Spatial-temporal reasoning
3342061
222120264
2008-06-27T17:31:16Z
13.2.18.145
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{{Merge|Spatial visualization ability|date=June 2007}}
'''Spatial-temporal reasoning''' is the ability to visualize [[pattern|spatial patterns]] and mentally manipulate them over a time-ordered sequence of spatial transformations.
This ability is important for generating and conceptualizing solutions to multi-step problems that arise in areas such as [[architecture]], [[engineering]], [[science]], [[mathematics]], [[art]], games, and everyday life.
Whilst some visual thinkers (who account for around 60% of the general population)<ref>[http://www.learning-styles-online.com/overview/ Overview of learning styles<!-- Bot generated title -->]</ref> also have good spatial-temporal reasoning this does not make spatial-temporal reasoning exclusive to those who "think in pictures".
Spatial-temporal reasoning can have as much or more to do with one of the other 5 main modes of thought: the logical (mathematical/systems) style of thought<ref>[http://www.learning-styles-online.com/overview/ Overview of learning styles<!-- Bot generated title -->]</ref>.
Kinesthetic learners (physical learners who learn through body mapping and physical patterning) are highly developed in spatial awareness<ref>[http://www.grapplearts.com/Learning-Styles-in-Grappling.htm Visual, Auditory and Kinesthetic Learning Styles in Grappling<!-- Bot generated title -->]</ref> and may also visualize spatial patterns and movement direction without being predominantly those who 'think in pictures'<ref>[http://www.learning-styles-online.com/overview/ Overview of learning styles<!-- Bot generated title -->]</ref>. The same is true of logical thinkers (mathematical/systems thinking) who think in patterns and relationships and may work diagrammatically<ref>[http://www.learning-styles-online.com/overview/ Overview of learning styles<!-- Bot generated title -->]</ref> without this being pictorially and, as such, may have excellent spatial-temporal reasoning yet not necessarily be strong visual thinkers at all.
Spatial-temporal reasoning is also studied in [[computer science]]. It aims at describing the common-sense background knowledge on which our human perspective on the physical reality is based. Methodologically, qualitative [[Constraint satisfaction|constraint]] calculi restrict the vocabulary of rich mathematical theories dealing with temporal or spatial entities such that specific aspects of these theories can be treated within [[Decidability (logic)|decidable]] fragments with simple qualitative (non-[[Metric (mathematics)|metric]]) languages. Contrary to mathematical or physical theories about space and time, qualitative constraint calculi allow for rather inexpensive reasoning about entities located in space and time. For this reason, the limited expressiveness of qualitative representation formalism calculi is a benefit if such reasoning tasks need to be integrated in applications. For example, some of these calculi may be implemented for handling spatial [[Geographic information system|GIS]] queries efficiently and some may be used for navigating, and communicating with, a mobile [[robot]].
Examples of temporal calculi include the so-called [[point algebra]], [[Allen's Interval Algebra]], and Villain's [[point-interval calculus]]. The most prominent spatial calculi are [[Mereotopology|mereotopological calculi]], Frank's [[cardinal direction calculus]], Freksa's double cross calculus, Egenhofer and Franzosa's [[9-intersection calculus|4- and 9-intersection calculi]], Ligozat's [[flip-flop calculus]], and various [[Region Connection Calculus|region connection calculi]] (RCC). Recently, spatio-temporal calculi have been designed. For example, the [[Spatio-temporal Constraint Calculus]] (STCC) by Gerevini and Nebel combines Allen's interval algebra with RCC-8. Moreover, the [[Qualitative Trajectory Calculus]] (QTC) allows for reasoning about moving objects.
Most of these calculi can be formalized as abstract [[relation algebra]]s, such that reasoning can be carried out at a symbolic level. For computing solutions of a [[constraint network]], the [[path-consistency algorithm]] is an important tool.
== See also ==
*[[Region Connection Calculus]]
*[[cerebral cortex]]
*[[Visual thinking‎]]
==References==
*J. Renz, B. Nebel, [http://users.rsise.anu.edu.au/~jrenz/papers/renz-nebel-los.pdf Qualitative Spatial Reasoning using Constraint Calculi], in: M. Aiello, I. Pratt-Hartmann, J. van Benthem (eds.): Handbook of Spatial Logics, Springer 2007.
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[[Category:Cognitive science]]
[[Category:Artificial intelligence]]
[[Category:Learning styles]]
[[Category:Educational psychology]]