A Context‐Dependent Bayesian Account for Causal‐Based Categorization

Abstract

The causal view of categories assumes that categories are represented by features and their causal relations. To study the effect of causal knowledge on categorization, researchers have used Bayesian causal models. Within that framework, categorization may be viewed as dependent on a likelihood computation (i.e., the likelihood of an exemplar with a certain combination of features, given the category's causal model) or as a posterior computation (i.e., the probability that the exemplar belongs to the category, given its features). Across three experiments, in combination with computational modeling, we offer evidence that categorization is better accounted for by assuming that people compute posteriors and not likelihoods, though both probabilities are closely related. This result contrasts with existing analyses of causal-based categorization, which assume that likelihood computations give a good approximation of human judgments. We also find that people are able to compute likelihoods in a closely related task that elicits judgments of consistency rather than category membership judgments. Our analyses show that people do use causal probabilistic information as prescribed by a Bayesian model but that they flexibly compute likelihoods or posteriors depending on the task. We discuss our results in relation to the relevant literature on the topic.

The common effect of value on prioritized memory and category representation

“…Possibly, the relationship is coincidental: it might be that value judgments just happen to play a role in each of these separate effects but that the two effects do not relate in any way to each other. For example, it might be that the impact of value on category representation has nothing to do with prioritized memory and is instead the results of a completely separate process involving conceptual coherence [32][33][34]. At the moment, there is no concrete empirical evidence against this hypothesis, and it therefore remains very much an open possibility.…”

Uncertainty can explain apparent mistakes in causal reasoning

“…Bayes nets are a formalism for normative causal reasoning, but researchers also hold that they might provide a good general hypothesis for how people represent and reason about causal systems (Glymour, 2003;Gopnik & Wellman, 2012;Hagmayer, 2016;Holyoak & Cheng, 2011;Rips, 2008;Rottman & Hastie, 2014;Sloman & Lagnado, 2015;Quillien & Lucas, 2022). Indeed, many studies suggest that causality is central to human cognition, and that people reason in a way that is wellapproximated by algorithms for inference on causal Bayes nets (Waldmann, Holyoak, & Frantianne, 1995;Gopnik & Wellman, 2012;Griffiths & Tenenbaum, 2005;Rottman & Hastie, 2014;Marchant, Quillien, & Chaigneau, 2023). Against this background, it is surprising that one of the most replicable findings in the field is that people also consistently flout basic axioms of causal reasoning.…”