scholarly journals Distinct neural networks underlie encoding of categorical versus coordinate spatial relations during active navigation

2011 ◽  
Vol 11 (11) ◽  
pp. 922-922
Author(s):  
O. Baumann ◽  
E. Chan ◽  
J. B. Mattingley
Keyword(s):  
Neural Networks ◽  
Spatial Relations ◽  
Coordinate Spatial Relations

scholarly journals The Road More Travelled: The Differential Effects of Spatial Experience in Young and Elderly Participants

2021 ◽  
Vol 18 (2) ◽  
pp. 709
Author(s):  
Antonella Lopez ◽  
Alessandro Germani ◽  
Luigi Tinella ◽  
Alessandro Oronzo Caffò ◽  
Albert Postma ◽  
...  
Keyword(s):  
Mental Representations ◽  
Spatial Relations ◽  
The Elderly ◽  
The Road ◽  
Socio Demographic Factors ◽  
Effects Of Aging ◽  
Coordinate Spatial Relations ◽  
Categorical Spatial Relations ◽  
The Relationship

Our spatial mental representations allow us to give refined descriptions of the environment in terms of the relative locations and distances between objects and landmarks. In this study, we investigated the effects of familiarity with the everyday environment, in terms of frequency of exploration and mode of transportation, on categorical and coordinate spatial relations, on young and elderly participants, controlling for socio-demographic factors. Participants were tested with a general anamnesis, a neuropsychological assessment, measures of explorations and the Landmark Positioning on a Map task. The results showed: (a) a modest difference in performance with categorical spatial relations; (b) a larger difference in coordinate spatial relations; (c) a significant moderating effect of age on the relationship between familiarity and spatial relations, with a stronger relation among the elderly than the young. Ceteris paribus, the role of direct experience with exploring their hometown on spatial mental representations appeared to be more important in the elderly than in the young. This advantage appears to make the elderly wiser and likely protects them from the detrimental effects of aging on spatial mental representations.

scholarly journals Examining Infant Relation Categorization Through Deep Neural Networks

2021 ◽  
Author(s):  
Guy Davidson ◽  
Brenden M. Lake
Keyword(s):  
Neural Networks ◽  
Spatial Cognition ◽  
Deep Neural Networks ◽  
Relational Learning ◽  
Spatial Relations ◽  
Computational Experiments ◽  
Visual Understanding

Categorizing spatial relations is central to the development of visual understanding and spatial cognition, with roots in the first few months of life. Quinn (2003) reviews two findings in infant relation categorization: categorizing one object as above/below another precedes categorizing an object as between other objects, and categorizing relations over specific objects predates abstract relations over varying objects. We model these phenomena with deep neural networks, including contemporary architectures specialized for relational learning and vision models pretrained on baby headcam footage (Sullivan et al., 2020). Across two computational experiments, we can account for most of the developmental findings, suggesting these models are useful for studying the computational mechanisms of infant categorization.

scholarly journals Geographical Imputation of Missing Poaceae Pollen Data via Convolutional Neural Networks

Atmosphere ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 717
Author(s):  
Ricardo Navares ◽  
José Luis Aznarte
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Airborne Pollen ◽  
Missing At Random ◽  
Spatial Relations ◽  
Computational Time ◽  
Pollen Monitoring ◽  
Distance Weighting ◽  
Poaceae Pollen ◽  
Data Points

Airborne pollen monitoring datasets sometimes exhibit gaps, even very long, either because of maintenance or because of a lack of expert personnel. Despite the numerous imputation techniques available, not all of them effectively include the spatial relations of the data since the assumption of missing-at-random is made. However, there are several techniques in geostatistics that overcome this limitation such as the inverse distance weighting and Gaussian processes or kriging. In this paper, a new method is proposed that utilizes convolutional neural networks. This method not only shows a competitive advantage in terms of accuracy when compared to the aforementioned techniques by improving the error by 5% on average, but also reduces execution training times by 90% when compared to a Gaussian process. To show the advantages of the proposal, 10%, 20%, and 30% of the data points are removed in the time series of a Poaceae pollen observation station in the region of Madrid, and the airborne concentrations from the remaining available stations in the network are used to impute the data removed. Even though the improvements in terms of accuracy are not significantly large, even if consistent, the gain in computational time and the flexibility of the proposed convolutional neural network allow field experts to adapt and extend the solution, for instance including meteorological variables, with the potential decrease of the errors reported in this paper.

scholarly journals Focusing Narrowly or Broadly Attention When Judging Categorical and Coordinate Spatial Relations: A MEG Study

PLoS ONE ◽  
2013 ◽  
Vol 8 (12) ◽  
pp. e83434 ◽  
Author(s):  
Raffaella Franciotti ◽  
Stefania D’Ascenzo ◽  
Alberto Di Domenico ◽  
Marco Onofrj ◽  
Luca Tommasi ◽  
...  
Keyword(s):  
Spatial Relations ◽  
Coordinate Spatial Relations

Categorical and coordinate spatial relations in working memory: An fMRI study

2009 ◽  
Vol 1297 ◽  
pp. 70-79 ◽  
Author(s):  
Ineke J.M. van der Ham ◽  
Mathijs Raemaekers ◽  
Richard J.A. van Wezel ◽  
Anna Oleksiak ◽  
Albert Postma
Keyword(s):  
Working Memory ◽  
Spatial Relations ◽  
Fmri Study ◽  
Coordinate Spatial Relations

Hemispheric Processing of Categorical and Coordinate Spatial Relations in the Absence of Low Spatial Frequencies

2002 ◽  
Vol 14 (2) ◽  
pp. 291-297 ◽  
Author(s):  
Matia Okubo ◽  
Chikashi Michimata
Keyword(s):  
Visual Field ◽  
Spatial Frequency ◽  
Right Hemisphere ◽  
Spatial Relation ◽  
Spatial Relations ◽  
Right Visual Field ◽  
Spatial Frequencies ◽  
Categorical Task ◽  
The Right ◽  
Coordinate Spatial Relations

Right-handed participants performed the categorical and coordinate spatial relation judgments on stimuli presented to either the left visual field—right hemisphere (LVF-RH) or the right visual field—left hemisphere (RVF-LH). The stimulus patterns were formulated either by bright dots or by contrast-balanced dots. When the stimuli were bright, an RVF-LH advantage was observed for the categorical task, whereas an LVF-RH advantage was observed for the coordinate task. When the stimuli were contrast balanced, the RVF-LH advantage was observed for the categorical task, but the LVF-RH advantage was eliminated for the coordinate task. Because the contrast-balanced dots are largely devoid of low spatial frequency content, these results suggest that processing of low spatial frequency is responsible for the right hemisphere advantage for the coordinate spatial processing.

Précis of Neural organization: Structure, function, and dynamics

2000 ◽  
Vol 23 (4) ◽  
pp. 513-533 ◽  
Author(s):  
Michael A. Arbib ◽  
Péter Érdi
Keyword(s):  
Neural Networks ◽  
Structure Function ◽  
Organization Structure ◽  
Spatial Relations ◽  
Brain Regions ◽  
Neural Data ◽  
Cellular Processes ◽  
Neural Organization ◽  
Dynamic System Theory

Neural organization: Structure, function, and dynamics shows how theory and experiment can supplement each other in an integrated, evolving account of the brain's structure, function, and dynamics. (1) Structure: Studies of brain function and dynamics build on and contribute to an understanding of many brain regions, the neural circuits that constitute them, and their spatial relations. We emphasize Szentágothai's modular architectonics principle, but also stress the importance of the microcomplexes of cerebellar circuitry and the lamellae of hippocampus. (2) Function: Control of eye movements, reaching and grasping, cognitive maps, and the roles of vision receive a functional decomposition in terms of schemas. Hypotheses as to how each schema is implemented through the interaction of specific brain regions provide the basis for modeling the overall function by neural networks constrained by neural data. Synthetic PET integrates modeling of primate circuitry with data from human brain imaging. (3) Dynamics: Dynamic system theory analyzes spatiotemporal neural phenomena, such as oscillatory and chaotic activity in both single neurons and (often synchronized) neural networks, the self-organizing development and plasticity of ordered neural structures, and learning and memory phenomena associated with synaptic modification. Rhythm generation involves multiple levels of analysis, from intrinsic cellular processes to loops involving multiple brain regions. A variety of rhythms are related to memory functions. The Précis presents a multifaceted case study of the hippocampus. We conclude with the claim that language and other cognitive processes can be fruitfully studied within the framework of neural organization that the authors have charted with John Szentágothai.

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