Theoretical studies suggest that the visual system uses prior knowledge of visual objects to recognize them in visual clutter, and posit that the strategies for recognizing objects in clutter may differ depending on whether or not the object was learned in clutter to begin with. We tested this hypothesis using functional magnetic resonance imaging (fMRI) of human subjects. We trained subjects to recognize naturalistic, yet novel objects in strong or weak clutter. We then tested subjects' recognition performance for both sets of objects in strong clutter. We found many brain regions that were differentially responsive to objects during object recognition depending on whether they were learned in strong or weak clutter. In particular, the responses of the left fusiform gyrus (FG) reliably reflected, on a trial-to-trial basis, subjects' object recognition performance for objects learned in the presence of strong clutter. These results indicate that the visual system does not use a single, general-purpose mechanism to cope with clutter. Instead, there are two distinct spatial patterns of activation whose responses are attributable not to the visual context in which the objects were seen, but to the context in which the objects were learned.
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http://dx.doi.org/10.3389/fnhum.2012.00170 | DOI Listing |
Sci Rep
January 2025
Laboratory of Chemical Biology, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, 130022, Jilin, China.
In order to address the issue of tracking errors of collision Caenorhabditis elegans, this research proposes an improved particle filter tracking method integrated with cultural algorithm. The particle filter algorithm is enhanced through the integration of the sine cosine algorithm, thereby facilitating uninterrupted tracking of the target C. elegans.
View Article and Find Full Text PDFBrain Res
January 2025
epartment of Basic Medicine, School of Medicine, Hangzhou City University, Hangzhou, Zhejiang 310015, China; Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou, Zhejiang 310015, China. Electronic address:
Whisker deprivation at different stages of early development results in varied behavioral outcomes. However, there is a notable lack of systematic studies evaluating the specific effects of whisker deprivation from postnatal day 0 (P0) to P14 on adolescent behavioral performance in mice. To investigate these effects, C57BL/6J mice underwent whisker deprivation from P0 to P14 and were subsequently assessed at 5 weeks of age using a battery of tests: motor skills were evaluated using open field test; emotional behavior was evaluated using a series of anxiety- and depression-related behavioral tests; cognitive function was examined via novel location and object recognition tests; and social interactions were analyzed using three-chamber social interaction test.
View Article and Find Full Text PDFWaste Manag
January 2025
Chair of Waste Processing Technology and Waste Management, Montanuniversitaet Leoben, Leoben, Austria. Electronic address:
Global waste generation is projected to reach 3.40 billion tons by 2050, necessitating improved waste sorting for effective recycling and progress toward a circular economy. Achieving this transformation requires higher sorting intensity through intensified processes, increased efficiency, and enhanced yield.
View Article and Find Full Text PDFComput Biol Med
January 2025
School of Computer Science, Chungbuk National University, Cheongju 28644, Republic of Korea. Electronic address:
The fusion index is a critical metric for quantitatively assessing the transformation of in vitro muscle cells into myotubes in the biological and medical fields. Traditional methods for calculating this index manually involve the labor-intensive counting of numerous muscle cell nuclei in images, which necessitates determining whether each nucleus is located inside or outside the myotubes, leading to significant inter-observer variation. To address these challenges, this study proposes a three-stage process that integrates the strengths of pattern recognition and deep-learning to automatically calculate the fusion index.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China.
With the rapid development of AI algorithms and computational power, object recognition based on deep learning frameworks has become a major research direction in computer vision. UAVs equipped with object detection systems are increasingly used in fields like smart transportation, disaster warning, and emergency rescue. However, due to factors such as the environment, lighting, altitude, and angle, UAV images face challenges like small object sizes, high object density, and significant background interference, making object detection tasks difficult.
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