Uncovering how categories develop through childhood is crucial for cognitive science. However, even for simple domains, categories can be complex, making it challenging to access them experimentally, especially in developmental studies. Markov-Chain Monte Carlo with People (MCMCp) is a statistically-based procedure that allows us to elicit category members from participants' implicit categories. However, due to the complexity of the paradigm, MCMCp has been limited to experiments with adult populations. Here, we develop and validate a child-friendly method for applying MCMCp, producing the first MCMCp experiment to elicit category examples from children. Comparing fruit category members for five-year-olds and seven-year-olds, we find generally consistent representative fruits and a developmental progression of initially broad and overlapping fruit categories to more differentiated distributions.

Capturing the structure of human conceptual knowledge is a challenging but fundamental task. The most prominent approach, Multidimensional Scaling (MDS), usually requires many similarity judgments, which leads to long experiments, and only provides a representation of a fixed set of stimuli. In contrast, we present a more flexible method that can generalize to novel stimuli. This method uses a child-friendly task that allows researchers to uncover the development of categories with fewer participant judgments. We evaluate this approach on simulated data and find that it can accurately reveal representations even when trained on data generated by groups that categorize differently. We then analyze data from the World Color Survey and find that we can recover language-specific color organization. Finally, we use the method in a novel developmental experiment and find age-dependent differences in how fruit categories are structured. These results suggest that our method is widely applicable in developmental tasks.

Multidimensional scaling (MDS) has provided insight into the structure of human perception and conceptual knowledge. However, MDS usually requires participants to produce large numbers of similarity judgments, leading to prohibitively long experiments for most developmental research. Here we propose a method that combines simple grouping tasks with recent neural network models to uncover participants’ psychological spaces. We validate the method on simulated data and find that it can uncover the true structure even when given heterogeneous groupings. We then apply the method to data from the World Color Survey and find that it can uncover language-specific color organization. Finally, we apply the method to a novel developmental experiment and find age-dependent differences in conceptual spaces.

Uncovering how categories develop through childhood is crucial for cognitive science. However, even for simple domains, categories can be complex, making it challenging to access them experimentally. Here, we use an experimental method that allows us to produce exemplars from children’s implicit categories (Markov Chain Monte Carlo with People, MCMCP). Instead of querying pre-specified materials, MCMCP adaptively selects which stimuli to present, allowing us to focus on the most informative exemplars. We use MCMCP to uncover age-dependent differences in the category organization of fruits in a pre-registered online experiment. Comparing five-year-olds, seven-year-olds, and adults, we find a developmental progression of initially broad and overlapping fruit categories to more differentiated distributions. Furthermore, we find that across age groups, apple and grape categories exhibit bi-modal color distributions. We discuss these results in the context of theories of randomness reduction over the lifespan and shape biases in category induction.

Understanding how statistical regularities result in category learning requires access to the underlying psychological spacesin which these categories are represented. However, uncovering these spaces, especially in developmental settings, posessignificant experimental and methodological challenges what are relevant dimensions on which these spaces are organizedand how can we uncover them without prohibitively long or straining experiments?Here, we propose a novel way of uncovering these spaces. We learn participants implicit similarity functions, instantiatedas a neuronal network, by training on simple groupings of stimuli. In simulations, we show that our method can recovergroup-specific categorical structures. Furthermore, we show that young children quickly understand the grouping task, andspaces can be obtained in short, engaging experiments. Finally, we apply our method to uncover age-related differencesin category representations. In an experiment contrasting 4-5, 6-7 year-olds, and adults, we find that the learned spacesexhibit age-specific feature biases.