Concurrent statistical learning of target and distractor spatial probability collide on the same spatial priority map(s)

Statistical learning (SL) of both target and distractor spatial probability distributions adjusts the attentional priority of locations. In the presence of a single manipulation for each location, SL also induces indirect effects (e.g., changes in filtering efficiency due to an uneven distribution of targets), suggesting that SL-induced plastic changes are implemented within common spatial priority maps. Here we tested whether, when target- and distractor-related manipulations are concurrently applied to the very same locations, dedicated mechanisms might support the independent encoding of spatial priority in relation to the attentional operation involved. In three related experiments, human healthy participants discriminated the direction of a target arrow, while ignoring a salient distractor, if present; target and distractor spatial probability distributions were systematically manipulated in relation to each single location. Critically, the selection bias produced by the target-related SL was significantly reduced by an adverse distractor contingency. Conversely, the suppression bias generated by the distractor-related SL was erased, or even reversed, by an adverse target contingency. Our results suggest that independent and concomitant target- and distractor-related SL manipulations concur to the plastic adjustment of the same spatial priority map(s), with the resulting priority corresponding to some kind of weighted average of the SL processes.

Integration of Spatial Probability and Size in Slope-Unit-Based Landslide Susceptibility Assessment: A Case Study

Landslide spatial probability and size are two essential components of landslide susceptibility. However, in existing slope-unit-based landslide susceptibility assessment methods, landslide size has not been explicitly considered. This paper developed a novel slope-unit based approach for landslide susceptibility assessment that explicitly incorporates landslide size. This novel approach integrates the predicted occurrence probability (spatial probability) of landslides and predicted size (area) of potential landslides for a slope-unit to obtain a landslide susceptibility value for that slope-unit. The results of a case study showed that, from a quantitative point of view, integrating spatial probability and size in slope-unit-based landslide susceptibility assessment can bring remarkable increases of AUC (Area under the ROC curve) values. For slope-unit-based scenarios using the logistic regression method and the neural network method, the average increase of AUC brought by incorporating landslide size is up to 0.0627 and 0.0606, respectively. Slope-unit-based landslide susceptibility models incorporating landslide size had utilized the spatial extent information of historical landslides, which was dropped in models not incorporating landslide size, and therefore can make potential improvements. Nevertheless, additional case studies are still needed to further evaluate the applicability of the proposed approach.