Literature DB >> 20044891

Perceptual basis of redundancy gains in visual pop-out search.

Thomas Töllner1, Michael Zehetleitner, Joseph Krummenacher, Hermann J Müller.   

Abstract

The redundant-signals effect (RSE) refers to a speed-up of RT when the response is triggered by two, rather than just one, response-relevant target elements. Although there is agreement that in the visual modality RSEs observed with dimensionally redundant signals originating from the same location are generated by coactive processing architectures, there has been a debate as to the exact stage(s)--preattentive versus postselective--of processing at which coactivation arises. To determine the origin(s) of redundancy gains in visual pop-out search, the present study combined mental chronometry with electrophysiological markers that reflect purely preattentive perceptual (posterior-contralateral negativity [PCN]), preattentive and postselective perceptual plus response selection-related (stimulus-locked lateralized readiness potential [LRP]), or purely response production-related processes (response-locked LRP). As expected, there was an RSE on target detection RTs, with evidence for coactivation. At the electrophysiological level, this pattern was mirrored by an RSE in PCN latencies, whereas stimulus-locked LRP latencies showed no RSE over and above the PCN effect. Also, there was no RSE on the response-locked LRPs. This pattern demonstrates a major contribution of preattentive perceptual processing stages to the RSE in visual pop-out search, consistent with parallel-coactive coding of target signals in multiple visual dimensions [Müller, H. J., Heller, D., & Ziegler, J. Visual search for singleton feature targets within and across feature dimensions.

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Year:  2011        PMID: 20044891     DOI: 10.1162/jocn.2010.21422

Source DB:  PubMed          Journal:  J Cogn Neurosci        ISSN: 0898-929X            Impact factor:   3.225


  11 in total

1.  How the speed of motor-response decisions, but not focal-attentional selection, differs as a function of task set and target prevalence.

Authors:  Thomas Töllner; Dragan Rangelov; Hermann J Müller
Journal:  Proc Natl Acad Sci U S A       Date:  2012-06-25       Impact factor: 11.205

2.  Redundancy gain for semantic features.

Authors:  Anja Fiedler; Hannes Schröter; Rolf Ulrich
Journal:  Psychon Bull Rev       Date:  2013-06

3.  Target detection and discrimination in pop-out visual search with two targets.

Authors:  James P Wilmott; Mukesh Makwana; Joo-Hyun Song
Journal:  Atten Percept Psychophys       Date:  2022-05-03       Impact factor: 2.199

4.  Visual search and the aging brain: discerning the effects of age-related brain volume shrinkage on alertness, feature binding, and attentional control.

Authors:  Eva M Müller-Oehring; Tilman Schulte; Torsten Rohlfing; Adolf Pfefferbaum; Edith V Sullivan
Journal:  Neuropsychology       Date:  2013-01       Impact factor: 3.295

5.  Integration of goal- and stimulus-related visual signals revealed by damage to human parietal cortex.

Authors:  Paul M Bays; Victoria Singh-Curry; Nikos Gorgoraptis; Jon Driver; Masud Husain
Journal:  J Neurosci       Date:  2010-04-28       Impact factor: 6.167

6.  Features in visual search combine linearly.

Authors:  R T Pramod; S P Arun
Journal:  J Vis       Date:  2014-04-08       Impact factor: 2.240

7.  The effect of task order predictability in audio-visual dual task performance: Just a central capacity limitation?

Authors:  Thomas Töllner; Tilo Strobach; Torsten Schubert; Hermann J Müller
Journal:  Front Integr Neurosci       Date:  2012-09-11

8.  Stimulus saliency modulates pre-attentive processing speed in human visual cortex.

Authors:  Thomas Töllner; Michael Zehetleitner; Klaus Gramann; Hermann J Müller
Journal:  PLoS One       Date:  2011-01-21       Impact factor: 3.240

9.  Dynamic weighting of feature dimensions in visual search: behavioral and psychophysiological evidence.

Authors:  Joseph Krummenacher; Hermann J Müller
Journal:  Front Psychol       Date:  2012-07-02

10.  Enhanced HMAX model with feedforward feature learning for multiclass categorization.

Authors:  Yinlin Li; Wei Wu; Bo Zhang; Fengfu Li
Journal:  Front Comput Neurosci       Date:  2015-10-07       Impact factor: 2.380

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