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Visual backward masking: Modeling spatial and temporal aspects.

Hermens F, Ernst U - Adv Cogn Psychol (2008)

Bottom Line: In modeling visual backward masking, the focus has been on temporal effects.Although interesting effects of the spatial layout of the mask have been found, only a few attempts have been made to model these phenomena.We argue that for better understanding of visual masking, it is vitally important to consider the interplay of spatial and temporal factors together in one single model.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Psychophysics, Brain Mind Institutem, Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland.

ABSTRACT
In modeling visual backward masking, the focus has been on temporal effects. More specifically, an explanation has been sought as to why strongest masking can occur when the mask is delayed with respect to the target. Although interesting effects of the spatial layout of the mask have been found, only a few attempts have been made to model these phenomena. Here, we elaborate a structurally simple model which employs lateral excitation and inhibition together with different neural time scales to explain many spatial and temporal aspects of backward masking. We argue that for better understanding of visual masking, it is vitally important to consider the interplay of spatial and temporal factors together in one single model.

No MeSH data available.


Stimulus sequence (left) and responses of the excitatory population							(right) for which optimal masking at a non-zero SOA occurs. The small							red horizontal bars indicate where the activity of the trace drops below							a particular threshold. The Vernier’s trace is long for a zero SOA, then							decreases in length for intermediate SOAs, and returns to full length							again at long SOAs, indicating that masking is strongest at intermediate							SOAs.
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Figure 9: Stimulus sequence (left) and responses of the excitatory population (right) for which optimal masking at a non-zero SOA occurs. The small red horizontal bars indicate where the activity of the trace drops below a particular threshold. The Vernier’s trace is long for a zero SOA, then decreases in length for intermediate SOAs, and returns to full length again at long SOAs, indicating that masking is strongest at intermediate SOAs.

Mentions: In the introduction, we mentioned the relatively strong focus of the masking research community on explaining that masking can be strongest at a non-zero SOA (B-type masking). The work by Francis (2000) suggests that many models that apply a non-linearity (rectification) and decay can explain B-type masking. As our version of the Wilson-Cowan model contains both properties, we would expect that a combination of target and mask can be found for which the model shows strongest masking at a non-zero SOA. Figure 9 shows such a combination (left), together with the corresponding network responses (right). The small red horizontal bars indicate where the activity of the trace drops below a particular value. For short SOAs, the target’s trace is long. For intermediate SOAs, the length of the trace decreases, to increase again with longer SOAs. This pattern of trace lengths as a function of SOA suggests a U-shaped dependence of predicted performance on SOA.


Visual backward masking: Modeling spatial and temporal aspects.

Hermens F, Ernst U - Adv Cogn Psychol (2008)

Stimulus sequence (left) and responses of the excitatory population							(right) for which optimal masking at a non-zero SOA occurs. The small							red horizontal bars indicate where the activity of the trace drops below							a particular threshold. The Vernier’s trace is long for a zero SOA, then							decreases in length for intermediate SOAs, and returns to full length							again at long SOAs, indicating that masking is strongest at intermediate							SOAs.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2864987&req=5

Figure 9: Stimulus sequence (left) and responses of the excitatory population (right) for which optimal masking at a non-zero SOA occurs. The small red horizontal bars indicate where the activity of the trace drops below a particular threshold. The Vernier’s trace is long for a zero SOA, then decreases in length for intermediate SOAs, and returns to full length again at long SOAs, indicating that masking is strongest at intermediate SOAs.
Mentions: In the introduction, we mentioned the relatively strong focus of the masking research community on explaining that masking can be strongest at a non-zero SOA (B-type masking). The work by Francis (2000) suggests that many models that apply a non-linearity (rectification) and decay can explain B-type masking. As our version of the Wilson-Cowan model contains both properties, we would expect that a combination of target and mask can be found for which the model shows strongest masking at a non-zero SOA. Figure 9 shows such a combination (left), together with the corresponding network responses (right). The small red horizontal bars indicate where the activity of the trace drops below a particular value. For short SOAs, the target’s trace is long. For intermediate SOAs, the length of the trace decreases, to increase again with longer SOAs. This pattern of trace lengths as a function of SOA suggests a U-shaped dependence of predicted performance on SOA.

Bottom Line: In modeling visual backward masking, the focus has been on temporal effects.Although interesting effects of the spatial layout of the mask have been found, only a few attempts have been made to model these phenomena.We argue that for better understanding of visual masking, it is vitally important to consider the interplay of spatial and temporal factors together in one single model.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Psychophysics, Brain Mind Institutem, Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland.

ABSTRACT
In modeling visual backward masking, the focus has been on temporal effects. More specifically, an explanation has been sought as to why strongest masking can occur when the mask is delayed with respect to the target. Although interesting effects of the spatial layout of the mask have been found, only a few attempts have been made to model these phenomena. Here, we elaborate a structurally simple model which employs lateral excitation and inhibition together with different neural time scales to explain many spatial and temporal aspects of backward masking. We argue that for better understanding of visual masking, it is vitally important to consider the interplay of spatial and temporal factors together in one single model.

No MeSH data available.