Five experiments examined what is learned based on the perceptual and semantic information of objects in visual statistical learning (VSL). In the familiarization phase, participants viewed a sequence of line drawings and detected repetitions of various objects. In a subsequent test phase, they watched 2 test sequences (statistically related triplets vs. unrelated foils) and decided whether the first or second sequence was more familiar based on the familiarization phase. In Experiment 1A, the test sequences comprised line drawings; in Experiment 1B, they comprised word stimuli representing each line drawing. The results showed that performance for statistically related triplets was greater than chance. In Experiments 2 and 3 containing the forward ABC and backward CBA triplets in the test, the results showed the importance of temporal order, especially in line drawings. In Experiment 4, in which the forward triplets were pitted against the backward triplets, we showed that temporal order is still important for the expression of VSL with word stimuli. Finally, in Experiment 5, we replicated the results of Experiments 2 and 3 even with the images of visual objects. These results suggest the parallel processes on the visual features and semantic information of objects in VSL.
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World J Gastroenterol
December 2024
School of Computer Science Technology, Changchun University, Changchun 130022, Jilin Province, China.
Background: Wireless capsule endoscopy (WCE) has become an important noninvasive and portable tool for diagnosing digestive tract diseases and has been propelled by advancements in medical imaging technology. However, the complexity of the digestive tract structure, and the diversity of lesion types, results in different sites and types of lesions distinctly appearing in the images, posing a challenge for the accurate identification of digestive tract diseases.
Aim: To propose a deep learning-based lesion detection model to automatically identify and accurately label digestive tract lesions, thereby improving the diagnostic efficiency of doctors, and creating significant clinical application value.
Cogn Neurodyn
December 2024
Centre for Theoretical Neuroscience, University of Waterloo, 200 University Ave., Waterloo, ON N2L 3G1 Canada.
Distributed vector representations are a key bridging point between connectionist and symbolic representations in cognition. It is unclear how uncertainty should be modelled in systems using such representations. In this paper we discuss how bundles of symbols in certain Vector Symbolic Architectures (VSAs) can be understood as defining an object that has a relationship to a probability distribution, and how statements in VSAs can be understood as being analogous to probabilistic statements.
View Article and Find Full Text PDFMar Environ Res
December 2024
School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China. Electronic address:
The oil spill is a significant source of marine pollution, causing severe harm to marine ecosystems. Detecting oil spills accurately using synthetic aperture radar (SAR) images is crucial for protecting the environment. However, oil spill targets in SAR images are small and resemble other objects "look-alike".
View Article and Find Full Text PDFPsychon Bull Rev
December 2024
Queen Margaret University, Edinburgh, Scotland, EH21 6UU, UK.
Learning and remembering what things are used for is a capacity that is central to successfully living in any human culture. The current paper investigates whether functional facts (information about what an object is used for) are remembered more efficiently compared with nonfunctional facts. Experiment 1 presented participants with images of functionally ambiguous objects associated with a (made-up) name and a (made-up) fact that could relate either to the object's function or to something nonfunctional.
View Article and Find Full Text PDFNat Comput Sci
December 2024
Department of Neural Dynamics and Magnetoencephalography, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.
Understanding how visual information is encoded in biological and artificial systems often requires the generation of appropriate stimuli to test specific hypotheses, but available methods for video generation are scarce. Here we introduce the spatiotemporal style transfer (STST) algorithm, a dynamic visual stimulus generation framework that allows the manipulation and synthesis of video stimuli for vision research. We show how stimuli can be generated that match the low-level spatiotemporal features of their natural counterparts, but lack their high-level semantic features, providing a useful tool to study object recognition.
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