Literature DB >> 18723044

Non-linear integration of crowded orientation signals.

Carolina Gheri1, Stefano Baldassi.   

Abstract

Crowding of oriented signals has been explained as linear, compulsory averaging of the signals from target and flankers [Parkes, L., Lund, J., Angelucci, A., Solomon, J. A., & Morgan, M. (2001). Compulsory averaging of crowded orientation signals in human vision. Nature Neuroscience, 4(7), 739-744]. On the other hand, a comparable search task with sparse stimuli is well modeled by a 'Signed-Max' rule that integrates non-linearly local tilt estimates [Baldassi, S., & Verghese, P. (2002). Comparing integration rules in visual search. Journal of Vision, 2(8), 559-570], as reflected by the bimodality of the distributions of reported tilts in a magnitude matching task [Baldassi, S., Megna, N., & Burr, D. C. (2006). Visual clutter causes high-magnitude errors. PLoS Biology, 4(3), e56]. This study compares the two models in the context of crowding by using a magnitude matching task, to measure distributions of perceived target angles and a localization task, to probe the degree of access to local information. Response distributions were bimodal, implying uncertainty, only in the presence of abutting flankers. Localization of the target is relatively preserved but it quantitatively falls in between the predictions of the two models, possibly suggesting local averaging followed by a max operation. This challenges the notion of global averaging and suggests some conscious access to local orientation estimates.

Entities:  

Mesh:

Year:  2008        PMID: 18723044     DOI: 10.1016/j.visres.2008.07.022

Source DB:  PubMed          Journal:  Vision Res        ISSN: 0042-6989            Impact factor:   1.886


  8 in total

1.  Positional averaging explains crowding with letter-like stimuli.

Authors:  John A Greenwood; Peter J Bex; Steven C Dakin
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2.  How are local orientation signals pooled?

Authors:  Jüri Allik; Mai Toom; Richard Naar; Aire Raidvee
Journal:  Atten Percept Psychophys       Date:  2022-03-02       Impact factor: 2.199

3.  Visual crowding cannot be wholly explained by feature pooling.

Authors:  Edward F Ester; Daniel Klee; Edward Awh
Journal:  J Exp Psychol Hum Percept Perform       Date:  2013-12-23       Impact factor: 3.332

4.  Object-based biased competition during covert spatial orienting.

Authors:  Miranda Scolari; Edward Awh
Journal:  Atten Percept Psychophys       Date:  2019-07       Impact factor: 2.199

5.  Electrophysiological evidence for failures of item individuation in crowded visual displays.

Authors:  David E Anderson; Edward F Ester; Daniel Klee; Edward K Vogel; Edward Awh
Journal:  J Cogn Neurosci       Date:  2014-04-16       Impact factor: 3.225

6.  Feature-based attention enhances performance by increasing response gain.

Authors:  Katrin Herrmann; David J Heeger; Marisa Carrasco
Journal:  Vision Res       Date:  2012-05-02       Impact factor: 1.886

7.  On the rules of integration of crowded orientation signals.

Authors:  Endel Põder
Journal:  Iperception       Date:  2012-07-09

8.  Relating spatial and temporal orientation pooling to population decoding solutions in human vision.

Authors:  Ben S Webb; Timothy Ledgeway; Paul V McGraw
Journal:  Vision Res       Date:  2010-05-04       Impact factor: 1.886

  8 in total

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