In visual search, context information can serve as a cue to guide attention to the target location. When observers repeatedly encounter displays with identical target-distractor arrangements, reaction times (RTs) are faster for repeated relative to nonrepeated displays, the latter containing novel configurations. This effect has been termed "contextual cueing." The present study asked whether information about the target location in repeated displays is "explicit" (or "conscious") in nature. To examine this issue, observers performed a test session (after an initial training phase in which RTs to repeated and nonrepeated displays were measured) in which the search stimuli were presented briefly and terminated by visual masks; following this, observers had to make a target localization response (with accuracy as the dependent measure) and indicate their visual experience and confidence associated with the localization response. The data were examined at the level of individual displays, i.e., in terms of whether or not a repeated display actually produced contextual cueing. The results were that (a) contextual cueing was driven by only a very small number of about four actually learned configurations; (b) localization accuracy was increased for learned relative to nonrepeated displays; and (c) both consciousness measures were enhanced for learned compared to nonrepeated displays. It is concluded that contextual cueing is driven by only a few repeated displays and the ability to locate the target in these displays is associated with increased visual experience.
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http://dx.doi.org/10.1167/12.11.25 | DOI Listing |
Health Psychol Behav Med
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Department of Psychology, Northumbria University, Newcastle upon Tyne, UK.
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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)
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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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