Peripheral Spatial Cues and Spatial Stroop Effect Can Modulate Each Other: Analyzing the Relationship between Input Selection and Dimensional Selection

2009 ◽  
Author(s):  
Chunming Luo ◽  
Xiaolan Fu ◽  
Xiang Qiu ◽  
Juncheng Shang ◽  
Dongning Ren
Keyword(s):  
Stroop Effect ◽  
Spatial Cues ◽  
Input Selection ◽  
The Relationship ◽  
Spatial Stroop Effect ◽  
Spatial Stroop

The Two Sides of Temporal Orienting

2010 ◽  
Vol 57 (2) ◽  
pp. 142-148 ◽  
Author(s):  
Ángel Correa ◽  
Paola Cappucci ◽  
Anna C. Nobre ◽  
Juan Lupiáñez
Keyword(s):  
Executive Control ◽  
Stroop Effect ◽  
Traffic Situation ◽  
Target Onset ◽  
Stimulus Features ◽  
Two Sides ◽  
Spatial Stroop Effect ◽  
Probable Interval ◽  
Spatial Stroop ◽  
Temporal Orienting

Would it be helpful to inform a driver about when a conflicting traffic situation is going to occur? We tested whether temporal orienting of attention could enhance executive control to select among conflicting stimuli and responses. Temporal orienting was induced by presenting explicit cues predicting the most probable interval for target onset, which could be short (400 ms) or long (1,300 ms). Executive control was measured both by flanker and Simon tasks involving conflict between incompatible responses and by the spatial Stroop task involving conflict between perceptual stimulus features. The results showed that temporal orienting facilitated the resolution of perceptual conflict by reducing the spatial Stroop effect, whereas it interfered with the resolution of response conflict by increasing flanker and Simon effects. Such opposite effects suggest that temporal orienting of attention modulates executive control through dissociable mechanisms, depending on whether the competition between conflicting representations is located at perceptual or response levels.

scholarly journals Reduction of the Spatial Stroop Effect by Peripheral Cueing as a Function of the Presence/Absence of Placeholders

PLoS ONE ◽  
2013 ◽  
Vol 8 (7) ◽  
pp. e69456 ◽  
Author(s):  
Chunming Luo ◽  
Juan Lupiáñez ◽  
María Jesús Funes ◽  
Xiaolan Fu
Keyword(s):  
Stroop Effect ◽  
Spatial Stroop Effect ◽  
Spatial Stroop

The Spatial Stroop Effect between The Direction and Installed Location of The Emergency Evacuation Route Sign

2016 ◽  
Vol 35 (1) ◽  
pp. 1
Author(s):  
Chobok Kim ◽  
Huiyeong Seo ◽  
Minyoung Hur ◽  
Yunji Lee ◽  
Yoonkyung Oh ◽  
...  
Keyword(s):  
Stroop Effect ◽  
Emergency Evacuation ◽  
Evacuation Route ◽  
Spatial Stroop Effect ◽  
Spatial Stroop

Influence of global precedence on spatial Stroop effect

2020 ◽  
Vol 208 ◽  
pp. 103116
Author(s):  
Chen Pang ◽  
Mingming Qi ◽  
Heming Gao
Keyword(s):  
Stroop Effect ◽  
Global Precedence ◽  
Spatial Stroop Effect ◽  
Spatial Stroop

The spatial Stroop effect: A comparison of color-word and position-word interference

2014 ◽  
Vol 21 (6) ◽  
pp. 1509-1515 ◽  
Author(s):  
Sven Hilbert ◽  
Tristan Toyo Nakagawa ◽  
Manuela Bindl ◽  
Markus Bühner
Keyword(s):  
Stroop Effect ◽  
Color Word ◽  
Spatial Stroop Effect ◽  
Spatial Stroop

Computational Intelligence for Modeling Human Sensations in Virtual Environments

2004 ◽  
Vol 8 (3) ◽  
pp. 302-312
Author(s):  
Ka Keung Lee ◽  
◽  
Yangsheng Xu
Keyword(s):  
Virtual Environments ◽  
Computational Intelligence ◽  
Input Data ◽  
Markov Models ◽  
Body Motion ◽  
Input Selection ◽  
Physical Stimuli ◽  
The Relationship ◽  
Relative Similarity ◽  
Full Body

In this research, computational intelligence techniques are applied towards the modeling of human sensations in virtual environments. We specifically focus on the following important questions: (1) how to efficiently model the relationship between human sensations and the physical stimuli presented to humans, (2) how to validate the human sensation models, and (3) how to reduce the size of the input data when it gets large and how to select the information which is most important to human sensation modeling. In order to provide an experimental testbed for the implementation of the proposed learning and analysis techniques, a full-body motion virtual reality interface capable of recording human sensations is developed. We propose using cascade neural networks with node-decoupled extended Kalman filter training for modeling human sensation in virtual environments. For the purpose of sensation model validation, we propose using a stochastic similarity measure based on hidden Markov models to calculate the relative similarity between model-generated sensation and actual human sensation. Next, we investigate a number of feature extraction and input selection techniques for reducing the input data size in human sensation modeling. We propose and develop a new input selection method based on independent component analysis, which is capable of reducing the data size and selecting the stimuli information that is most important to the human sensation.

Stimulus-Response Incompatibility: Extra Processing Stages versus Response Competition

1979 ◽  
Vol 23 (1) ◽  
pp. 415-419
Author(s):  
Steven P. Rogers
Keyword(s):  
Processing Time ◽  
Response Competition ◽  
Reaction Times ◽  
Stimulus Response ◽  
Compatibility Factor ◽  
Two Factors ◽  
Additive Factor Method ◽  
Time Required ◽  
The Relationship ◽  
Spatial Stroop

The extra processing time required by incompatible S-R arrays has been explained in two ways: an increased number of processing stages or response competition from the compatible response. The additive factor method was employed, combining an S-R compatibility factor with a known response competition factor (a spatial Stroop task). The relationship between these two factors was shown to be an additive one, indicating that the two variables have their loci in (at least) two separate stages. This outcome is seen as persuasive evidence that longer reaction times for incompatible responses result from extra processing stages, rather than from response competition.

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