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The Role of Naturalness in Concept Learning: A Computational Study

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Abstract

This paper studies the learnability of natural concepts in the context of the conceptual spaces framework. Previous work proposed that natural concepts are represented by the cells of optimally partitioned similarity spaces, where optimality was defined in terms of a number of constraints. Among these is the constraint that optimally partitioned similarity spaces result in easily learnable concepts. While there is evidence that systems of concepts generally regarded as natural satisfy a number of the proposed optimality constraints, the connection between naturalness and learnability has been less well studied. To fill this gap, we conduct a computational study employing two standard models of concept learning. Applying these models to the learning of color concepts, we examine whether natural color concepts are more readily learned than nonnatural ones. Our findings warrant a positive answer to this question for both models employed, thus lending empirical support to the notion that learnability is a distinctive characteristic of natural concepts.

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Notes

  1. Or by sets of convex regions, if we are dealing with a multi-domain concept. We take this qualification to be read from here on.

  2. The RGB coordinates of the Munsell chips were downloaded from the website of the Munsell Color Science Laboratory of the Rochester Institute of Technology and converted to CIELUV coordinates using the Colors.jl package for the Julia language.

  3. For instance, Gärdenfors’ model has been used to derive prior probabilities for items falling under specific concepts (Decock et al., 2016) and to formalize vagueness (Douven et al., 2013; Douven, 2016; Douven et al., 2017) as well as analogical reasoning (Douven et al., 2022). It is not clear to us how any of that could be accomplished using the GCM.

  4. For similar results, see Kemp and Regier (2012); Xu and Regier (2014); Xu et al. (2016), Zaslavsky et al. (2022), and Mollica et al. (2021).

  5. In Nosofsky’s GCM, there are parameters to model response bias, memory strength, and salience of dimensions, which help to model recency and context effects. For our computational implementation, these do not matter. For instance, the order in which data are stored in computer memory makes no difference to how easily they can be retrieved.

  6. As an anonyous referee remarked, it would be interesting to conduct the simulations to be presented in the following also for the full set of 1625 Munsell chips, especially given that the chips for the WCS were all selected precisely because they all show highly saturated colors, thus raising the question whether we would obtain the same results if also chips showing less saturated colors were included. A practical problem here is that we currently lack data on how people would carve up the full set of Munsell chips into the eleven basic color categories. Jraissati and Douven (2018) did use the full set in their study, but presented their participants with a free-naming task, meaning that they could use any name they liked for any of the chips they were shown.

  7. Note that because a random number of chips was sampled randomly from each concept, there was no fixed sample size. It was empirically determined that the sample size was, on average, 165.02 (± 31.98).

  8. The nonnatural concepts have the color resulting from averaging the CIELUV coordinates of the chips that fall under the concept. Also, all color concepts that occurred in the study—both the natural ones and the nonnatural ones—were convex, by construction. That the charts shown in Fig. 6 might suggest otherwise is simply because these charts are not meant to represent a perceptual color space.

  9. I am grateful to Christopher von Bülow and to two anonymous referees for this journal for valuable comments on a previous version.

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    Igor Douven

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The Supplementary Materials, including the Julia file that was used for the simulations reported, are available in a GitHub repository which can be accessed via this link https://github.com/IgorDouven/Concept_Learning.

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Douven, I. The Role of Naturalness in Concept Learning: A Computational Study. Minds & Machines 33, 695–714 (2023). https://doi.org/10.1007/s11023-023-09652-y

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