Proceedings of the Annual Meeting of the Cognitive Science Society

To make inferences about the frequency of events in theworld (e.g., the prevalence of diseases or the popularity ofconsumer products), people often exploit observations ofrelevant instances sampled from their personal social network.How does this ability to infer event frequencies by searchingand relying on personal instance knowledge develop fromchildhood to adulthood? To address this question, weconducted a study in which children (age 8–11 years) andadults (age 19–34 years) judged the relative frequencies offirst names in Germany. Based on the recalled instances of thenames in participants’ social networks, we modeled theirfrequency judgments and the underlying search process with aBayesian hierarchical latent-mixture approach encompassingdifferent computational models. We found developmentaldifferences in the inference strategies that children and adultsused. Whereas the judgments of most adults were bestdescribed by a noncompensatory strategy that assumes limitedand sequentially ordered search (social-circle model), thejudgments of most children were best described by acompensatory strategy that assumes exhaustive search andinformation aggregation (availability-by-recall). Our resultshighlight that already children use instance knowledge to inferevent frequencies but they appear to search more exhaustivelyfor instances than adults. One interpretation of these results isthat the ability to conduct ordered and focused search is acentral aspect in the development of noncompensatoryinstance-based inference.