Accurate knowledge about feature diagnosticities leads to less preference for unidimensional strategy

In classification learning of artificial stimuli, participants learn the perfectly diagnostic dimension better than the partially diagnostic dimensions. Also, there is a strong preference for a unidimensional categorization based on the perfectly diagnostic dimension. In a different experimental procedure, called array-based classification task, participants do not exhibit a preference for a unidimensional categorization. In Experiment 1, we replicate the above results. In Experiment 2, we show that when participants learn the partially diagnostic features through repeated testing, there is a decrease in unidimensional categorization. We use Bayesian modeling to show that only those participants who learned the diagnosticity of a dimension with a high level of accuracy (≥ 75%) used the dimension for categorization. Our results show that whenever accurate knowledge about feature diagnosticities is available, there is a lesser preference for unidimensional categorization. Our results provide a possible explanation for the preference of unidimensional categorization in classification and observation learning. (PsycInfo Database Record (c) 2023 APA, all rights reserved).