Handgrip Based Action Information Modulates Attentional Selection: An ERP Study

Prior work shows that the possibility of action to an object (visual affordance) facilitates attentional deployment. We sought to investigate the neural mechanisms underlying this modulation of attention by examining ERPs to target objects that were either congruently or incongruently gripped for their use in the presence of a congruently or incongruently gripped distractor. Participants responded to the presence or absence of a target object matching a preceding action word with a distractor object presented in the opposite location. Participants were faster in responding to congruently gripped targets compared to incongruently gripped targets. There was a reduced N2pc potential when the target was congruently gripped, and the distractor was incongruently gripped compared to the conditions where targets were incongruently gripped or when the distractor, as well as target, was congruently gripped. The N2pc results indicate that target selection is easier when action information is congruent with an object’s use.

Multiscale Spatial Polygonal Object Granularity Factor Matching Method Based on BPNN

Spatial object matching is one of the fundamental technologies used for updating and merging spatial data. This study focused mainly on the matching optimization of multiscale spatial polygonal objects. We proposed a granularity factor evaluation index that was developed to promote the recognition ability of complex matches in multiscale spatial polygonal object matching. Moreover, we designed the granularity factor matching model based on a backpropagation neural network (BPNN) and designed a multistage matching workflow. Our approach was validated experimentally using two topographical datasets at two different scales: 1:2000 and 1:10,000. Our results indicate that the granularity factor is effective both in improving the matching score of complex matching and reducing the occurrence of missing matching, and our matching model is suitable for multiscale spatial polygonal object matching, with a high precision and recall reach of 97.2% and 90.6%.