Imagine you’re trying to categorize different types of dogs based on their features and their relationships. Well, researchers have been studying how our understanding of cause and effect influences our categorization process. They’ve used Bayesian causal models to analyze this phenomenon. The researchers found that when it comes to categorization, people are more likely to calculate probabilities rather than just consider how likely a certain combination of features is given the category’s causal model. This result challenges previous assumptions about causal-based categorization. Interestingly, the study also found that people can still compute these likelihoods in a related task that focuses on consistency judgments rather than category membership decisions. Overall, the research suggests that humans do rely on causal probabilistic information as proposed by Bayesian models, but their calculations can be flexible depending on the specific task at hand. For more details and in-depth information, check out the full article!

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.

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