Abstract
We test the effect of a landmark’s visual and structural salience on memory retrieval of turning directions at choice points in a VR environment. We find a higher probability for a correct turning decision at intersections where the location of the visually salient landmark converges with the turning direction as compared to intersections where the location of the visually salient landmark diverges from the turning direction. Although altered versions of the intersections were mostly recognized as being novel, we found systematic error patterns depending on the placement in the original intersection. A cognitive model in the ACT-R architecture grounds these findings in an established framework of human memory. Our findings have implications, for example, for the selection of suitable landmarks for navigation assistance systems.
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Alkaike Information Criterion [1].
References
Akaike, H.: A new look at the statistical model identification. IEEE Trans. Autom. Control 19(6), 716–723 (1974)
Anderson, J.R., Lebiere, C.J.: The Atomic Components of Thought. Psychology Press, Hove (1998)
Anderson, J.R., Bothell, D., Byrne, M.D., Douglass, S., Lebiere, C., Qin, Y.: An integrated theory of the mind. Psychol. Rev. 111(4), 1036 (2004)
Bates, D., Mächler, M., Bolker, B., Walker, S.: Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67(1), 1–48 (2015)
Buchner, A., Jansen-Osmann, P.: Is route learning more than serial learning? Spat. Cogn. Comput. 8(4), 289–305 (2008)
Denis, M., Pazzaglia, F., Cornoldi, C., Bertolo, L.: Spatial discourse and navigation: an analysis of route directions in the city of venice. Appl. Cogn. Psychol. 13(2), 145–174 (1999)
Hamburger, K., Knauff, M.: SQUARELAND: a virtual environment for investigating cognitive processes in human wayfinding. PsychNology J. 9, 137–163 (2011)
Hinterecker, T., Roser, F., Strickrodt, M., Hamburger, K.: SQUARELAND 2.0: a flexible and realistic virtual environment for investigating cognitive processes in human wayfinding. In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol. 36 (2014)
Itti, L., Koch, C.: Computational modelling of visual attention. Nat. Rev. Neurosci. 2(3), 194 (2001)
Janzen, G.: Memory for object location and route direction in virtual large-scale space. Q. J. Exp. Psychol. 59(3), 493–508 (2006)
Karimpur, H., Röser, F., Hamburger, K.: Finding the return path: landmark position effects and the influence of perspective. Front. Psychol. 7, 1956 (2016)
Klippel, A., Winter, S.: Structural salience of landmarks for route directions. In: Cohn, A.G., Mark, D.M. (eds.) COSIT 2005. LNCS, vol. 3693, pp. 347–362. Springer, Heidelberg (2005). https://doi.org/10.1007/11556114_22
Lovelace, K.L., Hegarty, M., Montello, D.R.: Elements of good route directions in familiar and unfamiliar environments. In: Freksa, C., Mark, D.M. (eds.) COSIT 1999. LNCS, vol. 1661, pp. 65–82. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-48384-5_5
Lyon, D.R., Gunzelmann, G., Gluck, K.A.: A computational model of spatial visualization capacity. Cogn. Psychol. 57(2), 122–152 (2008)
Michon, P.-E., Denis, M.: When and why are visual landmarks used in giving directions? In: Montello, D.R. (ed.) COSIT 2001. LNCS, vol. 2205, pp. 292–305. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-45424-1_20
Miller, J., Carlson, L.: Selecting landmarks in novel environments. Psychon. Bull. Rev. 18(1), 184–191 (2011)
Montello, D.R.: Landmarks are exaggerated. KI-Künstliche Intelligenz 31(2), 193–197 (2017)
Peters, D., Wu, Y., Winter, S.: Testing landmark identification theories in virtual environments. In: Hölscher, C., Shipley, T.F., Olivetti Belardinelli, M., Bateman, J.A., Newcombe, N.S. (eds.) Spatial Cognition 2010. LNCS (LNAI), vol. 6222, pp. 54–69. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14749-4_8
Quesnot, T., Roche, S.: Measure of landmark semantic salience through geosocial data streams. ISPRS Int. J. Geo-Inf. 4(1), 1–31 (2014)
R Core Team: R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2016). https://www.R-project.org/
Raubal, M., Winter, S.: Enriching wayfinding instructions with local landmarks. In: Egenhofer, M.J., Mark, D.M. (eds.) GIScience 2002. LNCS, vol. 2478, pp. 243–259. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45799-2_17
Reitter, D., Lebiere, C.: A cognitive model of spatial path-planning. Comput. Math. Organ. Theory 16(3), 220–245 (2010)
Richter, K.F., Winter, S.: Landmarks, vol. 10. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-319-05732-3., pp. 978–983
Röser, F., Hamburger, K., Knauff, M.: The giessen virtual environment laboratory: human wayfinding and landmark salience. Cogn. Process. 12(2), 209–214 (2011)
Röser, F., Krumnack, A., Hamburger, K.: The influence of perceptual and structural salience. In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol. 35 (2013)
Röser, F., Krumnack, A., Hamburger, K., Knauff, M.: A four factor model of landmark salience, a new approach. In: Krüger, A., Butz, A., Oliver, P. (eds.) Proceedings of the 11th International Conference on Cognitive Modeling (ICCM), pp. 82–87. Springer, Heidelberg (2012)
Röser, F., Hamburger, K., Krumnack, A., Knauff, M.: The structural salience of landmarks: results from an on-line study and a virtual environment experiment. J. Spat. Sci. 57(1), 37–50 (2012)
Schwering, A., Krukar, J., Li, R., Anacta, V.J., Fuest, S.: Wayfinding through orientation. Spat. Cogn. Comput. 17(4), 273–303 (2017)
Siegel, A.W., White, S.H.: The development of spatial representations of large-scale environments. In: Advances in Child Development and Behavior, vol. 10, pp. 9–55. Elsevier (1975)
Sorrows, M.E., Hirtle, S.C.: The nature of landmarks for real and electronic spaces. In: Freksa, C., Mark, D.M. (eds.) COSIT 1999. LNCS, vol. 1661, pp. 37–50. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-48384-5_3
von Stülpnagel, R., Steffens, M.C.: Active route learning in virtual environments: disentangling movement control from intention, instruction specificity, and navigation control. Psychol. Res. 77(5), 555–574 (2013)
Wenczel, F., Hepperle, L., von Stülpnagel, R.: Gaze behavior during incidental and intentional navigation in an outdoor environment. Spat. Cogn. Comput. 17(1–2), 121–142 (2017)
Winter, B.: Linear models and linear mixed effects models in R with linguistic applications. arXiv preprint arXiv:1308.5499 (2013)
Zhao, C.: Understanding human spatial navigation behaviors: a novel cognitive modeling approach. The Pennsylvania State University (2016)
Acknowledgements
We thank Benedikt Solf for collecting the data and his idea of including foil blocks and Karoline Greger for implementing the SQUARELAND ACT-R environment.
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Albrecht, R., von Stuelpnagel, R. (2018). Memory for Salient Landmarks: Empirical Findings and a Cognitive Model. In: Creem-Regehr, S., Schöning, J., Klippel, A. (eds) Spatial Cognition XI. Spatial Cognition 2018. Lecture Notes in Computer Science(), vol 11034. Springer, Cham. https://doi.org/10.1007/978-3-319-96385-3_21
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