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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.


Cell activations in Bridgeman’s (1978) model for the conditions (1) Vernier only, (2) Vernier							followed by a five-element grating, (3) Vernier followed by a 25-element							grating, (4) Vernier followed by a 25-element grating with gaps. The							value p in the subplot titles refers to the sum of the squared							correlation over time between the activation for condition (1) and the							respective condition. The higher the value of, the higher the predicted							per-formance. The values indicate that the model fails to explain why a							5-element grating (2), and the 25-element grating with gaps (4) are much							stronger masks than the 25-element grating (3).
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Figure 7: Cell activations in Bridgeman’s (1978) model for the conditions (1) Vernier only, (2) Vernier followed by a five-element grating, (3) Vernier followed by a 25-element grating, (4) Vernier followed by a 25-element grating with gaps. The value p in the subplot titles refers to the sum of the squared correlation over time between the activation for condition (1) and the respective condition. The higher the value of, the higher the predicted per-formance. The values indicate that the model fails to explain why a 5-element grating (2), and the 25-element grating with gaps (4) are much stronger masks than the 25-element grating (3).

Mentions: Two findings suggest that breaking up the regularity of the mask increases its masking strength. Herzog et al. (fig. 4; 2001) introduced two gaps in the grating by removing two elements (illustrated in the left plot of Figure 4A), which resulted in a grating consisting of five central elements and two more distant groups of nine elements. The removal of the two grating elements strongly increased the strength of the mask. Similarly, Herzog et al. (fig. 7A; 2004) increased the luminance of the two elements at position offsets +2 and −2 from the Vernier, as illustrated in the right part of Figure 4A. Also, this slight change in mask layout resulted in a strong increase in the masking strength.


Visual backward masking: Modeling spatial and temporal aspects.

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

Cell activations in Bridgeman’s (1978) model for the conditions (1) Vernier only, (2) Vernier							followed by a five-element grating, (3) Vernier followed by a 25-element							grating, (4) Vernier followed by a 25-element grating with gaps. The							value p in the subplot titles refers to the sum of the squared							correlation over time between the activation for condition (1) and the							respective condition. The higher the value of, the higher the predicted							per-formance. The values indicate that the model fails to explain why a							5-element grating (2), and the 25-element grating with gaps (4) are much							stronger masks than the 25-element grating (3).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 7: Cell activations in Bridgeman’s (1978) model for the conditions (1) Vernier only, (2) Vernier followed by a five-element grating, (3) Vernier followed by a 25-element grating, (4) Vernier followed by a 25-element grating with gaps. The value p in the subplot titles refers to the sum of the squared correlation over time between the activation for condition (1) and the respective condition. The higher the value of, the higher the predicted per-formance. The values indicate that the model fails to explain why a 5-element grating (2), and the 25-element grating with gaps (4) are much stronger masks than the 25-element grating (3).
Mentions: Two findings suggest that breaking up the regularity of the mask increases its masking strength. Herzog et al. (fig. 4; 2001) introduced two gaps in the grating by removing two elements (illustrated in the left plot of Figure 4A), which resulted in a grating consisting of five central elements and two more distant groups of nine elements. The removal of the two grating elements strongly increased the strength of the mask. Similarly, Herzog et al. (fig. 7A; 2004) increased the luminance of the two elements at position offsets +2 and −2 from the Vernier, as illustrated in the right part of Figure 4A. Also, this slight change in mask layout resulted in a strong increase in the masking strength.

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.