Abstract
It is an ongoing and controversial debate in cognitive science which aspects of knowledge humans process visually and which ones they process spatially. Similarly, artificial intelligence (AI) and cognitive science research, in building computational cognitive systems, tended to use strictly spatial or strictly visual representations. The resulting systems, however, were suboptimal both with respect to computational efficiency and cognitive plau sibility. In this paper, we propose that the problems in both research strands stem from a mis conception of the visual and the spatial in mental spatial knowl edge pro cessing. Instead of viewing the visual and the spatial as two clearly separable categories, they should be conceptualized as the extremes of a con tinuous dimension of representation. Regarding psychology, a continuous di mension avoids the need to exclusively assign processes and representations to either one of the cate gories and, thus, facilitates a more unambiguous rating of processes and rep resentations. Regarding AI and cognitive science, the con cept of a continuous spatial / visual dimension provides the possibility of rep re sentation structures which can vary continuously along the spatial / visual di mension. As a first step in exploiting these potential advantages of the pro posed conception we (a) introduce criteria allowing for a non-dichotomic judgment of processes and representations and (b) present an approach towards rep re sentation structures that can flexibly vary along the spatial / visual dimension.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aginsky, V., Harris, C., Rensink, R.: Two strategies for learning a route in a driving simulator. Journal of Environmental Psychology 17, 317–331 (1997)
Barkowsky, T.: Mental representation and processing of geographic knowledge - A computational approach. Springer, Heidelberg (2002)
Bertel, S., Barkowsky, T., Engel, D.: Computational modeling of reasoning with mental images: basic requirements. In: Fum, D., Missier, d.F., Stocco, A. (eds.) Proceedings of ICCM 2006, Trieste, pp. 50–55. Edizioni Goliardiche, Trieste (2006)
Brockmole, J.R., Wang, R.F.: Switching between environmental representations in memory. Cognition 83, 295–316 (2002)
Carlson, L.A.: Selecting a reference frame. Spatial Cognition and Computation 1(4), 365–379 (1999)
Chandrasekaran, B., Kurup, U., Banerjee, B., Josephson, J.R.: An Architecture for Problem Solving with Diagrams. In: Blackwell, A., Marriott, K., Shimojima, A. (eds.) Proceedings of Diagrams 2004, pp. 151–165. Springer, Heidelberg (2004)
Chang, S.K., Shi, Q.Y., Yan, C.W.: Iconic indexing by 2-D string. IEEE Transactions on Pattern Analysis and Machine Intelligence 9(3), 413–428 (1987)
Charlot, V., Tzourio, N., Zilbovicius, M., Mazoyer, B., Denis, M.: Different mental imagery abilities result in different regional cerebral blood flow activation patterns during cognitive tests. Neuropsychologia 30, 565–580 (1992)
Cohn, A.G., Bennett, B., Gooday, J.M., Gotts, N.: RCC: a calculus for region based qualitative spatial reasoning. GeoInformatica 1, 275–316 (1997)
Collins, A.M., Quillian, M.R.: Retrieval time from semantic memory. Journal of Verbal Learning and Verbal Behavior 8, 240–247 (1969)
De Vooght, G., Vandierendonck, A.: Spatial Mental Models in Linear Reasoning. Kognitionswissenschaft 7(1), 5–10 (1998)
Easton, R.D., Sholl, M.J.: Object-array structure, frames of reference, and retrieval of spatial knowledge. Journal of Experimental Psychology: Learning, Memory, and Cognition 21(2), 483–500 (1995)
Egenhofer, M.J.: Deriving the composition of binary topological relations. Journal of Visual Languages and Computing 5, 133–149 (1994)
Frank, A.: Qualitative spatial reasoning: Cardinal directions as an example. International Journal of Geographical Information Systems (1995)
Funt, B.: Problem-solving with diagrammatic representations. Artificial Intelligence 13, 201–230 (1980)
Gilhooly, K.J., Logie, R.H., Wetherick, N.E., Wynn, V.: Working memory and strategies in syllogistic reasoning tasks. Mem. Cognit. 21, 115–124 (1993)
Glasgow, J., Papadias, D.: Computational imagery. Cognitive Science 16, 355–394 (1992)
Goodwin, G.P., Johnson-Laird, P.N.: Reasoning About Relations. Psychological Review 112(2), 468–493 (2005)
Haxby, J.V., Grady, C.L., Horwitz, B., Ungerleider, L.G., Mishkin, M., Carson, R.E., Herscovitch, P., Schapiro, M.B., Rapoport, S.I.: Dissociation of object and spatial visual processing pathways in human extrastriate cortex. Proceedings of the National Academy of Sciences 88, 1621–1625 (1991)
Hegarty, M., Kozhevnikov, M.: Types of visual-spatial representations and mathematical problem solving. Journal of Educational Psychology 91(4), 684–689 (1999)
Hirtle, S.C., Jonides, J.: Evidence of hierarchies in cognitive maps. Memory & Cognition 13, 208–217 (1985)
Ishai, A., Sagi, D.: Visual imagery facilitates visual perception: Psychophysical evidence. Journal of Cognitive Neuroscience 9(4), 476–489 (1997)
Jahn, G.: Hybrid representation of spatial descriptions. International workshop Spatial and Visual Components in Mental Reasoning about Large-Scale Spaces (01-02 September 2003), Bad Zwischenahn, Germany (2003)
Johnson-Laird, P.N.: Mental models. Harvard University Press, Cambridge, MA (1983)
Khenkhar, M.: Object-oriented representation of depictions on the basis of cell matrices. In: Herzog, O., Rollinger, C.-R. (eds.) Text Understanding in LILOG. LNCS, vol. 546, pp. 645–656. Springer, Heidelberg (1991)
Klauer, K.C.: Double dissociations in visual and spatial short-term memory. Journal of Experimental Psychology: General 133(3), 355–381 (2004)
Knauff, M., Johnson-Laird, P.N.: Visual imagery can impede reasoning. Memory & Cognition 30(3), 363–371 (2002)
Kosslyn, S.M.: Image and Mind. Harvard University Press, Cambridge, MA (1980)
Kosslyn, S.M.: Image and brain - The resolution of the imagery debate. MIT Press, Cambridge, MA (1994)
Kosslyn, S.M., Sussman, A.L.: Roles of imagery in perception: Or, there is no such thing as immaculate perception. In: Gazzaniga, M.S. (ed.) The cognitive neurosciences, pp. 1035–1042. MIT Press, Cambridge, MA (1995)
Kosslyn, S.M., Thompson, W.L.: When is early visual cortex activated during visual mental imagery? Psychological Bulletin 129(5), 723–746 (2003)
Kozhevnikov, M., Hegarty, M., Mayer, R.E.: Revisiting the visualizer-verbalizer dimension: Evidence for two types of visualizers. Cognition & Instruction 20, 47–78 (2002)
Kozhevnikov, M., Kosslyn, S., Shepard, J.: Spatial versus object visualizers: A new characterization of visual cognitive style. Memory & Cognition (in press)
Leeuwenberg, E.: Structural information theory and visual form. In: Kaernbach, C., Schröger, E., Müller, H. (eds.) Psychophysics beyond sensation, pp. 481–505. Lawrence Erlbaum, Mahwah, NJ (2004)
Levine, D.N., Warach, J., Farah, M.: Two visual systems in mental imagery: Dissociation of ”what” and ”where” in imagery disorders due to bilateral posterior cerebral lesions. Neurology 35, 1010–1018 (1985)
Likert, A., Quasha, W.H.: Revised Minnesota Paper Form Board Test (Series AA), New York. The Psychological Corporation (1941)
McNamara, T.P.: Mental representations of spatial judgments. Cognitive Psychology 18, 87–121 (1986)
Mellet, E., Tzourio, N., Denis, M., Mazoyer, B.: A positron emission tomography study of visual and mental spatial exploration. Journal of Cognitive Neuroscience 16, 6504–6512 (1995)
Moratz, R., Nebel, B., Freksa, C.: Qualitative spatial reasoning about relative position: The tradeoff between strong formal properties and successful reasoning about route graphs. In: Freksa, C., Brauer, W., Habel, C., Wender, K.F. (eds.) Spatial Cognition III. LNCS (LNAI), vol. 2685, pp. 385–400. Springer, Heidelberg (2003)
Ragni, M., Knauff, M.: A Computational Model for Spatial Reasoning with Mental Models. In: Bara, B.G., Barsalou, L., Bucciarelli, M. (eds.) Proceedings of the 27th Annual Cognitive Science Conference, p. 1797. LEA (2005)
Rieser, J.J.: Access to knowledge of spatial structure at novel points of observation. Journal of Experimental Psychology: Learning, Memory, and Cognition 15(6), 1157–1165 (1989)
Schlieder, C., Berendt, B.: Mental model construction in spatial reasoning: A comparison of two computational theories. In: Schmid, U., Krems, J.F., Wysotzki, F. (eds.) Mind modelling: A cognitive science approach to reasoning, learning and discovery, pp. 133–162. Pabst Science Publishers, Lengerich (1998)
Sharma, J.: Integrated Spatial Reasoning in Geographic Information Systems: Combining Topology and Direction. Ph. D. Thesis, University of Maine (1996)
Sloman, A.: Why we need many knowledge representation formalisms. In: Bramer, M. (ed.) Proceedings BCS Expert Systems Conf. 1984, Cambridge University Press, Cambridge (1985)
Stenning, K., Oberlander, J.: A cognitive theory of graphical and linguistic reasoning: logic and implementation. Cognitive Science 19, 97–140 (1995)
Ungerleider, L.G., Mishkin, M.: Two cortical visual systems. In: Ingle, D.J., Goodale, M.A., Mansfield, R.J.W. (eds.) Analysis of visual behavior, pp. 549–586. MIT Press, Cambridge, MA (1982)
Vandenberg, S.G., Kuse, A.R.: Mental Rotations, a group test of three-dimensional spatial visualization. Perception and Motor Skills 47, 599–604 (1978)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Schultheis, H., Bertel, S., Barkowsky, T., Seifert, I. (2007). The Spatial and the Visual in Mental Spatial Reasoning: An Ill-Posed Distinction. In: Barkowsky, T., Knauff, M., Ligozat, G., Montello, D.R. (eds) Spatial Cognition V Reasoning, Action, Interaction. Spatial Cognition 2006. Lecture Notes in Computer Science(), vol 4387. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75666-8_12
Download citation
DOI: https://doi.org/10.1007/978-3-540-75666-8_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75665-1
Online ISBN: 978-3-540-75666-8
eBook Packages: Computer ScienceComputer Science (R0)