Visual backward masking: Modeling spatial and temporal aspects

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.

Keywords: visual backward masking.

Figure 1.

The general setup of the model. The input, which is coded as an array of ones and zeros is fed into an inhibitory and an excitatory layer via a Mexican-hat filter. The activation of these layers is updated over time.

Figure 2.

Stimulus sequence (A) and simulation results (B) of data presented by Herzog et al. (2001). A Vernier target was masked by a grating consisting of either five (left) or 25 elements (right). The model correctly predicts that the five-element grating masks the Vernier much more strongly than the 25-element grating.

Figure 3.

Stimulus sequence (A) and simulation results (B) of data presented by Herzog et al. (2003). A Vernier target was masked by a field of light of the size either of either five (left) or 25 elements (right). The model correctly predicts that the five-element size field masks the Vernier much more strongly than that of the size of a 25-element grating, as indicated by the longer Vernier trace for the 25-element grating in the center of the image of the network activation.

Figure 4.

Stimulus sequences (A) and simulation results (B). A Vernier target was followed either by a grating with two gaps at offset positions +/-2 from the Vernier, or two elements of double luminance at these positions. Experimental data showed that both masks yield a strong increase in offset discrimination thresholds with respect to the standard grating. The simulations show that the model can well detect the irregularities in the mask, and explain how these irregularities result in an increase in masking strength. The irregularities are associated with strong network activation causing strong inhibition in their immediate surroundings that suppresses activation of the target, because the irregularities were close to the target.

Figure 5.

The activation in the excitatory population over time (vertical dimension) for different sizes of the shift of the center of the grating to the right. The small red horizontal bar indicates where the activity at the center drops below a certain value. The model predicts that when the grating’s edge approaches the Vernier, the Vernier’s trace is strongly reduced, implying much worse performance on the Vernier.

Figure 6.

The sequence of Vernier and mask (left) and Vernier offset discrimination thresholds for observer FH (right) as a function of the size of the shift of the center of the grating mask. The data confirm the model’s prediction that a close edge yields strong inhibition of the Vernier’s signal, reflected in higher offset discrimination thresholds.

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).

Figure 8.

Stimulus sequence (A) and simulation results (B) of data presented by Herzog et al. (2001). The small red horizontal bars indicate where the activity of the trace drops below a particular threshold. A Vernier target was masked by a grating consisting of a five-element center and a 20-element surround, which were presented at different onset times. Once presented, the stimulus remained on the screen until 300 ms after target offset. The model correctly predicts that the target strength remains strongest for simultaneous onset of the mask’s center and surround.

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.