When the eyes have it and when not: How multiple sources of activation combine to guide eye movements during multiattribute decision making.

Memory plays a major but underexplored role in judgment and decision making (JDM). Studying eye movements-especially how people look at empty spatial locations when retrieving from memory information previously associated with those locations-provides useful information about how memory influences JDM. This so-called looking-at-nothing behavior is thought to reflect memory-driven allocation of attention. However, eye movements are also guided toward salient visual stimuli, such as test items presented on a screen. It is unclear how these multiple sources of activation combine to guide looking-at-nothing in JDM. We investigated this question in two experiments in which participants solved multiattribute categorization tasks using an exemplar-based decision strategy. In the first experiment, we tested how the occurrence and the strength of looking-at-nothing vary with the presentation format and the amount of training participants received. Looking-at-nothing occurred during categorizations when test-item information was presented auditorily and visually, but for the latter only after visual information was removed from the screen. It occurred both when training items were learned by heart and when they were presented 10 times on the screen. A second experiment revealed that an explicit instruction to imagine retrieval-relevant information during categorizations increased looking-at-nothing but did not change the decision-making process. The results shed light on the interaction between eye movements and attention to information in memory during JDM that can be explained in light of a shared priority map in memory. A detailed understanding of this interaction forms the basis for using eye movements to study memory processes in JDM. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The relevance of rock shape over mass-implications for rockfall hazard assessments.

The mitigation of rapid mass movements involves a subtle interplay between field surveys, numerical modelling, and experience. Hazard engineers rely on a combination of best practices and, if available, historical facts as a vital prerequisite in establishing reproducible and accurate hazard zoning. Full-scale field tests have been performed to reinforce the physical understanding of debris flows and snow avalanches. Rockfall dynamics are - especially the quantification of energy dissipation during the complex rock-ground interaction - largely unknown. The awareness of rock shape dependence is growing, but presently, there exists little experimental basis on how rockfall hazard scales with rock mass, size, and shape. Here, we present a unique data set of induced single-block rockfall events comprising data from equant and wheel-shaped blocks with masses up to 2670 kg, quantifying the influence of rock shape and mass on lateral spreading and longitudinal runout and hence challenging common practices in rockfall hazard assessment.

Towards Edge-Aware Spatio-Temporal Filtering in Real-Time.

Spatio-temporal edge-aware (STEA) filtering methods have recently received increased attention due to their ability to efficiently solve or approximate important image-domain problems in a temporally consistent manner - which is a crucial property for video-processing applications. However, existing STEA methods are currently unsuited for real-time, embedded stream-processing settings due to their high processing latency, large memory, and bandwidth requirements, and the need for accurate optical flow to enable filtering along motion paths. To this end, we propose an efficient STEA filtering pipeline based on the recently proposed permeability filter (PF), which offers high quality and halo reduction capabilities. Using mathematical properties of the PF, we reformulate its temporal extension as a causal, non-linear infinite impulse response filter, which can be efficiently evaluated due to its incremental nature. We bootstrap our own accurate flow using the PF and its temporal extension by interpolating a quasi-dense nearest neighbour field obtained with an improved PatchMatch algorithm, which employs binarized octal orientation maps (BOOM) descriptors to find correspondences among subsequent frames. Our method is able to create temporally consistent results for a variety of applications such as optical flow estimation, sparse data upsampling, visual saliency computation and disparity estimation. We benchmark our optical flow estimation on the MPI Sintel dataset, where we currently achieve a Pareto optimal quality-efficiency tradeoff with an average endpoint error of 7.68 at 0.59 s single-core execution time on a recent desktop machine.

Reliability of intraoperative neurophysiological monitoring using motor evoked potentials during resection of metastases in motor-eloquent brain regions: clinical article.

OBJECT: Resection of gliomas in or adjacent to the motor system is widely performed using intraoperative neuromonitoring (IOM). For resection of cerebral metastases in motor-eloquent regions, however, data are sparse and IOM in such cases is not yet widely described. Since recent studies have shown that cerebral metastases infiltrate surrounding brain tissue, this study was undertaken to assess the value and influence of IOM during resection of supratentorial metastases in motor-eloquent regions. METHODS: Between 2006 and 2011, the authors resected 206 consecutive supratentorial metastases, including 56 in eloquent motor areas with monitoring of monopolar direct cortically stimulated motor evoked potentials (MEPs). The authors evaluated the relationship between the monitoring data and the course of surgery, clinical data, and postoperative imaging. RESULTS: Motor evoked potential monitoring was successful in 53 cases (93%). Reduction of MEP amplitude correlated better with postoperative outcomes when the threshold for significant amplitude reduction was set at 80% (only > 80% reduction was considered significant decline) than when it was set at 50% (> 50% amplitude reduction was considered significant decline). Evidence of residual tumor was seen on MR images in 28% of the cases with significant MEP reduction. No residual tumor was seen in any case of stable MEP monitoring. Moreover, preoperative motor deficit, recursive partitioning analysis Class 3, and preoperative radiotherapy were independent risk factors for a new surgery-related motor weakness (occurring in 64% of patients with and 11% of patients without radiotherapy, p > 0.01). CONCLUSIONS: Continuous MEP monitoring provides reliable monitoring of the motor system and also influences the course of operation in resection of cerebral metastases. However, in establishing warning criteria, only an amplitude decline > 80% of the baseline should be considered significant.