Literature DB >> 15869494

Rapid categorization of achromatic natural scenes: how robust at very low contrasts?

Marc J-M Macé1, Simon J Thorpe, Michèle Fabre-Thorpe.   

Abstract

The human visual system is remarkably good at categorizing objects even in challenging visual conditions. Here we specifically assessed the robustness of the visual system in the face of large contrast variations in a high-level categorization task using natural images. Human subjects performed a go/no-go animal/nonanimal categorization task with briefly flashed grey level images. Performance was analysed for a large range of contrast conditions randomly presented to the subjects and varying from normal to 3% of initial contrast. Accuracy was very robust and subjects were performing well above chance level (approximately 70% correct) with only 10-12% of initial contrast. Accuracy decreased with contrast reduction but reached chance level only in the most extreme condition (3% of initial contrast). Conversely, the maximal increase in mean reaction time was approximately 60 ms (at 8% of initial contrast); it then remained stable with further contrast reductions. Associated ERPs recorded on correct target and distractor trials showed a clear differential effect whose amplitude and peak latency were correlated respectively with task accuracy and mean reaction times. These data show the strong robustness of the visual system in object categorization at very low contrast. They suggest that magnocellular information could play a role in ventral stream visual functions such as object recognition. Performance may rely on early object representations which lack the details provided subsequently by the parvocellular system but contain enough information to reach decision in the categorization task.

Entities:  

Mesh:

Year:  2005        PMID: 15869494     DOI: 10.1111/j.1460-9568.2005.04029.x

Source DB:  PubMed          Journal:  Eur J Neurosci        ISSN: 0953-816X            Impact factor:   3.386


  13 in total

1.  Explicit semantic stimulus categorization interferes with implicit emotion processing.

Authors:  Harald T Schupp; Ralf Schmälzle; Tobias Flaisch
Journal:  Soc Cogn Affect Neurosci       Date:  2013-11-05       Impact factor: 3.436

2.  Similarity relations in visual search predict rapid visual categorization.

Authors:  Krithika Mohan; S P Arun
Journal:  J Vis       Date:  2012-10-23       Impact factor: 2.240

3.  Key visual features for rapid categorization of animals in natural scenes.

Authors:  Arnaud Delorme; Ghislaine Richard; Michele Fabre-Thorpe
Journal:  Front Psychol       Date:  2010-06-23

4.  Quantifying the Time Course of Visual Object Processing Using ERPs: It's Time to Up the Game.

Authors:  Guillaume A Rousselet; Cyril R Pernet
Journal:  Front Psychol       Date:  2011-05-23

5.  The characteristics and limits of rapid visual categorization.

Authors:  Michèle Fabre-Thorpe
Journal:  Front Psychol       Date:  2011-10-03

6.  Examining the role of red background in magnocellular contribution to face perception.

Authors:  Bhuvanesh Awasthi; Mark A Williams; Jason Friedman
Journal:  PeerJ       Date:  2016-02-04       Impact factor: 2.984

7.  Contrast adaptation contributes to contrast-invariance of orientation tuning of primate V1 cells.

Authors:  Lionel G Nowak; Pascal Barone
Journal:  PLoS One       Date:  2009-03-10       Impact factor: 3.240

8.  The time-course of visual categorizations: you spot the animal faster than the bird.

Authors:  Marc J-M Macé; Olivier R Joubert; Jean-Luc Nespoulous; Michèle Fabre-Thorpe
Journal:  PLoS One       Date:  2009-06-17       Impact factor: 3.240

9.  Impact of feature saliency on visual category learning.

Authors:  Rubi Hammer
Journal:  Front Psychol       Date:  2015-04-21

10.  Dense sampling reveals behavioral oscillations in rapid visual categorization.

Authors:  Jan Drewes; Weina Zhu; Andreas Wutz; David Melcher
Journal:  Sci Rep       Date:  2015-11-06       Impact factor: 4.379

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.