Teaching Recombinable Motifs Through Simple Examples

Dados Bibliográficos

ID7150340
AutoresHuang Ham (0009-0007-8630-3350, Department of Psychology Princeton University), Bonan Zhao (0000-0002-5667-1722, Institute for Language, Cognition and Computation, School of Informatics University of Edinburgh), Thomas L Griffiths (0000-0002-5138-7255, Department of Psychology Princeton University), Natalia Vélez (0000-0002-6939-3282, Department of Psychology Princeton University, autor correspondente)
Ano2025
Volume49
Fascículo8
Páginase70103-e70103
Data de publicação2025-08-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoCognitive Science (JOURNAL)
Identificadores do periódicoISSN: 0364-0213 • E-ISSN: 1551-6709
EditoraWiley (PUBLISHER • GB)
DOI10.1111/cogs.70103
PMID40810254
OpenAlexW4413114377
IdiomaEN
Referências citadas40

A hallmark of effective teaching is that it grants learners not just a collection of facts about the world, but also a toolkit of abstractions that can be applied to solve new problems. How do humans teach abstractions from examples? Here, we applied Bayesian models of pedagogy to a necklace‐building task where teachers create necklaces to teach a learner “motifs” that can be flexibly recombined to create new necklaces. In Experiment 1 ( N = 151), we find that human teachers produce necklaces that are simpler (i.e., have lower algorithmic complexity) than would be expected by chance, as indexed by a model that samples uniformly from all necklaces that contain the target motifs. This tendency to select simpler examples is captured by a pedagogical sampling model that tries to maximize the learner's belief in the true motifs by prioritizing examples that have fewer alternative interpretations. In Experiment 2 ( N = 295), we find that simplicity is beneficial. Human learners recover the underlying motifs better when teachers produce simpler sequences, as predicted by the pedagogical sampling model. However, humans learn best from human teachers rather than from model‐generated examples, which suggests that human teachers have additional expectations about how learners will interpret examples that are not captured by standard models of teaching. Our work provides a principled framework to understand when and why teachers use simple examples to convey abstract knowledge

Epistemology · Computer Science · Human Motion and Animation · Intelligent Tutoring Systems and Adaptive Learning · Philosophy · Visual and Cognitive Learning Processes · Artificial Intelligence