Active inductive inference in children and adults: A constructivist perspective

Neil R Bramley*, Fei Xu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

A defining aspect of being human is an ability to reason about the world by generating and adapting ideas and hypotheses. Here we explore how this ability develops by comparing children’s and adults’ active search and explicit hypothesis generation patterns in a task that mimics the open-ended process of scientific induction. In our experiment, 54 children (aged 8.97 ± 1.11) and 50 adults performed inductive inferences about a series of causal rules through active testing. Children were more elaborate in their testing behavior and generated substantially more complex guesses about the hidden rules. We take a ‘computational constructivist’ perspective to explaining these patterns, arguing that these inferences are driven by a combination of thinking (generating and modifying symbolic concepts) and exploring (discovering and investigating patterns in the physical world). We show how this framework and rich new dataset speak to questions about developmental differences in hypothesis generation, active learning and inductive generalization. In particular, we find children’s learning is driven by less fine-tuned construction mechanisms than adults’, resulting in a greater diversity of ideas but less reliable discovery of simple explanations
Original languageEnglish
Article number105471
Early online date24 May 2023
Publication statusPublished - Sept 2023

Keywords / Materials (for Non-textual outputs)

  • hypothesis generation
  • active learning
  • inductive inference
  • developmental change
  • concept learning
  • program induction


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