Some Comments on Non-Euclidean Mental Maps

The Euclidean metric is perhaps the most commonly used and most convenient one for representing mapped phenomena. In this paper we examine the suitability of representing cognitive phenomena via the Euclidean metric. Some general properties of spaces are examined with particular emphasis on the properties of isotropy, incompleteness, and curvature, and a more detailed discussion is undertaken of the suitability of using curved spaces (particularly Reimannian spaces) for the representation of cognitive information. A final discussion is presented on the problems of handling manifolds with folds, warps, and tears; and speculations are made concerning the appropriateness of non-Euclidean metrics for the spatial representation of mental maps.