  The cognitive framework of conceptual spaces proposes to represent concepts
as regions in psychological similarity spaces. These similarity spaces are
typically obtained through multidimensional scaling (MDS), which converts human
dissimilarity ratings for a fixed set of stimuli into a spatial representation.
One can distinguish metric MDS (which assumes that the dissimilarity ratings
are interval or ratio scaled) from nonmetric MDS (which only assumes an ordinal
scale). In our first study, we show that despite its additional assumptions,
metric MDS does not necessarily yield better solutions than nonmetric MDS. In
this chapter, we furthermore propose to learn a mapping from raw stimuli into
the similarity space using artificial neural networks (ANNs) in order to
generalize the similarity space to unseen inputs. In our second study, we show
that a linear regression from the activation vectors of a convolutional ANN to
similarity spaces obtained by MDS can be successful and that the results are
sensitive to the number of dimensions of the similarity space.
