Humans have remarkable abilities to find deep similarities in what are superficially different experiences and to use those similarities to help categorize their experiences. This process of categorization allows people to learn from examples and to generalize that knowledge to new situations. Categorization is also a fundamental aspect of human reasoning. This work investigates whether a key contributor to human categorization success is the ability to name the features and dimensions that compose the categories. Testing the causal link between naming and categorizing helps us understand why children with poorer language skills often go on to have poor academic outcomes, even in domains that appear to be nonverbal in nature like geometry. This work enhances scientific infrastructure by developing a freely available online tool for measuring the nameability of any visual or auditory set of items for adults and children, which will help researchers and educators more effectively describe learning strategies and allow for better understanding the sources of children's reasoning errors.
To explain what makes some categories harder to learn than others, researchers have typically posited a fixed set of features that are available to the learner. But where do the features come from? This work tests the hypothesis that an important source of features is the words people learn when learning a language. On this view, the words of a language do not simply map onto pre-existing conceptual distinctions, but are one of the contributing factors that create the distinctions. This hypothesis is tested using a series of category-learning and category-induction experiments with adults and a unique population of linguistically deprived children. The experiments systematically test the extent to which ease of naming predicts categorization success and tests the causal involvement of language by using verbal interference protocols. The behavioral work is guided by computational modeling using convolutional neural networks to help disentangle whether a category is easy to learn because it is nameable or whether it is nameable because it is easy to learn. This work is among the first to use deep-learning models to understand human categorization.
