Biology subjects

Tenenbaum, J. B.

Publications and source records attributed to Tenenbaum, J. B..

3 recordsLinked to original sources

bioRxiv2018

Learning to act by integrating mental simulations and physical experiments

People can learn about the effects of their actions either by performing physical experiments or by running mental simulations. Physical experiments are reliable but risky; mental simulations are unreliable but safe. We investigate how people negotiate the balance between these strategies. Participants attempted to shoot a ball at a target, and could pay to take practice shots (physical experiments). They could also simply think (run mental simulations), but were incentivized to act quickly by paying for time. We demonstrate that the amount of thinking time and physical experiments is sensitive to trial characteristics in a way that is consistent with a model that integrates information across simulation and experimentation and decides online when to perform each.

animal behavior and cognition↗

bioRxiv2018

Learning list concepts through program induction

Humans master complex systems of interrelated concepts like mathematics and natural language. Previous work suggests learning these systems relies on iteratively and directly revising a language-like conceptual representation. We introduce and assess a novel concept learning paradigm called Marthas Magical Machines that captures complex relationships between concepts. We model human concept learning in this paradigm as a search in the space of term rewriting systems, previously developed as an abstract model of computation. Our model accurately predicts that participants learn some transformations more easily than others and that they learn harder concepts more easily using a bootstrapping curriculum focused on their compositional parts. Our results suggest that term rewriting systems may be a useful model of human conceptual representations.

animal behavior and cognition↗

bioRxiv2016

Compositional Inductive Biases in Function Learning

How do people recognize and learn about complex functional structure? Taking inspiration from other areas of cognitive science, we propose that this is achieved by harnessing compositionality: complex structure is decomposed into simpler building blocks. We formalize this idea within the framework of Bayesian regression using a grammar over Gaussian process kernels, and compare this approach with other structure learning approaches. Participants consistently chose compositional (over non-compositional) extrapolations and interpolations of functions. Experiments designed to elicit priors over functional patterns revealed an inductive bias for compositional structure. Compositional functions were perceived as subjectively more predictable than non-compositional functions, and exhibited other signatures of predictability, such as enhanced memorability and reduced numerosity. Taken together, these results support the view that the human intuitive theory of functions is inherently compositional.

animal behavior and cognition↗