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Articoli di riviste sul tema "Representation spatial"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 5 febbraio 2022

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1

SRIHARI, SARGUR N., e ZHIGANG XIANG. "SPATIAL KNOWLEDGE REPRESENTATION". International Journal of Pattern Recognition and Artificial Intelligence 03, n. 01 (marzo 1989): 67–84. http://dx.doi.org/10.1142/s0218001489000073.

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The use of spatial knowledge is necessary in a variety of artificial intelligence and expert systems applications. The need is not only in tasks with spatial goals such as image interpretation and robot motion, but also in tasks not involving spatial goals, e.g. diagnosis and language understanding. The paper discusses methods of representing spatial knowledge, with particular focus on the broad categories known as analogical and propositional representations. The problem of neurological localization is considered in some detail as an example of intelligent problem-solving that requires the use of spatial knowledge. Several solutions for the problem are presented: the first uses an analogical representation only, the second uses a propositional representation and the third uses an integrated representation. Conclusions about the different representations for building intelligent systems are drawn.

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2

McNamara, Timothy P. "Spatial representation". Geoforum 23, n. 2 (maggio 1992): 139–50. http://dx.doi.org/10.1016/0016-7185(92)90012-s.

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3

Fiantika, F. R., e S. P. Setyawati. "Representation, representational transformation and spatial reasoning hierarchical in spatial thinking". Journal of Physics: Conference Series 1321 (ottobre 2019): 022056. http://dx.doi.org/10.1088/1742-6596/1321/2/022056.

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4

Masser, Ian, e Peter J. B. Brown. "SPATIAL REPRESENTATION AND SPATIAL INTERACTION". Papers in Regional Science 38, n. 1 (14 gennaio 2005): 71–92. http://dx.doi.org/10.1111/j.1435-5597.1977.tb00992.x.

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5

Hayward, William G., e Michael J. Tarr. "Spatial language and spatial representation". Cognition 55, n. 1 (aprile 1995): 39–84. http://dx.doi.org/10.1016/0010-0277(94)00643-y.

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6

Jacobson, Lowell D., e Harry Wechsler. "Joint spatial/spatial-frequency representation". Signal Processing 14, n. 1 (gennaio 1988): 37–68. http://dx.doi.org/10.1016/0165-1684(88)90043-6.

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7

De Hevia, Maria-Dolores, Luisa Girelli, Emanuela Bricolo e Giuseppe Vallar. "The representational space of numerical magnitude: Illusions of length". Quarterly Journal of Experimental Psychology 61, n. 10 (ottobre 2008): 1496–514. http://dx.doi.org/10.1080/17470210701560674.

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In recent years, a growing amount of evidence concerning the relationships between numerical and spatial representations has been interpreted, by and large, in favour of the mental number line hypothesis—namely, the analogue continuum where numbers are spatially represented (Dehaene, 1992; Dehaene, Piazza, Pinel, & Cohen, 2003). This numerical representation is considered the core of number meaning and, accordingly, needs to be accessed whenever numbers are semantically processed. The present study explored, by means of a length reproduction task, whether besides the activation of lateralized spatial codes, numerical processing modulates the mental representation of a horizontal spatial extension. Mis-estimations of length induced by Arabic numbers are interpreted in terms of a cognitive illusion, according to which the elaboration of magnitude information brings about an expansion or compression of the mental representation of spatial extension. These results support the hypothesis that visuo-spatial resources are involved in the representation of numerical magnitude.

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MASUYAMA, Eitaro. "Spatial representation of colors." Japanese journal of ergonomics 24, n. 2 (1988): 93–100. http://dx.doi.org/10.5100/jje.24.93.

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9

Boubezari, Mohammed, e Jos‐Luis Bento Coelho. "Spatial representation of soundscape". Journal of the Acoustical Society of America 115, n. 5 (maggio 2004): 2453. http://dx.doi.org/10.1121/1.4782264.

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10

von Hecker, Ulrich, Ulrike Hahn e Jasmine Rollings. "Spatial representation of coherence." Journal of Experimental Psychology: General 145, n. 7 (luglio 2016): 853–71. http://dx.doi.org/10.1037/xge0000176.

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11

O’Mara, Shane M. "Spatial representation, spatial navigation and the hippocampus". Irish Journal of Psychology 14, n. 3 (gennaio 1993): 343–60. http://dx.doi.org/10.1080/03033910.1993.10557939.

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12

Proboretno, Setyaning, e Pradnyo Wijayanti. "Representasi Matematis Siswa SMP dalam Meyelesaikan Masalah Segiempat Ditinjau dari Perbedaan Jenis Kelamin". MATHEdunesa 8, n. 3 (12 agosto 2019): 472–76. http://dx.doi.org/10.26740/mathedunesa.v8n3.p472-476.

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Mathematical representation has an important role to help students understand and solve quadrilateral problems in mathematics learning. Students will use different forms of mathematical representation to solve a quadrilateral problem. This allows that the form of mathematical representation used by male and female students is different. The purpose of this study was to describe the mathematical representation of male and female junior high school students in solving quadrilateral problems. This research is classified into descriptive qualitative research using test and interview methods. The results of this study indicate that male students use visual-spatial representations in the form of images to represent an object that is in the problem solving test. In addition, they use visual-spatial representations and formal-notational representations to reveal information about a problem. During the problem solving process, dominant male students use formal-notational representation. They also explained verbally each step of the completion in detail and in order. Dominant female students use formal-notational representation to write information and solve a problem. To represent an object in a problem solving test, they use visual-spatial representations. Female students also use verbal representations to explain each step of solving problems.Keywords: mathematical representation, quadrilateral problems, gender

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13

Brown, Paul B., Ronald Millecchia, Jeffrey J. Lawson, Stephanie Stephens, Paul Harton e James C. Culberson. "Dorsal Horn Spatial Representation of Simple Cutaneous Stimuli". Journal of Neurophysiology 79, n. 2 (1 febbraio 1998): 983–98. http://dx.doi.org/10.1152/jn.1998.79.2.983.

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Brown, Paul B., Ronald Millecchia, Jeffrey J. Lawson, Stephanie Stephens, Paul Harton, and James C. Culberson. Dorsal horn spatial representation of simple cutaneous stimuli. J. Neurophysiol. 79: 983–998, 1998. A model of lamina III–IV dorsal horn cell receptive fields (RFs) has been developed to visualize the spatial patterns of cells activated by light touch stimuli. Low-threshold mechanoreceptive fields (RFs) of 551 dorsal horn neurons recorded in anesthetized cats were characterized by location of RF center in cylindrical coordinates, area, length/width ratio, and orientation of long axis. Best-fitting ellipses overlapped actual RFs by 90%. Exponentially smoothed mean and variance surfaces were estimated for these five variables, on a grid of 40 points mediolaterally by 20/segment rostrocaudally in dorsal horn segments L4–S1. The variations of model RF location, area, and length/width ratio with map location were all similar to previous observations. When elliptical RFs were simulated at the locations of the original cells, the RFs of real and simulated cells overlapped by 64%. The densities of cell representations of skin points on the hindlimb were represented as pseudocolor contour plots on dorsal view maps, and segmental representations were plotted on the standard views of the leg. Overlap of modeled and real segmental representations was at the 84% level. Simulated and observed RFs had similar relations between area and length/width ratio and location on the hindlimb: r( A) = 0.52; r( L/ W) = 0.56. Although the representation of simple stimuli was orderly, and there was clearly only one somatotopic map of the skin, the representation of a single point often was not a single cluster of active neurons. When two-point stimuli were simulated, there usually was no fractionation of response zones or addition of new zones. Variation of stimulus size (area of skin contacted) produced less variation of representation size (number of cells responding) than movement of stimuli from one location to another. We conclude that stimulus features are preserved poorly in their dorsal horn spatial representation and that discrimination mechanisms that depend on detection of such features in the spatial representation would be unreliable.

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Iddianozie, Chidubem, e Gavin McArdle. "Towards Robust Representations of Spatial Networks Using Graph Neural Networks". Applied Sciences 11, n. 15 (27 luglio 2021): 6918. http://dx.doi.org/10.3390/app11156918.

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The effectiveness of a machine learning model is impacted by the data representation used. Consequently, it is crucial to investigate robust representations for efficient machine learning methods. In this paper, we explore the link between data representations and model performance for inference tasks on spatial networks. We argue that representations which explicitly encode the relations between spatial entities would improve model performance. Specifically, we consider homogeneous and heterogeneous representations of spatial networks. We recognise that the expressive nature of the heterogeneous representation may benefit spatial networks and could improve model performance on certain tasks. Thus, we carry out an empirical study using Graph Neural Network models for two inference tasks on spatial networks. Our results demonstrate that heterogeneous representations improves model performance for down-stream inference tasks on spatial networks.

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15

Tatler, Benjamin W., e Michael F. Land. "Vision and the representation of the surroundings in spatial memory". Philosophical Transactions of the Royal Society B: Biological Sciences 366, n. 1564 (27 febbraio 2011): 596–610. http://dx.doi.org/10.1098/rstb.2010.0188.

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One of the paradoxes of vision is that the world as it appears to us and the image on the retina at any moment are not much like each other. The visual world seems to be extensive and continuous across time. However, the manner in which we sample the visual environment is neither extensive nor continuous. How does the brain reconcile these differences? Here, we consider existing evidence from both static and dynamic viewing paradigms together with the logical requirements of any representational scheme that would be able to support active behaviour. While static scene viewing paradigms favour extensive, but perhaps abstracted, memory representations, dynamic settings suggest sparser and task-selective representation. We suggest that in dynamic settings where movement within extended environments is required to complete a task, the combination of visual input, egocentric and allocentric representations work together to allow efficient behaviour. The egocentric model serves as a coding scheme in which actions can be planned, but also offers a potential means of providing the perceptual stability that we experience.

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Humayun, Mohammad Ali, Hayati Yassin e Pg Emeroylariffion Abas. "Spatial position constraint for unsupervised learning of speech representations". PeerJ Computer Science 7 (21 luglio 2021): e650. http://dx.doi.org/10.7717/peerj-cs.650.

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The success of supervised learning techniques for automatic speech processing does not always extend to problems with limited annotated speech. Unsupervised representation learning aims at utilizing unlabelled data to learn a transformation that makes speech easily distinguishable for classification tasks, whereby deep auto-encoder variants have been most successful in finding such representations. This paper proposes a novel mechanism to incorporate geometric position of speech samples within the global structure of an unlabelled feature set. Regression to the geometric position is also added as an additional constraint for the representation learning auto-encoder. The representation learnt by the proposed model has been evaluated over a supervised classification task for limited vocabulary keyword spotting, with the proposed representation outperforming the commonly used cepstral features by about 9% in terms of classification accuracy, despite using a limited amount of labels during supervision. Furthermore, a small keyword dataset has been collected for Kadazan, an indigenous, low-resourced Southeast Asian language. Analysis for the Kadazan dataset also confirms the superiority of the proposed representation for limited annotation. The results are significant as they confirm that the proposed method can learn unsupervised speech representations effectively for classification tasks with scarce labelled data.

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Gao, Zhanning, Le Wang, Qilin Zhang, Zhenxing Niu, Nanning Zheng e Gang Hua. "Video Imprint Segmentation for Temporal Action Detection in Untrimmed Videos". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 luglio 2019): 8328–35. http://dx.doi.org/10.1609/aaai.v33i01.33018328.

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We propose a temporal action detection by spatial segmentation framework, which simultaneously categorize actions and temporally localize action instances in untrimmed videos. The core idea is the conversion of temporal detection task into a spatial semantic segmentation task. Firstly, the video imprint representation is employed to capture the spatial/temporal interdependences within/among frames and represent them as spatial proximity in a feature space. Subsequently, the obtained imprint representation is spatially segmented by a fully convolutional network. With such segmentation labels projected back to the video space, both temporal action boundary localization and per-frame spatial annotation can be obtained simultaneously. The proposed framework is robust to variable lengths of untrimmed videos, due to the underlying fixed-size imprint representations. The efficacy of the framework is validated in two public action detection datasets.

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18

Lim Jihyun e Seo Jungmin. "Yanbian as a Spatial Representation". Culture and Politics 5, n. 2 (giugno 2018): 31–60. http://dx.doi.org/10.22539/culpol.2018.5.2.31.

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19

Wang, Zhiyao, e Hyung Gi Kim. "Spatial representation using repeated images". TECHART: Journal of Arts and Imaging Science 5, n. 1 (28 febbraio 2018): 15–16. http://dx.doi.org/10.15323/techart.2018.2.5.1.15.

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Pontius, Anneliese A. "Spatial Representation, Modified by Ecology". Journal of Cross-Cultural Psychology 24, n. 4 (dicembre 1993): 399–413. http://dx.doi.org/10.1177/0022022193244002.

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21

Fiantika, F. R. "Representation Elements of Spatial Thinking". Journal of Physics: Conference Series 824 (18 aprile 2017): 012056. http://dx.doi.org/10.1088/1742-6596/824/1/012056.

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22

Liverence, Brandon M., e Brian J. Scholl. "Selective Attention Warps Spatial Representation". Psychological Science 22, n. 12 (17 novembre 2011): 1600–1608. http://dx.doi.org/10.1177/0956797611422543.

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Selective attention not only influences which objects in a display are perceived, but also directly changes the character of how they are perceived—for example, making attended objects appear larger or sharper. In studies of multiple-object tracking and probe detection, we explored the influence of sustained selective attention on where objects are seen to be in relation to each other in dynamic multi-object displays. Surprisingly, we found that sustained attention can warp the representation of space in a way that is object-specific: In immediate recall of the positions of objects that have just disappeared, space between targets is compressed, whereas space between distractors is expanded. These effects suggest that sustained attention can warp spatial representation in unexpected ways.

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23

Wilkie, Donald M., e Robert J. Wilison. "Comparative cognition of spatial representation". Behavioral and Brain Sciences 12, n. 1 (marzo 1989): 97–98. http://dx.doi.org/10.1017/s0140525x00024547.

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24

Fumarola, Antonia, Valter Prpic, Deanna Fornasier, Flavio Sartoretto, Tiziano Agostini e Carlo Umiltà. "The Spatial Representation of Angles". Perception 45, n. 11 (19 agosto 2016): 1320–30. http://dx.doi.org/10.1177/0301006616661915.

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25

Park, K. M., e S. C. Chong. "Representation of mean spatial frequency". Journal of Vision 8, n. 6 (8 aprile 2010): 944. http://dx.doi.org/10.1167/8.6.944.

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26

Hecht, Heiko, Mary K. Kaiser, Naomi Eilan, Rosaleen McCarthy e Bill Brewer. "Spatial Representation: Posers and Paradigms". American Journal of Psychology 108, n. 2 (1995): 283. http://dx.doi.org/10.2307/1423133.

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27

Dessalegn, Banchiamlack, e Barbara Landau. "Forming a stable spatial representation". Cognitive Processing 7, S1 (9 agosto 2006): 27. http://dx.doi.org/10.1007/s10339-006-0052-z.

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Tolja, Jader, e Clara Cardia. "Body organisation and spatial representation". Cognitive Processing 7, S1 (9 agosto 2006): 95. http://dx.doi.org/10.1007/s10339-006-0084-4.

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29

Miller, Harvey J. "Geographic representation in spatial analysis". Journal of Geographical Systems 2, n. 1 (9 marzo 2000): 55–60. http://dx.doi.org/10.1007/s101090050030.

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30

Galton, Antony. "Spatial and temporal knowledge representation". Earth Science Informatics 2, n. 3 (12 maggio 2009): 169–87. http://dx.doi.org/10.1007/s12145-009-0027-6.

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31

Franklin, Nancy. "Spatial representation for described environments". Geoforum 23, n. 2 (maggio 1992): 165–74. http://dx.doi.org/10.1016/0016-7185(92)90014-u.

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32

Isham, V., D. R. Cox, I. Rodríguez-Iturbe, A. Porporato e S. Manfreda. "Representation of space–time variability of soil moisture". Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 461, n. 2064 (10 ottobre 2005): 4035–55. http://dx.doi.org/10.1098/rspa.2005.1568.

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A simplified spatial-temporal soil moisture model driven by stochastic spatial rainfall forcing is proposed. The model is mathematically tractable, and allows the spatial and temporal structure of soil moisture fields, induced by the spatial-temporal variability of rainfall and the spatial variability of vegetation, to be explored analytically. The influence of the main model parameters, reflecting the spatial scale of rain cells, the soil storage capacity, the rainfall interception and the soil water loss rate (representing evaporation and deep infiltration) is investigated. The variabilities of the spatially averaged soil moisture process, and that averaged in both space and time, are derived. The present analysis focuses on spatially uniform vegetation conditions; a follow-up paper will incorporate stochastically heterogeneous vegetation.

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Long, Xiaoyang, e Sheng-Jia Zhang. "A novel somatosensory spatial navigation system outside the hippocampal formation". Cell Research 31, n. 6 (18 gennaio 2021): 649–63. http://dx.doi.org/10.1038/s41422-020-00448-8.

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AbstractSpatially selective firing of place cells, grid cells, boundary vector/border cells and head direction cells constitutes the basic building blocks of a canonical spatial navigation system centered on the hippocampal-entorhinal complex. While head direction cells can be found throughout the brain, spatial tuning outside the hippocampal formation is often non-specific or conjunctive to other representations such as a reward. Although the precise mechanism of spatially selective firing activity is not understood, various studies show sensory inputs, particularly vision, heavily modulate spatial representation in the hippocampal-entorhinal circuit. To better understand the contribution of other sensory inputs in shaping spatial representation in the brain, we performed recording from the primary somatosensory cortex in foraging rats. To our surprise, we were able to detect the full complement of spatially selective firing patterns similar to that reported in the hippocampal-entorhinal network, namely, place cells, head direction cells, boundary vector/border cells, grid cells and conjunctive cells, in the somatosensory cortex. These newly identified somatosensory spatial cells form a spatial map outside the hippocampal formation and support the hypothesis that location information modulates body representation in the somatosensory cortex. Our findings provide transformative insights into our understanding of how spatial information is processed and integrated in the brain, as well as functional operations of the somatosensory cortex in the context of rehabilitation with brain-machine interfaces.

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Kuang, Shi Rong. "Knowledge Representation of Art Patterns Based on the Calculation Mental Image". Advanced Materials Research 989-994 (luglio 2014): 1493–96. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.1493.

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Computational imaginary is a simulation of the human mental image based on the study of cognitive science. Art pattern composition knowledge representation is the basis of the intelligence of computer-aided art pattern design. The paper describes an art pattern composition knowledge representation scheme based on the model of computational imaginary. The scheme includes the deep representation, visual representation and spatial representation, and the operations of these three representations. It further describes the abstract and image information from the perspective of the relation between art pattern layout visual and spatial shape.

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Watkins, Ceri. "Representations of Space, Spatial Practices and Spaces of Representation: An Application of Lefebvre’s Spatial Triad". Culture and Organization 11, n. 3 (settembre 2005): 209–20. http://dx.doi.org/10.1080/14759550500203318.

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36

Young-Sil Kim, 고은실 e 최진숙. "Spatial representation ability of 1 and 2-year-old infants according to spatial representation types and spatial relationships". Korean Journal of Early Childhood Education 38, n. 4 (agosto 2018): 399–413. http://dx.doi.org/10.18023/kjece.2018.38.4.016.

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37

Bonds, Mark Evan. "The Spatial Representation of Musical Form". Journal of Musicology 27, n. 3 (2010): 265–303. http://dx.doi.org/10.1525/jm.2010.27.3.265.

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Musical form can be conceptualized in two basic ways, temporally or spatially. The temporal approach conceives of form diachronically, as a series of events that unfold through time, whereas the spatial approach conceives of form synchronically, as a synoptic design in which the relationship of the individual parts to the whole is apparent at once. These two modes are interdependent and by no means mutually exclusively. Indeed, virtually every account of musical form——either in the abstract or as applied to a specific work——draws on both concepts to varying degrees. Narrative accounts that relate a series of events often rely on spatial imagery, and the formal diagrams that are a standard feature of analytical discourse nowadays almost invariably reflect the progression of music through time. Not until 1825, however, did any critic or theorist attempt to represent musical form in an essentially spatial, synoptic manner. Antoine Reicha's diagrams of binary, ternary, and rondo forms in his Traitéé de haute composition musicale, moreover, found little resonance among his contemporaries. Even the simplest formal diagrams would remain a rarity for another seventy-five years and would not become a standard element of theoretical accounts of form until the early twentieth century. Spatial representations of form were slow to emerge and gain acceptance, at least in part because of a broader reluctance to accept the premise of depicting linear time in two-dimensional space.

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Yousif, Sami R., e Frank C. Keil. "The Shape of Space: Evidence for Spontaneous but Flexible Use of Polar Coordinates in Visuospatial Representations". Psychological Science 32, n. 4 (15 marzo 2021): 573–86. http://dx.doi.org/10.1177/0956797620972373.

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What is the format of spatial representation? In mathematics, we often conceive of two primary ways of representing 2D space, Cartesian coordinates, which capture horizontal and vertical relations, and polar coordinates, which capture angle and distance relations. Do either of these two coordinate systems play a representational role in the human mind? Six experiments, using a simple visual-matching paradigm, show that (a) representational format is recoverable from the errors that observers make in simple spatial tasks, (b) human-made errors spontaneously favor a polar coordinate system of representation, and (c) observers are capable of using other coordinate systems when acting in highly structured spaces (e.g., grids). We discuss these findings in relation to classic work on dimension independence as well as work on spatial representation at other spatial scales.

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Schwan, Nicole, Peter Brugger e Elisabeth Huberle. "Spatial Representation of Time in Backspace". Timing & Time Perception 6, n. 2 (30 luglio 2018): 154–68. http://dx.doi.org/10.1163/22134468-20181120.

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Temporal information, numerical magnitude and space extension appear to share common representational mechanisms and be processed similarly in the brain. Evidence comes from the phenomenon of ‘pseudoneglect’, i.e. healthy persons’ orientation asymmetry toward the left side of space. Pseudoneglect is also evident along the mental number line which extends from small numbers on the left to large numbers on the right. In analogy to numbers, time is typically represented on a line extending from the left to the right side. It may thus be no surprise that pseudoneglect has been demonstrated in the temporal domain as well. Besides the perception of the space located anteriorly to our trunk (frontspace), we are able to represent the space behind us, which we cannot visually perceive (backspace). The translational model suggests a mapping of spatially defined information to the ipsilateral side of the egocentric reference frame in front- and backspace, while the rotational concept focuses on a 360° spatial representation around the midsagittal plane of the trunk. At the present stage of investigation, little is known about the representation of temporal information in backspace. In an attempt to fill this gap, we compared duration estimations of auditory stimuli in frontspace and backspace. Healthy right-handers were instructed to judge their duration relative to each other. We found a pseudoneglect-behavior not only in frontspace but also in backspace. The data are discussed in the context of common processing mechanisms for time, numbers and space and favor a translational over a rotational account for the representation of backspace. The results are further discussed with reference to potential consequences for the rehabilitation of hemispatial neglect.

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Henderson, Marlone D., Cheryl J. Wakslak, Kentaro Fujita e John Rohrbach. "Construal Level Theory and Spatial Distance". Social Psychology 42, n. 3 (gennaio 2011): 165–73. http://dx.doi.org/10.1027/1864-9335/a000060.

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Growing evidence points to a bidirectional relationship between spatial distance and level of mental representation, whereby distant (vs. near) events are represented by a higher level of representation, and higher levels of representations increase perceptions of distance. In the current article, we review research that establishes this association and explores its implications. We begin by briefly describing construal level theory, the theoretical framework that gives rise to this associative prediction, and then review a set of theory-consistent findings that serve to illuminate the way that spatial distance influences cognition and behavior and the way in which people make judgments about spatial distance. Finally, we discuss open questions for future research on spatial distance using a construal level theory approach.

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Munnich, Edward, Barbara Landau e Barbara Anne Dosher. "Spatial language and spatial representation: a cross-linguistic comparison". Cognition 81, n. 3 (ottobre 2001): 171–208. http://dx.doi.org/10.1016/s0010-0277(01)00127-5.

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42

Glaser, I. "Multiplication of complex spatial signals using spatial carrier representation". Applied Optics 24, n. 6 (15 marzo 1985): 748_1. http://dx.doi.org/10.1364/ao.24.0748_1.

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Rico, J. M., e J. Duffy. "A Representation of the Euclidean Group by Spin Groups, and Spatial Kinematics Mappings". Journal of Mechanical Design 112, n. 1 (1 marzo 1990): 42–49. http://dx.doi.org/10.1115/1.2912577.

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A new derivation of the spin and biquaternion representation of the Euclidean group is presented. The derivation is based upon the even Clifford algebra representation of the orientation preserving orthogonal automorphisms of nondegenerate orthogonal spaces, also called spin representation. Embedding the degenerate orthogonal space IR1,0,3 into the nondegenerate orthogonal space IR1,4, and imposing certain conditions on the orthogonal automorphisms of IR1,4, one obtains a subgroup of the spin group. The action of this subgroup, on a subspace of IR1,4, is isomorphic to IR1,0,3, is precisely a Euclidean motion. The conditions imposed on the orthogonal automorphisms of IR1,4 lead to the biquaternion representation. Furthermore, the invariants of the representations are easily obtained. The derivation also allows the spin representation to be related to the action of the representation over an element of a three-dimensional vector space proposed by Porteous, and used by Selig. As a byproduct, the derivation provides an insightful interpretation of the dual unit used in both the spin representation and the biquaternion representation.

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44

Hommel, Bernhard. "Responding to object files: Automatic integration of spatial information revealed by stimulus-response compatibility effects". Quarterly Journal of Experimental Psychology Section A 55, n. 2 (aprile 2002): 567–80. http://dx.doi.org/10.1080/02724980143000361.

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Spatial information is assumed to play a central, organizing role in object perception and to be an important ingredient of object representations. Here, evidence is provided to show that automatically integrated spatial object information is also functional in guiding spatial action. In particular, retrieving nonspatial information about a previewed object facilitates responses that spatially correspond to this object. This is true whether the object is still in sight or has already disappeared. So, forming an object representation entails the integration and storage of action-related information concerning the action that the object affords.

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45

Bodenhamer, David. "Qualitative Spatial Representation: A Special Section". International Journal of Humanities and Arts Computing 13, n. 1-2 (ottobre 2019): 1. http://dx.doi.org/10.3366/ijhac.2019.0227.

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46

Stell, John G. "Qualitative Spatial Representation for the Humanities". International Journal of Humanities and Arts Computing 13, n. 1-2 (ottobre 2019): 2–27. http://dx.doi.org/10.3366/ijhac.2019.0228.

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Abstract (sommario):

‘Qualitative spatial reasoning and representation’ is a range of techniques developed in Artificial Intelligence to meet the need for a computational treatment of qualitative spatial relations. Examples of such relations include ‘next to’, ‘overlapping’, ‘to the left of’, ‘separate from’, ‘including’, and so on. These relations occur within the data found in the spatial humanities, but the computational techniques described here do not appear to have been used in connection with this context. While Geographical Information Systems (GIS) are widely used as a means of visualizing and exploring material in the spatial humanities, GIS technology is acknowledged to be ill-suited to information that is vague, uncertain, ambiguous, imprecise or having other qualities that in a scientific setting could be regarded as imperfections. In the humanities such ‘imperfections’ are of course important, and qualitative spatial relations are one source of data that challenges scientifically based GIS. This article reviews the origin of qualitative spatial reasoning and representation in A. N. Whitehead's mereotopology and argues for exploring how these methods could complement GIS as a computational technique in the humanities. Qualitative representation is applicable to modelling spatial arrangements in many domains, not just geographical space. This is demonstrated through an example of spatial relations in lines of printed text.

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Xiao, Chengli, Yangmin Lian e Mary Hegarty. "Spatial updating of map-acquired representation". Memory & Cognition 43, n. 7 (27 marzo 2015): 1032–42. http://dx.doi.org/10.3758/s13421-015-0520-8.

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48

Wang, Ranxiao Frances, e Elizabeth S. Spelke. "Human spatial representation: insights from animals". Trends in Cognitive Sciences 6, n. 9 (settembre 2002): 376–82. http://dx.doi.org/10.1016/s1364-6613(02)01961-7.

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49

Colby, Carol L., Jean-René Duhamel e Michael E. Goldberg. "Oculocentric Spatial Representation in Parietal Cortex". Cerebral Cortex 5, n. 5 (1995): 470–81. http://dx.doi.org/10.1093/cercor/5.5.470.

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50

Fischer, Martin H. "Bisection performance indicates spatial word representation". Cognitive Brain Research 4, n. 3 (ottobre 1996): 163–70. http://dx.doi.org/10.1016/s0926-6410(96)00029-8.

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