People's attention is well attracted to a stimulus matching their memory. For example, when people are required to remember the color of a visual object, stimuli matching the memory color powerfully capture attention. Remarkably, stimuli with the shape of the memory object, that is, irrelevant-matching stimuli were also found to capture attention. Here, we examined how task relevance affects the temporal dynamics and the strength of memory-driven attention. In the experiment, participants performed a visual search task while maintaining the color or shape of a colored shape. When participants were required to memorize the color of the memory sample, the shape of the sample stimulus is task-irrelevant feature and vice versa. Importantly, while a search item matching working memory in the task-relevant dimension was presented for one group of participants, an irrelevant-matching search item appeared for the other group of participants. Further, we varied stimulus onset asynchrony (SOA) between the memory sample and search items. We found that relevant-matching stimuli captured attention regardless of whether the SOA was short or long. However, attentional capture by irrelevant-matching stimuli depended on the SOA; no memory-driven capture was observed at the shortest SOA, but significant capture was found at longer SOAs. Further, the capture effects by relevant-matching stimuli were greater than that of irrelevant-matching stimuli. These findings suggest both task-relevant and -irrelevant features in working memory affect the attentional selection in visual search task, but its temporal dynamics and strength are modulated by the task-relevance.
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http://dx.doi.org/10.1007/s10339-021-01069-8 | DOI Listing |
PLoS One
January 2025
Sensory Circuits and Neurotechnology Laboratory, The Francis Crick Institute, London, United Kingdom.
Odours released by objects in natural environments can contain information about their spatial locations. In particular, the correlation of odour concentration timeseries produced by two spatially separated sources contains information about the distance between the sources. For example, mice are able to distinguish correlated and anti-correlated odour fluctuations at frequencies up to 40 Hz, while insect olfactory receptor neurons can resolve fluctuations exceeding 100 Hz.
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Department of Chemistry, Center for BioAnalytical Chemistry, Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology, Tsinghua University, Beijing, 100084, China.
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View Article and Find Full Text PDFChaos
January 2025
School of Mathematical & Computer Sciences, Heriot-Watt University, EH14 4AS Edinburgh, United Kingdom.
Time-evolving graphs arise frequently when modeling complex dynamical systems such as social networks, traffic flow, and biological processes. Developing techniques to identify and analyze communities in these time-varying graph structures is an important challenge. In this work, we generalize existing spectral clustering algorithms from static to dynamic graphs using canonical correlation analysis to capture the temporal evolution of clusters.
View Article and Find Full Text PDFSci Adv
January 2025
Center for Nano Science and Technology, Fondazione Istituto Italiano di Tecnologia, Milano, Italy.
Achieving highly tailored control over both the spatial and temporal evolution of light's orbital angular momentum (OAM) on ultrafast timescales remains a critical challenge in photonics. Here, we introduce a method to modulate the OAM of light on a femtosecond scale by engineering a space-time coupling in ultrashort pulses. By linking azimuthal position with time, we implement an azimuthally varying Fourier transformation to dynamically alter light's spatial distribution in a fixed transverse plane.
View Article and Find Full Text PDFSci Adv
January 2025
Institute of Materials Research and Engineering (IMRE), Agency for Science Technology and Research (A*STAR), 2 Fusionopolis Way, #08-03 Innovis, Singapore 138634, Republic of Singapore.
Combining physics with computational models is increasingly recognized for enhancing the performance and energy efficiency in neural networks. Physical reservoir computing uses material dynamics of physical substrates for temporal data processing. Despite the ease of training, building an efficient reservoir remains challenging.
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