A total of 41 participants explored a novel square-shaped environment containing five identical boxes each hiding a visually distinct object. After an initial free exploration the participants were required to locate the objects first in a predetermined and subsequently in an optional order task. Two distinct exploration strategies emerged: Participants explored either along the main axes of the room (axial), or in a more spatially spread, circular pattern around the edges of the room (circular). These initial exploration strategies influenced the optimality of spatial navigation performance in the subsequent optional order task. The results reflect a trade-off between memory demands and distance efficiency. The more sequential axial strategy resulted in fewer demands on spatial memory but required more distance to be travelled. The circular strategy was more demanding on memory but required less subsequent travelling distance. The findings are discussed in terms of spatial knowledge acquisition and optimality of strategy representations.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1080/17470210701536310 | DOI Listing |
Neural Netw
January 2025
School of Computer Science and Technology, East China Normal University, 200062, Shanghai, China.
Real-world image super-resolution (RISR) has received increased focus for improving the quality of SR images under unknown complex degradation. Existing methods rely on the heavy SR models to enhance low-resolution (LR) images of different degradation levels, which significantly restricts their practical deployments on resource-limited devices. In this paper, we propose a novel Dynamic Channel Splitting scheme for efficient Real-world Image Super-Resolution, termed DCS-RISR.
View Article and Find Full Text PDFLearn Behav
January 2025
Normandie UnivUnicaen, CNRS, EthoS, 14000, Caen, France.
Episodic memory and future thinking are generally considered as two parts of the same mental time travelling system in vertebrates. Modern cephalopods, with their independent evolutionary lineage and their complex cognitive abilities, appear as promising species to determine whether these abilities have separate evolutionary histories or not. In our study, we tested future-planning abilities in a cephalopod species which has been shown to possess episodic-like memory abilities: the common cuttlefish.
View Article and Find Full Text PDFPLoS One
January 2025
NCCA, Bournemouth University, Poole, United Kingdom.
Biomimetics (Basel)
December 2024
IDLab-AIRO, Faculty of Engineering and Architecture, Ghent University, 9052 Ghent, Belgium.
The performance of echo state networks (ESNs) in temporal pattern learning tasks depends both on their memory capacity (MC) and their non-linear processing. It has been shown that linear memory capacity is maximized when ESN neurons have linear activation, and that a trade-off between non-linearity and linear memory capacity is required for temporal pattern learning tasks. The more recent distance-based delay networks (DDNs) have shown improved memory capacity over ESNs in several benchmark temporal pattern learning tasks.
View Article and Find Full Text PDFMath Biosci Eng
October 2024
State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, Guangdong, China.
Super-resolution (SR) of magnetic resonance imaging (MRI) is gaining increasing attention for being able to provide detailed anatomical information. However, current SR methods often use the complex convolutional network for feature extraction, which is difficult to train and not suitable for limited computation resources in the medical scenario. To tackle these bottlenecks, we propose a multi-distillation residual network (MDRN) for more differential feature refinement, which has a superior trade-off between reconstruction accuracy and computation cost.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!