In fast vision, local spatial properties of the visual scene can automatically capture the observer's attention. We used specific local features, predicted by a constrained maximum-entropy model to be optimal information-carriers, as candidate "salient features''. Previous studies showed that participants choose these optimal features as "more salient" if explicitly asked. Here, we investigated the implicit saliency of these optimal features in two attentional tasks. In a covert-attention experiment, we measured the luminance-contrast threshold for discriminating the orientation of a peripheral gabor. In a gaze-orienting experiment, we analyzed latency and direction of saccades towards a peripheral target. In both tasks, two brief peripheral cues, differing in saliency according to the model, preceded the target, presented on the same (valid trials) or the opposite side (invalid trials) of the optimal cue. Results showed reduced contrast thresholds, saccadic latencies, and direction errors in valid trials, and the opposite in invalid trials, compared to baseline values obtained with equally salient cues. Also, optimal features triggered more anticipatory saccades. Similar effects emerged in a luminance-control condition. Overall, in fast vision, optimal features automatically attract covert and overt attention, suggesting that saliency is determined by information maximization criteria coupled with computational limitations.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9200825 | PMC |
http://dx.doi.org/10.1038/s41598-022-14262-2 | DOI Listing |
Arch Pathol Lab Med
January 2025
From the Divisions of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas (Gan, Y Ding, Wu, Zhang, Meng, QQ Ding, Han).
Objective.—: To report the isolation and significance of C kroppenstedtii, features of patients with GLM, pathologic findings and mechanism, bacteriologic workup, and optimal treatment.
Design.
Discov Oncol
January 2025
Department of Neurosurgery, Changde Hospital, Xiangya School of Medicine, Central South University (The First People's Hospital of Changde City), Changde, 415003, Hunan, China.
Purpose: Glioma is the most prevalent tumor of the central nervous system. The poor clinical outcomes and limited therapeutic efficacy underscore the urgent need for early diagnosis and an optimized prognostic approach for glioma. Therefore, the aim of this study was to identify sensitive biomarkers for glioma.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Macromolecular Physics, Faculty of Mathematics and Physics, Charles University, V Holešovičkách 2, 180 00, Prague 8, Czech Republic.
Vanadium dioxide (VO) is a phase transition material that undergoes semiconductor-to-metal transition at the temperature of about 68 °C. This extraordinary feature triggered intensive research focused on the controlled synthesis of VO. In this study, we introduce and investigate an original linker- and solvent-free strategy enabling the production of highly porous VO nanoparticle-based films.
View Article and Find Full Text PDFNat Commun
January 2025
Bioinformatics and computational systems biology of cancer, Institut Curie, Inserm U900, PSL Research University, Paris, France.
Immunotherapy is improving the survival of patients with metastatic non-small cell lung cancer (NSCLC), yet reliable biomarkers are needed to identify responders prospectively and optimize patient care. In this study, we explore the benefits of multimodal approaches to predict immunotherapy outcome using multiple machine learning algorithms and integration strategies. We analyze baseline multimodal data from a cohort of 317 metastatic NSCLC patients treated with first-line immunotherapy, including positron emission tomography images, digitized pathological slides, bulk transcriptomic profiles, and clinical information.
View Article and Find Full Text PDFBMC Bioinformatics
January 2025
School of Information and Artificial Intelligence, Anhui Agricultural University, Changjiang West Road, Hefei, 230036, Anhui, China.
Drug-target interactions (DTIs) are pivotal in drug discovery and development, and their accurate identification can significantly expedite the process. Numerous DTI prediction methods have emerged, yet many fail to fully harness the feature information of drugs and targets or address the issue of feature redundancy. We aim to refine DTI prediction accuracy by eliminating redundant features and capitalizing on the node topological structure to enhance feature extraction.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!