Generalizing Psychological Similarity Spaces to Unseen Stimuli

Lucas Bechberger,Kai-Uwe Kühnberger

doi:10.1007/978-3-030-69823-2_2

Abstract

AbstractThe 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.

Highlights

In this chapter, we propose a combination of psychologically derived similarity ratings with modern machine learning techniques in the context of cognitive artificial intelligence
We base our work on the cognitive framework of conceptual spaces (Gärdenfors 2000), which proposes a geometric representation of conceptual structures: Instances are represented as points and concepts are represented as regions in psychological similarity spaces
Metric and nonmetric SMACOF yield almost identical performance with respect to both metric and nonmetric stress. This suggests that interpreting the SpAM dissimilarity ratings as ratio scaled is neither helpful nor harmful

Summary

Introduction

We propose a combination of psychologically derived similarity ratings with modern machine learning techniques in the context of cognitive artificial intelligence. We base our work on the cognitive framework of conceptual spaces (Gärdenfors 2000), which proposes a geometric representation of conceptual structures: Instances are represented as points and concepts are represented as regions in psychological similarity spaces. Based on this representation, one can explain a range of cognitive phenomena from one-shot learning to concept combination. Conceptual spaces can be related to the feature spaces typically used in machine learning (Mitchell 1997), where individual observations are represented as sets of feature values and where the task is to identify regions which correspond to pre-defined categories

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Conclusion