A method for detection of inattentional feature blindness

Attention in redundancy masking

Peripheral vision is limited due to several factors, such as visual resolution, crowding, and attention. When attention is not directed towards a stimulus, detection, discrimination, and identification are often compromised. Recent studies have found a new phenomenon that strongly limits peripheral vision, "redundancy masking". In redundancy masking, the number of perceived items in repeating patterns is reduced. For example, when presenting three lines in the peripheral visual field and asking participants to report the number of lines, often only two lines are reported. Here, we investigated what role attention plays in redundancy masking. If redundancy masking was due to limited attention to the target, it should be stronger when less attention is allocated to the target, and absent when attention is maximally focused on the target. Participants were presented with line arrays and reported the number of lines in three cueing conditions (i.e., single cue, double cue, and no cue). Redundancy masking was observed in all cueing conditions, with observers reporting fewer lines than presented in the single, double, and no cue conditions. These results suggest that redundancy masking is not due to limited attention. The number of lines reported was closer to the correct number of lines in the single compared to the double and the no cue conditions, suggesting that reduced attention additionally compromised stimulus discrimination, and replicating typical effects of diminished attention. Taken together, our results suggest that the extent of attention to peripherally presented stimuli modulates discrimination performance, but does not account for redundancy masking.

How much time does it take to discriminate two sets by their numbers of elements?

The ability to evaluate the number of elements in a set-numerosity-without symbolic representation is a form of primitive perceptual intelligence. A simple binomial model was proposed to explain how observers discriminate the numerical proportion between two sets of elements distinct in color or orientation (Raidvee et al., 2017, Attention Perception & Psychophysics, 79[1], 267-282). The binomial model's only parameter β is the probability with which each visual element can be noticed and registered by the perceptual system. Here we analyzed the response times (RT) which were ignored in the previous report since there were no instructions concerning response speed. The relationship between the mean RT and the absolute difference |ΔN| between numbers of elements in two sets was described by a linear regression, the slope of which became flatter as the total number of elements N increased. Because the coefficients of regression between the mean RT and |ΔN| were more directly related to the binomial probability β rather than to the standard deviation of the best fitting cumulative normal distribution, it was regarded as evidence that the binomial model with a single parameter - probability β - is a viable alternative to the customary Thurstonian-Gaussian model.

How are local orientation signals pooled?

Visual perception is capable of pooling multiple local orientation signals into a single more accurate summary orientation. However, there is still a lack of systematic inquiry into which summary statistics are implemented in that process. Here, the task was to recognize in which direction, clockwise or counter-clockwise, the mean orientation of a set of randomly distributed Gabor patches (N = 1, 2, 4, and 8) was rotated from the implicit vertical. The mean orientation discrimination accuracy did not improve with the increase of the number N of elements in proportion to the square-root-N, as could be expected if noisy internal representations were arithmetically averaged. The Proportion of Informative Elements (PIE), defined as the percentage of elements having an orientation different from the vertical, also affected the discrimination precision, violating the arithmetic averaging rules. The decrease in the orientation discrimination precision with the increase of the PIE would suggest that the orientation pooling could be more adequately described by a quadratic or higher power mean. Thus, we parameterized the averaging process for the power parameter of the generalized mean formula. The results indicate that different pooling rules in different trials may apply, for example, the arithmetic mean in some and the maximal deviation rule in others. It is concluded that pooling of orientation information is a relatively inaccurate process for which different perceptual cues and their combination rules can be used.