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Instance theory predicts categorization decisions in the absence of categorical structure: A computational analysis of artificial grammar learning without a grammar

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Abstract

Theories of categorization have historically focused on the stimulus characteristics to which people are sensitive. Artificial grammar learning (AGL) provides a clear example of this phenomenon, with theorists debating between knowledge of rules, fragments, whole strings, and so on as the basis of categorization decisions (i.e., stimulus-driven explanations). We argue that this focus loses sight of the more important question of how participants make categorization decisions on a mechanistic level (i.e., process-driven explanations). To address the problem, we derived predictions from an instance-based model of human memory in a pseudo-AGL task in which all study and test strings were generated randomly, a task that stimulus-driven explanations of AGL would have difficulty accommodating. We conducted a standard AGL experiment with human participants using the same strings. The model’s predictions corresponded to participants’ decisions well, even in the absence of any experimenter-generated structure and regardless of whether test stimuli contained any incidental structure. We argue that theories of categorization ought to continue shifting towards the goal of modeling categorization at the level of cognitive processes rather than primarily attempting to identify the stimulus characteristics to which participants are drawn.

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Data availability

The data and materials for all experiments are available at https://osf.io/25r39/. None of the experiments were preregistered.

Code availability

The code, including experiment scripts, analysis scripts, and model scripts, are available at https://osf.io/25r39/.

Notes

  1. The model was originally published under the name Holographic Exemplar Model. The name of the model was changed at the suggestion of R.K. Jamieson in Curtis and Jamieson (2019) to better reflect its historical connection and shared formalisms with MINERVA2.

  2. We did not conduct any power analysis to determine this sample size. This is a natural consequence of our planned analysis, which is outlined in greater detail in the Results section. In short, our model evaluation procedures used test strings as units of analysis. Degrees of freedom, then, are based on the number of test items rather than the number of participants.

  3. Due to a random seed issue, there were two instances of repeated strings, one in each stimulus set. The cause of the issue has not been determined. We chose to keep these strings in place rather than excluding them from analysis. If we had been conducting a typical AGL experiment where the repeated strings might create a confound in the experimental design (e.g., participants endorsement rates in one cell of the design are artificially increased as a result of the repeated item), this would be a more pressing issue. However, given that our primary goal is to determine whether our model tracks participants’ ratings, these strings simply stand as data points to be predicted.

  4. Mean ratings to each test string are provided in Appendix Table 3.

  5. Linear mixed effects models offer protection against the inflation of Type I error rates caused by the Lorch and Myers (1990) approach that has previously been employed in AGL research (see Johnstone & Shanks, 1999; Scott & Dienes, 2008).

  6. See Cook et al. (2017) for a summary of each metric.

  7. We do not mean to attack chunking accounts specifically. The criticism holds for theories explaining performance on the basis of grammaticality and similarity as well, and the sentence could easily be revised by replacing “high chunk strength” with “high similarity” or “grammatical characteristics”.

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The research was supported partially by a research stipend provided to all faculty members of Booth University College.

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    E. T. Curtis & I. Lebek

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Table 3 Study strings, test strings, and mean participant ratings
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Curtis, E.T., Lebek, I. Instance theory predicts categorization decisions in the absence of categorical structure: A computational analysis of artificial grammar learning without a grammar. Mem Cogn 52, 132–145 (2024). https://doi.org/10.3758/s13421-023-01449-9

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