Open Access
A rational model of function learning
Thomas L. Griffiths,Michael L. Kalish,Chris Lucas +2 more
- 01 Jan 2009
Vol. 31, Iss: 31
TL;DR: In this paper, the authors present a rational model that transparently identifies the inductive biases that a process model should seek to capture, and they find that it explains several phenomena, including knowledge partitioning and iterated learning data.
read more
Abstract: A rational model of function learning Christopher Lucas UC Berkeley Thomas Griffiths UC Berkeley Michael Kalish University of Lafayette Abstract: People often face the problem of learning what value a variable will take, given information about the values of other variables. Categorization and causal prediction are special cases, each the subject of extensive research dealing exclusively with discrete variables. With continuous variables, this problem is known as function learning. Most function learning research has been concerned with specifying representations and processes by which people understand the functional relationship between pairs of continuous variables. In contrast, we present a rational model that transparently identifies the inductive biases that a process model should seek to capture. The foundation of our approach is an infinite mixture of Gaussian process experts. It extends our previous Gaussian process model, which outperforms several well-known alternatives and has been shown to be a generalization of both associative and rule-based (i.e., regression-like) function-learning models. We find that it explains several phenomena, including knowledge partitioning and iterated learning data.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
A tutorial on Gaussian process regression: Modelling, exploring, and exploiting functions
TL;DR: This tutorial introduces the reader to Gaussian process regression as an expressive tool to model, actively explore and exploit unknown functions and describes a situation modelling risk-averse exploration in which an additional constraint needs to be accounted for.
1.1K
Generalization guides human exploration in vast decision spaces
Charley M. Wu,Eric Schulz,Maarten Speekenbrink,Jonathan D. Nelson,Jonathan D. Nelson,Björn Meder +5 more
TL;DR: Modelling how humans search for rewards under limited search horizons finds evidence that Gaussian process function learning—combined with an optimistic upper confidence bound sampling strategy—provides a robust account of how people use generalization to guide search.
Interactive machine learning: experimental evidence for the human in the algorithmic loop
Andreas Holzinger,Markus Plass,Michael D. Kickmeier-Rust,Katharina Holzinger,Gloria Cerasela Crisan,Camelia-Mihaela Pintea,Vasile Palade +6 more
TL;DR: This paper provides new experimental insights on how to improve computational intelligence by complementing it with human intelligence in an interactive machine learning approach (iML).
The algorithmic architecture of exploration in the human brain
Eric Schulz,Samuel J. Gershman +1 more
TL;DR: This work reviews recent studies that have identified multiple algorithmic strategies underlying exploration, which rely on different brain systems, have different developmental trajectories, and are sensitive to different task manipulations.
182
References
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Gaussian processes in machine learning
TL;DR: In this paper, the authors give a basic introduction to Gaussian Process regression models and present the simple equations for incorporating training data and examine how to learn the hyperparameters using the marginal likelihood.
Markov Chain Monte Carlo in Practice
TL;DR: The Markov Chain Monte Carlo Implementation Results Summary and Discussion MEDICAL MONITORING Introduction Modelling Medical Monitoring Computing Posterior Distributions Forecasting Model Criticism Illustrative Application Discussion MCMC for NONLINEAR HIERARCHICAL MODELS.
Adaptive mixtures of local experts
TL;DR: A new supervised learning procedure for systems composed of many separate networks, each of which learns to handle a subset of the complete set of training cases, which is demonstrated to be able to be solved by a very simple expert network.
5.2K
Markov Chain Sampling Methods for Dirichlet Process Mixture Models
TL;DR: In this article, Markov chain methods for sampling from the posterior distribution of a Dirichlet process mixture model are presented, and two new classes of methods are presented. But neither of these methods is suitable for handling general models with non-conjugate priors.
2.6K