Allocation of Attention to Visual and Nonvisual Perceptual Channels by Marksmen During Aiming: Skill-Level Differences.

This study examined attention allocation in 30 marksmen categorized into 3 skill levels ranging from expert to novice. Each shooter performed 336 shooting trials. Half of the trials were performed under an occluded-vision condition and the rest under regular, unoccluded conditions. Immediately after completion of a random subset of shots (96 trials), shooters estimated the actual location of each shot, and on a random subset of trials (48 trials), shooters gave retrospective verbal reports. A mixed 3 × 2 factorial analysis of variance revealed that the expert marksmen performed and estimated their shots more accurately than the intermediate and novice marksmen, the intermediates performed like the experts under the full-vision condition and like novices under the occluded-vision condition, and the experts reported attending more to nonvisual information while they estimated their shots than did the novices. The findings advance our understanding of the mechanisms mediating expertise.

Extended categorization of conjunction object stimuli decreases the latency of attentional feature selection and recruits orthography-linked ERPs.

The role of attention in driving perceptual expertise effects is controversial. The current study addressed the effect of training on ERP components related to and independent of attentional feature selection. Participants learned to categorize cartoon animals over six training sessions (8,800 trials) after which ERPs were recorded during a target detection task performed on trained and untrained stimulus sets. The onset of the selection negativity, an ERP component indexing attentional modulation, was about 60 msec earlier for trained than untrained stimuli. Trained stimuli also elicited centro-parietal N200 and N320 components that were insensitive to attentional feature selection. The scalp distribution and timecourse of these components were better matched by studies of orthography than object expertise. Source localization using eLORETA suggested that the strongest neural sources of the selection negativity were in right ventral temporal cortex whereas the strongest sources of the N200/N320 components were in left ventral temporal cortex, again consistent with the hypothesis that training recruited orthography related areas. Overall, training altered neural processes related to attentional selection, but also affected neural processes that were independent of feature selection.

The effect of category learning on visual attention and visual representation.

Subordinate-level category learning recruits neural resources associated with perceptual expertise, including the N250 component of the ERP, a posterolateral negative wave maximal between 230 and 330 ms. The N250 is a relatively late visual ERP and could plausibly be driven by attention to the features of categorized objects. Indeed, it has a latency and scalp distribution similar to the selection negativity (SN), an ERP component long known to be sensitive to attentional selection of target features. To clarify sensitivity of the N250 to attention and to more generally investigate the effect of category learning on attentional modulation of learned features, we independently manipulated subordinate-level category learning and target detection in a speeded paradigm designed to optimally elicit the SN and accompanying frontal selection positivity (FSP). Participants first practiced categorizing a set of artificial animal stimuli and then performed a speeded target detection task on trained and untrained stimuli while ERPs were recorded. SN and FSP were roughly linearly related to the number of target features in the stimulus. Trained stimuli elicited a significantly larger N250 than untrained stimuli. The SN and N250 effects were additive, with all levels of target similarity equally affected by training, and had different time courses. Training had little effect on the FSP. The results suggest that (a) the N250 and SN have different sources, and (b) at the very least, the learning-induced N250 indexes a different attentional subprocess from the target-induced SN and could be driven by a different cognitive process altogether.