia 47, 2927-2936. [abstract] [pdf]
  • Rentschler, I. & Jüttner, M. (2007) Mirror-image relations in category learning. Visual Cognition 15, 211-237. [abstract] [pdf]
  • Rentschler, I., Jüttner, M., Unzicker, A. & Landis. T. (1999) Innate and learned components of human visual preference. Current Biology 9, 665-671. [pdf]
  • Strasburger, H., Rentschler, I. & Jüttner, M. (2011). Peripheral vision and pattern recognition: A review. Journal of Vision, 11(5):13, 1–82. http://www.journalofvision.org/content/11/5/13 [abstract]
  • Unzicker, A., Jüttner, M. & Rentschler, I. (1998) Similarity models of human visual recognition. Vision Research 38, 2289-2305. [pdf]
  • Unzicker, A., Jüttner, M. & Rentschler, I. (1999) Modelling the dynamics of visual classification behaviour. Mathematical Social Sciences 38, 295-313.

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