Interplay between supramodal attentional control and capacity limits in the low-level visual processors modulate the tendency to inattention.

When engaged in a demanding task, individuals may neglect unexpected visual stimuli presented concomitantly. Here we use a change detection task to show that propensity to inattention is associated with a flexible allocation of attentional resources to filter and represent visual information. This was reflected by N2 posterior contralateral (N2pc) and contralateral delay activity (CDA) respectively, but also during high-order reorienting of attentional resources (known as anterior directing attention negativity, ADAN). Results show that differences in noticing and failing to notice unexpected stimuli/changes are associated with different patterns of brain activity. When processing (N2) and working memory (CDA) capacities are low, resources are mostly allocated to small set-sizes and associated with a tendency to filter information during early low-level processing (N2). When resources are high, saturation is obtained with larger set-sizes. This is also associated to a tendency to select (N2) and reorient resources (ADAN) to maintain extra information (CDA).

Attentional gain and processing capacity limits predict the propensity to neglect unexpected visual stimuli.

Exogenous allocation of attentional resources allows the visual system to encode and maintain representations of stimuli in visual working memory (VWM). However, limits in the processing capacity to allocate resources can prevent unexpected visual stimuli from gaining access to VWM and thereby to consciousness. Using a novel approach to create unbiased stimuli of increasing saliency, we investigated visual processing during a visual search task in individuals who show a high or low propensity to neglect unexpected stimuli. When propensity to inattention is high, ERP recordings show a diminished amplification concomitantly with a decrease in theta band power during the N1 latency, followed by a poor target enhancement during the N2 latency. Furthermore, a later modulation in the P3 latency was also found in individuals showing propensity to visual neglect, suggesting that more effort is required for conscious maintenance of visual information in VWM. Effects during early stages of processing (N80 and P1) were also observed suggesting that sensitivity to contrasts and medium-to-high spatial frequencies may be modulated by low-level saliency (albeit no statistical group differences were found). In accordance with the Global Workplace Model, our data indicate that a lack of resources in low-level processors and visual attention may be responsible for the failure to "ignite" a state of high-level activity spread across several brain areas that is necessary for stimuli to access awareness. These findings may aid in the development of diagnostic tests and intervention to detect/reduce inattention propensity to visual neglect of unexpected stimuli.

Artificially created stimuli produced by a genetic algorithm using a saliency model as its fitness function show that Inattentional Blindness modulates performance in a pop-out visual search paradigm.

Salient stimuli are more readily detected than less salient stimuli, and individual differences in such detection may be relevant to why some people fail to notice an unexpected stimulus that appears in their visual field whereas others do notice it. This failure to notice unexpected stimuli is termed 'Inattentional Blindness' and is more likely to occur when we are engaged in a resource-consuming task. A genetic algorithm is described in which artificial stimuli are created using a saliency model as its fitness function. These generated stimuli, which vary in their saliency level, are used in two studies that implement a pop-out visual search task to evaluate the power of the model to discriminate the performance of people who were and were not Inattentionally Blind (IB). In one study the number of orientational filters in the model was increased to check if discriminatory power and the saliency estimation for low-level images could be improved. Results show that the performance of the model does improve when additional filters are included, leading to the conclusion that low-level images may require a higher number of orientational filters for the model to better predict participants' performance. In both studies we found that given the same target patch image (i.e. same saliency value) IB individuals take longer to identify a target compared to non-IB individuals. This suggests that IB individuals require a higher level of saliency for low-level visual features in order to identify target patches.