Retention of features in visual short-term memory (VSTM) involves maintenance of sensory traces in early visual cortex. However, the mechanism through which this is accomplished is not known. Here, we formulate specific hypotheses derived from studies on feature-based attention to test the prediction that visual cortex is recruited by attentional mechanisms during VSTM of low-level features. Functional magnetic resonance imaging (fMRI) of human visual areas revealed that neural populations coding for task-irrelevant feature information are suppressed during maintenance of detailed spatial frequency memory representations. The narrow spectral extent of this suppression agrees well with known effects of feature-based attention. Additionally, analyses of effective connectivity during maintenance between retinotopic areas in visual cortex show that the observed highlighting of task-relevant parts of the feature spectrum originates in V4, a visual area strongly connected with higher-level control regions and known to convey top-down influence to earlier visual areas during attentional tasks. In line with this property of V4 during attentional operations, we demonstrate that modulations of earlier visual areas during memory maintenance have behavioral consequences, and that these modulations are a result of influences from V4.
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http://dx.doi.org/10.1016/j.neuropsychologia.2014.11.004 | DOI Listing |
Sensors (Basel)
January 2025
Department of Earth, Environment and Geospatial Sciences, Saint Louis University, Saint Louis, MO 63108, USA.
Wheat is a globally cultivated cereal crop with substantial protein content present in its seeds. This research aimed to develop robust methods for predicting seed protein concentration in wheat seeds using bench-top hyperspectral imaging in the visible, near-infrared (VNIR), and shortwave infrared (SWIR) regions. To fully utilize the spectral and texture features of the full VNIR and SWIR spectral domains, a computer-vision-aided image co-registration methodology was implemented to seamlessly align the VNIR and SWIR bands.
View Article and Find Full Text PDFFood Chem
January 2025
Department of Pharmaceutical Analysis, School of Pharmacy, Second Military Medical University, Shanghai, China; Shanghai Key Laboratory for Pharmaceutical Metabolite Research, School of Pharmacy, Second Military Medical University, Shanghai, China. Electronic address:
Pomegranate seeds, a by-product of pomegranate processing, are gaining attention in food industries due to their high antioxidant activity. However, the lack of quality markers reflecting activity and spatial characteristics limits their utilization and product stability. In this research, a selective and sensitive method integrating ultra performance liquid chromatography-quadrupole time-of-flight mass spectrometry with feature-based molecular networking, and desorption electrospray ionization-mass spectrometry imaging developed to identify components and locate in-situ images of quality markers via spatial metabolomics analysis.
View Article and Find Full Text PDFInterference from a salient distractor is typically reduced when the appearance of the distractor follows either spatial or feature-based regularities. Although there is a growing body of literature on distractor location learning, the understanding of distractor feature learning remains limited. In the current study, we investigated distractor feature learning by using EEG measures.
View Article and Find Full Text PDFJ Neurosci
December 2024
Department of Psychological and Brain Sciences, Dartmouth College, NH, USA
Real-world choice options have many features or attributes, whereas the reward outcome from those options only depends on a few features or attributes. It has been shown that humans learn and combine feature-based with more complex conjunction-based learning to tackle challenges of learning in naturalistic reward environments. However, it remains unclear how different learning strategies interact to determine what features or conjunctions should be attended to and control choice behavior, and how subsequent attentional modulations influence future learning and choice.
View Article and Find Full Text PDFFront Neurosci
November 2024
Global R&D Center, China FAW Corporation Limited, Changchun, China.
Brain-computer interfaces (BCIs) establish a direct communication pathway between the brain and external devices and have been widely applied in upper limb rehabilitation for hemiplegic patients. However, significant individual variability in motor imagery electroencephalogram (MI-EEG) signals leads to poor generalization performance of MI-based BCI decoding methods to new patients. This paper proposes a Multi-scale Frequency domain Feature-based Dynamic graph Attention Network (MFF-DANet) for upper limb MI decoding in hemiplegic patients.
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