Cathryn Johns · 2003
This paper presents the novel idea of using the cognitive mapping process to teach relationships between data items, called the spatial learning method. To investigate the feasibility of the method, a VE based on a data set was created. Three studies using this VE were run concurrently on a single set of 40 participants. The findings showed that while most participants formed a cognitive map of the VE, the learning of the underlying data set varied greatly between participants and that participants who attended the conventional lecture performed significantly better at the learning test than participants who were taught via the spatial learning method.
Cited by 10
Lars Schwabe A, Melly S. Oitzl B, Steffen Richter A et al. · 2013
available at www.sciencedirect.com journal homepage: www.elsevier.com/locate/psyneuen Modulation of spatial and stimulus—response learning strategies by exogenous cortisol in healthy young women
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Denny, Michael Sean, 1945- · Michigan State University Libraries · 2024
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Robert Lloyd · Annals of the Association of American Geographers · 1994
Abstract This paper considers spatial prototypes as cognitive structures that are encoded into memory through experiences with spatial information. A prototype represents a category of information in your memory and serves as an abstraction of the most typical or central member of that category. Results from an experimental study are compared with results from a simulation. The purpose of the experiment is to understand the factors that influence the formation of spatial prototypes in human memory. The purpose of the simulation is to evaluate the ability of neural network models to learn spatial prototypes. Human subjects learn the nature of four categories of maps by viewing individual maps that are instances of the categories. The prototypes for the categories are defined by the variation of the location, size, and orientation of an island on the maps. Subjects then evaluate all possible maps to determine how typical each map is of the category. An analysis of variance reveals that subjects are able to encode the expected prototypes. The analysis also indicates that the order of the maps in the learning sequences affects how clearly the prototypes are defined. Presenting maps with prototypical characteristics early in the sequence appears to provide the best learning experience. Auto-associator neural networks are provided the same information learned by the human subjects and are able to learn the prototypes better than the human subjects. Geographers may find neural networks useful for modeling many types of visual and behavioral processes where decisions are intuitive rather than logical. Key Words: cognitive structuresdecision makinglearningneural networksspatial prototypes
Cited by 21
Shannon I. Skov-Rackette, Sara J. Shettleworth · PsycEXTRA Dataset · 2004
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He, Yuhang · Oxford University Research Archive (ORA) (University of Oxford) · 2024
As humans, we extensively depend on multimodal signals to perceive, interact with, and analyze our surrounding 3D spatial environment, so as to accomplish various complex tasks. Amongst all our multimodal senses, sound and vision are the two most ubiquitous signals in real world scenarios. Equipping machines with such spatial audio-visual multimodal inferring and learning capability, or human-level audio-visual intelligence, is a vital yet challenging task. Although the widely accepted importance for spatial audio-visual intelligence, current research community’s focus has been heavily biased towards vision, with far less attention paid to spatial audio. Research in spatial audio-visual learning typically takes spatial audio as an auxiliary input to assist vision-centred tasks, ignoring the intricate and multifaceted interactions between audio and vision. These observations and limitations discussed above motivate my two main DPhil research focus: spatial audio learning and spatial audio-visual learning. While the first one serves as the preparatory exploration aims to fill up the gap between the well-explored vision learning and the underexplored spatial audio learning, the second one raises new challenges in modality issue and exhibits strong applicability in real-scenarios. In summary, my DPhil research is driven by the following three main questions: 1. In the modern deep neural network era, how can classic signal processing based sound waveform feature extraction methods benefit from the powerful expressiveness of deep neural network? 2. Is the widely-adopted process of spectrogram extraction which treats sound as ordinary 2D image an optimal representation? If not, can we design novel neural networks specifically tailored for sound? 3. How can we design robust audio-visual multimodal learning framework in embodied settings where sound and vision are weakly associated? Exploring these questions requires treating sound as being equally important as vision, yet fundamentally distinct. To accomplish these aims, <strong>First</strong>, we first systematically explore the fundamental knowledge behind classical acoustic and visual signal processing, and further study the modality difference between acoustic signals and visual signals. <strong>Second</strong>, based on the previous exploration, we further investigate how these fundamentals behind acoustic and visual signals can guide deep neural network design for sound raw waveform learning. <strong>Third</strong>, we show how sound can be efficiently and effectively processed together with vision to provide an initial attempt towards spatial audio-visual intelligence under sound-vision weak-correlation condition.
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Praveen K. Pilly (304122), Stephen Grossberg (304121) · Figshare · 2015
<p>(a) Environment occupancy map based on the trajectories traveled across the learning trials, and (b) ensemble rate map of all model hippocampal cells in the last trial. Color coding from blue (min.) to red (max.) is used in either panel.</p>
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YAN, LIXIANG · Figshare · 2023
Capturing socio-spatial aspects of learning is vital for classroom orchestration and collaborative learning in complex spaces. Traditional methods like surveys and observations have limitations. This thesis addresses gaps in socio-spatial learning analytics: 1) the need for theory-grounded methodologies, 2) ensuring ecological and statistical validity, and 3) considering learning design. We developed methodologies based on traces from two large-scale studies in authentic learning settings. We also created a framework for socio-spatial learning analytics, incorporating theory, learning design, and evidence-based methods. The findings impact future analytics, emphasising collaborative learning's socio-spatial facets, instructional design effects, and ecological studies. Ethical considerations are also discussed.
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Jianzhong Qi, Guanli Liu, Christian S. Jensen et al. · Proceedings of the VLDB Endowment · 2020
Machine learning, especially deep learning, is used increasingly to enable better solutions for data management tasks previously solved by other means, including database indexing. A recent study shows that a neural network can not only learn to predict the disk address of the data value associated with a one-dimensional search key but also outperform B-tree-based indexing, thus promises to speed up a broad range of database queries that rely on B-trees for efficient data access. We consider the problem of learning an index for two-dimensional spatial data. A direct application of a neural network is unattractive because there is no obvious ordering of spatial point data. Instead, we introduce a rank space based ordering technique to establish an ordering of point data and group the points into blocks for index learning. To enable scalability, we propose a recursive strategy that partitions a large point set and learns indices for each partition. Experiments on real and synthetic data sets with more than 100 million points show that our learned indices are highly effective and efficient. Query processing using our indices is more than an order of magnitude faster than the use of R-trees or a recently proposed learned index.
Cited by 102Open access
Amin Hashemi · OSF Preprints (OSF Preprints) · 2025
This is the repository for the article: "How Spatial Structures Shape Learning"
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Katherine A. Ruppert (8893049), Karthik S. Prabhakara (8893052), Naama E. Toledano-Furman (8893055) et al. · Figshare · 2020
<p>Animals treated at 3d have significantly shorter latencies compared to CCI on days 2–6. Animals treated at 14d have significantly shorter latencies compared to CCI on days 2, 4, 6. On days 3,4,5 3d treated animals are significantly shorter latency than those treated at 14d. Sham, n = 10, CCI + PBS, n = 13, CCI + HB-adMSCs 3d, n = 7, CCI + HB-adMSCs 14d, n = 3.</p>
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De Meester, Gilles · Figshare · 2025
Contains: Rscript_SpatialLearning_Fish_Final.RDescription: Step-by-step R code required to analyse the spatial learning data and generate the graphs
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James F. Malec, Robert J. Ivnik, Nancy S. Hinkeldey · Psychological Assessment · 1991
The Visual Spatial Learning Test (VSLT) is a visuospatial memory measure requiring little fine motor dexterity. A series of studies accessed the validity of the VSLT
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PsycEXTRA Dataset · 2015
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De Meester, Gilles · Figshare · 2025
Contains: Rscript_SpatialLearning_Fish_Final.RDescription: Step-by-step R code required to analyse the spatial learning data and generate the graphs
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Susan E. Embretson · PsycTESTS Dataset · 1994
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Alec M. Bodzin, Thomas Hammond, Qiong Fu et al. · PsycTESTS Dataset · 2020
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SpringerReference · 2012
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Perri K. Eason, Paul Switzer · International Journal of Comparative Psychology · 2006
Spatial learning is evident in dragonflies on a variety of spatial scales. Mature dragonflies must be able to locate a variety of features in the habitat that are critical to survival and reproduction, including sites for breeding, foraging, roosting, and thermoregulating. In many species, these sites do not coincide in space. Because individuals may repeatedly use particular sites for different activities, they must learn both the locations of these sites and routes among them. Further evidence of spatial memory in dragonflies is provided by their site specificity on a finer scale. Breeding males, for example, often are faithful not only to a particular area, but to a specific territory site within that area. Males appear to become faithful to a territory site through localization, a process during which they explore the site and develop a spatial map of the location of the territory and its resources. Males also respond to their interactions with other individuals, adjusting both their choice of territories and their space use within their territories to reflect those interactions. In eastern amberwing dragonflies (Perithemis tenera), males are not faithful to territories on which they have lost a fight with another male; in contrast, males are more likely to be faithful to territories on which they successfully mated than to territories on which they obtained no matings. Similarly, while on territories, male amberwings adjust their position in response to negative and positive interactions. They move away from the side of the territory from which neighbors most frequently intruded, and they move toward locations from which they pursued a female. Territorial amberwings thus modify their space use at both the territory and within-territory spatial scale in response to their social environment. Their responses are consistent with the hypothesis that they learn from their positive and negative experiences and adjust their future space use accordingly. Further study of spatial learning in dragonflies would greatly enhance studies of dragonflies’ behavior and ecology, and help us understand learning in general.
Cited by 8Open access
Matteo Zallio, Camelia Chivăran, Sonia Capece et al. · Strategic Design Research Journal · 2023
The design of the built environment strongly influences people’s needs, how they learn, behave, and build relationships. Education and learning are part of the nature of human beings, and allow them to develop skills, advance culture, and answer needs. In the current context, the incremental use of technological devices inside spaces has brought several positive outcomes, but also various challenges due to increased complexity. Limitations in considering the diversity of human needs confronted with architecture and technologies may raise stigmatization and exclusion. This article explores the links between the learning theories and the paradigms of user-centered design, providing theoretical affinities able to reduce the exclusion of people during interaction with spaces and objects. The process of the inclusive spatial learning experience aims to stimulate designers to deliver inclusive spaces that provide a progressive discovery of their characteristics and technologies, with the final goal to improve the experience for people with different abilities, age, gender, culture, and various roles within the specific architecture they use and live.
Cited by 1Open access