Abstract
To navigate through daily life, humans use their ability to conceptualize spatio-temporal information, which ultimately leads to a system of categories. Likewise, the spatial sciences rely heavily on conceptualization and categorization as means to create knowledge when they process spatio-temporal data. In the spatial sciences and in related branches of artificial intelligence, an approach has been developed for processing spatio-temporal data on the level of coarse categories: qualitative spatio-temporal representation and reasoning (QSTR). Calculi developed in QSTR allow for the meaningful processing of and reasoning with spatio-temporal information. While qualitative calculi are widely acknowledged in the cognitive sciences, there is little behavioral assessment whether these calculi are indeed cognitively adequate. This is an astonishing conundrum given that these calculi are ubiquitous, are often intended to improve processes at the human–machine interface, and are on several occasions claimed to be cognitively adequate. We have systematically evaluated several approaches to formally characterize spatial relations from a cognitive-behavioral perspective for both static and dynamically changing spatial relations. This contribution will detail our framework, which is addressing the question how formal characterization of space can help us understand how people think with, in, and about space.
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References
Camara K, Jungert E (2007) A visual query language for dynamic processes applied to a scenario driven environment. J Vis Lang Comput 18:315–338
Cohn AG, Renz J (2008) Qualitative spatial representation and reasoning. In: van Harmelen F, Lifschitz V, Porter B (eds.), Foundations of artificial intelligence. Handbook of knowledge representation (1st ed), Amsterdam, Elsevier, pp 551–596
Egenhofer MJ, Franzosa RD (1991) Point-set topological spatial relations. Int J Geogr Inf Syst 5(2):161–174
Egenhofer MJ, Mark DM (1995) Modeling conceptual neighborhoods of topological relations. Int J Geogr Inf Syst 9(5):555–565
Freksa C (1991) Conceptual neighborhood and its role in temporal and spatial reasoning. In: Singh MG, Trav′e-Massuy`es L (eds), Proceedings of the IMACS Workshop on Decision Support Systems and Qualitative Reasoning, North-Holland, Amsterdam, Elsevier, pp 181–187
Galton A (2000) Qualitative spatial change. Spatial information systems. Oxford University Press, Oxford
Gibson J (1979) The ecological approach to visual perception. Houghton Mifflin, Boston, MA
Knauff M, Rauh R, Renz J (1997) A cognitive assessment of topological spatial relations: Results from an empirical investigation. In: Hirtle SC, Frank AU (eds) Spatial information theory: a theoretical basis for GIS. Springer, Berlin, pp 193–206
Klippel A (in print) Spatial information theory meets spatial thinking—is topology the Rosetta Stone of spatio-temporal cognition? Annals of the Association of American Geographers, (67 manuscript pages)
Kordjamshidi P, Otterlo M von, Moens M-F (2010) From language towards formal spatial calculi. In: Ross RJ, Hois J, Kelleher J (eds), Computational models of spatial language interpretation (CoSLI) Workshop at Spatial Cognition 2010, Mt. Hood, Oregon, 17–24. CEUR Workshop Proceedings
Lakoff G (1990) The invariance hypothesis: is abstract reason based on image schemata? Cogn Linguistics 1(1):39–74
Medin DL, Wattenmaker WD, Hampson SE (1987) Family resemblance, conceptual cohesiveness, and category construction. Cogn Psychol 19(2):242–279
Moratz R, Tenbrink T (2006) Spatial reference in linguistic human-robot interaction: iterative, empirically supported development of a model of projective relations. Spatial Cogn Comput 6(1):63–106
Randell DA, Cui Z, Cohn AG (1992) A spatial logic based on regions and connections. In: Nebel B, Rich C, Swartout WR (eds), Proceedings of the 3rd International Conference on Knowledge Representation and Reasoning, San Francisco, Morgan Kaufmann, pp 165–176
Schultz C, Amor R, Guesgen HW (2011) Methodologies for qualitative spatial and temporal reasoning application design. In: Hazarika SM (ed), Qualitative Spatio-temporal representation and reasoning. Trends and future directions. Hershey, IGI Global
Sridhar M, Cohn A, Hogg D (2011) From Video to RCC8: exploiting a distance based semantics to stabilise the interpretation of mereotopological relations: spatial information theory. In: Egenhofer M, Giudice N, Moratz R, Worboys M (eds), Lecture notes in computer science. Spatial Information Theory. 10th International Conference, COSIT 2011, Belfast, ME, USA, September 12–16. Proceedings, 110–125. Berlin, Springer
Acknowledgments
This research is funded by the National Science Foundation (#0924534). Additionally, F. Dylla acknowledges funding by German Research Organization (DFG) SFB/TR8 Spatial Cognition.
Conflict of interest
This supplement was not sponsored by outside commercial interests. It was funded entirely by ECONA, Via dei Marsi, 78, 00185 Roma, Italy
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Klippel, A., Wallgrün, J.O., Yang, J. et al. Formally grounding spatio-temporal thinking. Cogn Process 13 (Suppl 1), 209–214 (2012). https://doi.org/10.1007/s10339-012-0451-2
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DOI: https://doi.org/10.1007/s10339-012-0451-2