Visual search is speeded when a target item is positioned consistently within an invariant (repeatedly encountered) configuration of distractor items ("contextual cueing"). Contextual cueing is also observed in cross-modal search, when the location of the-visual-target is predicted by distractors from another-tactile-sensory modality. Previous studies examining lateralized waveforms of the event-related potential (ERP) with millisecond precision have shown that learned visual contexts improve a whole cascade of search-processing stages. Drawing on ERPs, the present study tested alternative accounts of contextual cueing in tasks in which distractor-target contextual associations are established across, as compared to, within sensory modalities. To this end, we devised a novel, cross-modal search task: search for a visual feature singleton, with repeated (and nonrepeated) distractor configurations presented either within the same (visual) or a different (tactile) modality. We found reaction times (RTs) to be faster for repeated versus nonrepeated configurations, with comparable facilitation effects between visual (unimodal) and tactile (crossmodal) context cues. Further, for repeated configurations, there were enhanced amplitudes (and reduced latencies) of ERPs indexing attentional allocation (PCN) and postselective analysis of the target (CDA), respectively; both components correlated positively with the RT facilitation. These effects were again comparable between uni- and crossmodal cueing conditions. In contrast, motor-related processes indexed by the response-locked LRP contributed little to the RT effects. These results indicate that both uni- and crossmodal context cues benefit the same, visual processing stages related to the selection and subsequent analysis of the search target.
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Prog Neuropsychopharmacol Biol Psychiatry
January 2025
Department of Pharmacology, Federal University of Parana, Curitiba, Parana, Brazil. Electronic address:
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View Article and Find Full Text PDFECAI 2024 (2024)
January 2024
Department of Computer Science, University of Kentucky.
Long-term time-series forecasting remains challenging due to the difficulty in capturing long-term dependencies, achieving linear scalability, and maintaining computational efficiency. We introduce TimeMachine, an innovative model that leverages Mamba, a state-space model, to capture long-term dependencies in multivariate time series data while maintaining linear scalability and small memory footprints. TimeMachine exploits the unique properties of time series data to produce salient contextual cues at multi-scales and leverage an innovative integrated quadruple-Mamba architecture to unify the handling of channel-mixing and channel-independence situations, thus enabling effective selection of contents for prediction against global and local contexts at different scales.
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Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA; Medical Discovery Team on Addiction, University of Minnesota, Minneapolis, MN 55455, USA. Electronic address:
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View Article and Find Full Text PDFDisabil Rehabil Assist Technol
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School of Rehabilitation Therapy, Queen's University, Kingston, Ontario, Canada.
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