23 papers · ranked by Valyu relevance
Alexis Buatois, Robert Gerlai
Spatial learning and memory have been studied for several decades. Analyses of these processes pose fundamental scientific questions but are also relevant from a biomedical perspective. The cellular, synaptic and molecular mechanisms underlying spatial learning have been intensively investigated, yet the behavioral…
Sabine U. König, Viviane Kakerbeck, Debora Nolte, Laura Duesberg + 2 more
On the basis of embodied/-enacted theories of the mind, investigations of spatial cognition related to real world environments have become current research interests. How this perspective relates to acquiring spatial knowledge not by active exploration, but through map learning, however, remains unresolved. Therefore…
Xiaohe Qiu, Lala Wen, Changxu Wu, Zhen Yang + 3 more
'Hongting Li' 'Duming Wang'] Research on the acquisition of spatial knowledge not only enriches our understanding of the theory of spatial knowledge representation but also creates practical value for the application of spatial knowledge. The aim of this study is to understand the impact of different learning methods…
Andrey Babichev, Sen Cheng, Yuri Dabaghian
1Department of Neurology Pediatrics, Jan and Dan Duncan Neurological Research Institute, Baylor College of Medicine, Houston, TX 77030 USA Department of Computational and Applied Mathematics, Rice University, Houston, TX 77005 USA, 2Mercator Research Group "Structure of Memory" and Department of Psychology…
Zsolt Gyozo Török, Ágoston Török
To study geo-visualization processes a Cognitive Cartography Lab was established at Eötvös University, and the “Virtual Tourist” experiment was designed for the better understanding of actual map use during navigation. In this paper we present some preliminary results of the experiment. We explored the use of a static…
Çetin Tüker, Togan Tong
This study aimed to compare and investigate the efficacy of the real-world experiences, immersive virtual reality (IVR) experiences, and video walkthrough representations on layout-learning in a complex building. A quasiexperimental, intervention, and delayed post-test research design was used among three groups…
Yuto Tachiki, Yusuke Suzuki, Mutsumi Kurahashi, Keisuke Oki + 6 more
Animals including humans are capable of representing different scale spaces from smaller to larger ones. However, most laboratory animals live their life in a narrow range of scale spaces like home-cages and experimental setups, making it hard to extrapolate the spatial representation and learning process in large…
Demetriou, Yiannos, Parikh, Manasvi + 6 more
—Background: Spatial reasoning has been identified as a critical skill for success in STEM. Unfortunately, underrepresented groups often have lower incoming spatial ability. Courses that improve spatial skills exist but are not widely used. Virtual reality (VR) has been suggested as a possible tool for teaching spatial…
Alex Hodgkiss, Katie A. Gilligan, Andrew K. Tolmie, Michael S. C. Thomas + 1 more
Large-scale longitudinal studies spanning the past 50 years provide convincing evidence that spatial ability in adolescence predicts later science, technology, engineering, and mathematics (STEM) achievement (Lubinski & Benbow, [bjep12211-bib-0028]; Wai, Lubinski, & Benbow, [bjep12211-bib-0049]). In addition to often…
Alban Laflaquière, J. Kevin Ο’Regan, Sylvain Argentieri, Bruno Gas + 1 more
'Alexander V. Terekhov'] The design of robotic systems is largely dictated by our purely human intuition about how we perceive the world. This intuition has been proven incorrect with regard to a number of critical issues, such as visual change blindness. In order to develop truly autonomous robots, we must step away…
Konstantinos Kostakos, Alexandra Pliakopanou, Vasileios Meimaridis, Ourania-Natalia (Oriana) Galanou + 14 more
'Ourania-Natalia (Oriana) Galanou' 'Aikaterini Argyro Anagnostou' 'Dimitra Sertidou' 'Panagiotis Katis' 'Periklis Anastasiou' 'Konstantinos Katsoulidis' 'Yannis Lykogiorgos' 'Dimitrios Mytilinaios' 'Andreas P. Katsenos' 'Yannis V. Simos' 'Stefanos Bellos' 'Spyridon Konitsiotis' 'Dimitrios Peschos' 'Konstantinos I.…
James A. Gornet, Matt Thomson
Humans construct internal cognitive maps of their environment directly from sensory inputs without access to a system of explicit coordinates or distance measurements. While machine learning algorithms like SLAM utilize specialized inference procedures to identify visual features and construct spatial maps from visual…
Yuri Dabaghian
In the mammalian brain, many neuronal ensembles are involved in representing spatial structure of the environment. In particular, there exist cells that encode the animal's location and cells that encode head direction. A number of studies have addressed properties of the spatial maps produced by these two populations…
Marcus Lewis
The hippocampal formation is thought to learn spatial maps of environments, and in many models this learning process consists of forming a sensory association for each location in the environment. This is inefficient, akin to learning a large lookup table for each environment. Spatial maps can be learned much more…
Usman Farooq, George Dragoi
Euclidean space is the fabric of the world we live in. Whether and how geometric experience shapes our spatial-temporal representations of the world remained unknown. We deprived rats of experience with crucial features of Euclidean geometry by rearing them inside translucent spheres, and compared activity of large…
Robert M. Mok, Bradley C. Love
One view is that conceptual knowledge is organized using the circuitry in the medial temporal lobe (MTL) that supports spatial processing and navigation. In contrast, we find that a domain-general learning algorithm explains key findings in both spatial and conceptual domains. When the clustering model is applied to…
Frank Dickmann, Julian Keil, Annika Korte, Dennis Edler + 3 more
'Denise O´Meara' 'Martin Bordewieck' 'Nikolai Axmacher'] When using navigation devices the "cognitive map" created in the user's mind is much more fragmented, incomplete and inaccurate, compared to the mental model of space created when reading a conventional printed map. As users become more dependent on digital…
Robert M. Mok, Bradley C. Love
One view is that conceptual knowledge is organized as a “cognitive map” in the brain, using the circuitry in the medial temporal lobe (MTL) that supports spatial navigation. In contrast, we find that a domain-general learning algorithm explains key findings in both spatial and conceptual domains. When the clustering…
Authors not listed
Predicting protein-ligand binding affinity from three-dimensional (3D) structural data is a central task in structure-based drug discovery, yet it remains challenging due to limited data availability, structural complexity, and the sparse nature of 3D molecular representations. In this study, we investigate the…
Hang Hu, Jyothsna Padmakumar Bindu, Julia Laskin
Mass spectrometry imaging (MSI) is widely used for the label-free molecular mapping of biological samples. The identification of co-localized molecules in MSI data is crucial to the understanding of biochemical pathways. However, complex MSI data are too large for manual annotation but too small for training deep…
Authors not listed
Real-world datasets in chemical engineering and bioengineering processes--such as those from catalytic reactors, multiphase flows, polymerization reactors, bioreactors, and clinical trials--can often be unlabelled or disorganized, rendering the training of existing supervised learning models ineffective at learning the…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Jeff Guo, Vendy Fialková, Juan Diego Arango, Christian Margreitter + 4 more
Reinforcement learning (RL) is a powerful paradigm that has gained popularity across multiple domains. However, applying RL may come at a cost of multiple interactions between the agent and the environment. This cost can be especially pronounced when the single feedback from the environment is slow or computationally…