Hemispheric Differences for Global and Local Processing: Effect of Stimulus Size and Sparsity

2009 ◽  
Vol 12 (1) ◽  
pp. 21-31 ◽  
Author(s):  
María J. Blanca ◽  
Gema López-Montiel

The present experiment was designed to assess the hemispheric differences for global and local processing in healthy participants under different conditions of stimuli visibility, by means of varying the size and sparsity. Three different sizes and three different matrixes of hierarchical stimuli were introduced. Stimuli consisted of incomplete squares with one side missing. Participants were asked to carry out an orientation classification task (left/right), indicating the orientation of the square opening either at global or local levels. The results do not support the hemispheric differences for global and local processing, showing the same efficiency of right and left hemispheres for analyzing global and local information. Nevertheless, other results found are consistent with the hypothesis of right hemisphere superiority under degraded stimulus conditions.

2019 ◽  
Author(s):  
Georgin Jacob ◽  
S. P. Arun

ABSTRACTHierarchical stimuli (such as a circle made of diamonds) have been widely used to study global and local processing. Two classic phenomena have been observed using these stimuli: the global advantage effect (that we identify the circle faster than the diamonds) and the incongruence effect (that we identify the circle faster when both global and local shapes are circles). Understanding them has been difficult because they occur during shape detection, where an unknown categorical judgement is made on an unknown feature representation.Here we report two essential findings. First, these phenomena are present both in a general same-different task and a visual search task, suggesting that they may be intrinsic properties of the underlying representation. Second, in both tasks, responses were explained using linear models that combined multiscale shape differences and shape distinctiveness. Thus, global and local processing can be understood as properties of a systematic underlying feature representation.


1994 ◽  
Vol 32 (11) ◽  
pp. 1343-1351 ◽  
Author(s):  
M.J. Blanca ◽  
C. Zalabardo ◽  
F. García-Criado ◽  
R. Siles

2017 ◽  
Vol 119 ◽  
pp. 10-16 ◽  
Author(s):  
Sanne G. Brederoo ◽  
Mark R. Nieuwenstein ◽  
Monicque M. Lorist ◽  
Frans W. Cornelissen

Neuroreport ◽  
1999 ◽  
Vol 10 (12) ◽  
pp. 2467-2472 ◽  
Author(s):  
Alessandro Schiavetto ◽  
Filomeno Cortese ◽  
Claude Alain

Sign in / Sign up

Export Citation Format

Share Document