How Does Instance-Based Inference About Event Frequencies Develop? An Analysis with a Computational Process Model

To make inferences about the frequency of events in the world (e.g., the prevalence of diseases or the popularity of consumer products), people often exploit observations of relevant instances sampled from their personal social network. How does this ability to infer event frequencies by searching and relying on personal instance knowledge develop from childhood to adulthood? To address this question, we conducted a study in which children (age 8-11 years) and adults (age 19-34 years) judged the relative frequencies of first names in Germany. Based on the recalled instances of the names in participants' social networks, we modeled their frequency judgments and the underlying search process with a Bayesian hierarchical latent-mixture approach encompassing different computational models. We found developmental differences in the inference strategies that children and adults used. Whereas the judgments of most adults were best described by a noncompensatory strategy that assumes limited and sequentially ordered search (social-circle model), the judgments of most children were best described by a compensatory strategy that assumes exhaustive search and information aggregation (availability-by-recall). Our results highlight that already children use instance knowledge to infer event frequencies but they appear to search more exhaustively for instances than adults. One interpretation of these results is that the ability to conduct ordered and focused search is a central aspect in the development of noncompensatory instance-based inference.