Learning from Exemplars and Prototypes in Machine Learning and Psychology

TL;DR

This paper explores the parallels between cognitive psychology's similarity-based categorisation models and machine learning's nearest neighbour classifiers, highlighting how their integration can inspire new models in both fields.

Contribution

It draws a comparison between psychological and machine learning models, proposing that their integration can enhance similarity-based categorisation methods.

Findings

Machine learning methods for prototype and exemplar selection are analogous to psychological categorisation models.

Cross-disciplinary insights can lead to improved similarity-based classification models.

The paper encourages mutual enrichment between cognitive psychology and machine learning.

Abstract

This paper draws a parallel between similarity-based categorisation models developed in cognitive psychology and the nearest neighbour classifier (1-NN) in machine learning. Conceived as a result of the historical rivalry between prototype theories (abstraction) and exemplar theories (memorisation), recent models of human categorisation seek a compromise in-between. Regarding the stimuli (entities to be categorised) as points in a metric space, machine learning offers a large collection of methods to select a small, representative and discriminative point set. These methods are known under various names: instance selection, data editing, prototype selection, prototype generation or prototype replacement. The nearest neighbour classifier is used with the selected reference set. Such a set can be interpreted as a data-driven categorisation model. We juxtapose the models from the two fields to enable cross-referencing. We believe that both machine learning and cognitive psychology can draw inspiration from the comparison and enrich their repertoire of similarity-based models.