Timezone: »
Spotlight
A rational model of preference learning and choice prediction by children
Chris Lucas · Tom Griffiths · Fei Xu · Christine Fawcett
Young children demonstrate the ability to make inferences about the preferences of other agents based on their choices. However, there exists no overarching account of what children are doing when they learn about preferences or how they use that knowledge. We use a rational model of preference learning, drawing on ideas from economics and computer science, to explain the behavior of children in several recent experiments. Specifically, we show how a simple econometric model can be extended to capture two- to four-year-oldsâ use of statistical information in inferring preferences, and their generalization of these preferences.
Author Information
Chris Lucas (UC Berkeley)
Tom Griffiths (Princeton)
Fei Xu (University of California, Berkeley)
Christine Fawcett (Max Planck Institute for Psycholinguistics)
Related Events (a corresponding poster, oral, or spotlight)
-
2008 Poster: A rational model of preference learning and choice prediction by children »
Wed. Dec 10th through Tue the 9th Room
More from the Same Authors
-
2018 : Research Panel »
Sinead Williamson · Barbara Engelhardt · Tom Griffiths · Neil Lawrence · Hanna Wallach -
2017 : Revealing human inductive biases and metacognitive processes with rational models »
Tom Griffiths -
2017 Poster: A graph-theoretic approach to multitasking »
Noga Alon · Daniel Reichman · Igor Shinkar · Tal Wagner · Sebastian Musslick · Jonathan D Cohen · Tom Griffiths · Biswadip dey · Kayhan Ozcimder -
2017 Oral: A graph-theoretic approach to multitasking »
Noga Alon · Daniel Reichman · Igor Shinkar · Tal Wagner · Sebastian Musslick · Jonathan D Cohen · Tom Griffiths · Biswadip dey · Kayhan Ozcimder -
2016 : Bounded Optimality and Rational Metareasoning in Human Cognition »
Tom Griffiths -
2015 Workshop: Bounded Optimality and Rational Metareasoning »
Samuel J Gershman · Falk Lieder · Tom Griffiths · Noah Goodman -
2014 Poster: Algorithm selection by rational metareasoning as a model of human strategy selection »
Falk Lieder · Dillon Plunkett · Jessica B Hamrick · Stuart J Russell · Nicholas Hay · Tom Griffiths -
2013 Poster: Visual Concept Learning: Combining Machine Vision and Bayesian Generalization on Concept Hierarchies »
Yangqing Jia · Joshua T Abbott · Joseph L Austerweil · Tom Griffiths · Trevor Darrell -
2012 Poster: Human memory search as a random walk in a semantic network »
Joshua T Abbott · Joseph L Austerweil · Tom Griffiths -
2012 Spotlight: Human memory search as a random walk in a semantic network »
Joshua T Abbott · Joseph L Austerweil · Tom Griffiths -
2012 Poster: Burn-in, bias, and the rationality of anchoring »
Falk Lieder · Tom Griffiths · Noah Goodman -
2011 Poster: A rational model of causal inference with continuous causes »
M Pacer · Tom Griffiths -
2011 Poster: An ideal observer model for identifying the reference frame of objects »
Joseph L Austerweil · Abram Friesen · Tom Griffiths -
2011 Poster: Testing a Bayesian Measure of Representativeness Using a Large Image Database »
Joshua T Abbott · Katherine Heller · Zoubin Ghahramani · Tom Griffiths -
2010 Workshop: Transfer Learning Via Rich Generative Models. »
Russ Salakhutdinov · Ryan Adams · Josh Tenenbaum · Zoubin Ghahramani · Tom Griffiths -
2010 Spotlight: Learning invariant features using the Transformed Indian Buffet Process »
Joseph L Austerweil · Tom Griffiths -
2010 Poster: Learning invariant features using the Transformed Indian Buffet Process »
Joseph L Austerweil · Tom Griffiths -
2009 Workshop: Bounded-rational analyses of human cognition: Bayesian models, approximate inference, and the brain »
Noah Goodman · Edward Vul · Tom Griffiths · Josh Tenenbaum -
2009 Poster: Neural Implementation of Hierarchical Bayesian Inference by Importance Sampling »
Lei ShiUpdateMe · Tom Griffiths -
2009 Spotlight: Neural Implementation of Hierarchical Bayesian Inference by Importance Sampling »
Lei ShiUpdateMe · Tom Griffiths -
2009 Poster: Differential Use of Implicit Negative Evidence in Generative and Discriminative Language Learning »
Anne Hsu · Tom Griffiths -
2009 Oral: Differential Use of Implicit Negative Evidence in Generative and Discriminative Language Learning »
Anne Hsu · Tom Griffiths -
2009 Poster: Individuation, Identification and Object Discovery »
Charles Kemp · Alan Jern · Fei Xu -
2009 Poster: Nonparametric Latent Feature Models for Link Prediction »
Kurt T Miller · Tom Griffiths · Michael Jordan -
2009 Spotlight: Nonparametric Latent Feature Models for Link Prediction »
Kurt T Miller · Tom Griffiths · Michael Jordan -
2008 Workshop: Machine learning meets human learning »
Nathaniel D Daw · Tom Griffiths · Josh Tenenbaum · Jerry Zhu -
2008 Poster: Modeling the effects of memory on human online sentence processing with particle filters »
Roger Levy · Florencia Reali · Tom Griffiths -
2008 Oral: Modeling the effects of memory on human online sentence processing with particle filters »
Roger Levy · Florencia Reali · Tom Griffiths -
2008 Poster: How memory biases affect information transmission: A rational analysis of serial reproduction »
Jing Xu · Tom Griffiths -
2008 Poster: Analyzing human feature learning as nonparametric Bayesian inference »
Joseph L Austerweil · Tom Griffiths -
2008 Spotlight: Analyzing human feature learning as nonparametric Bayesian inference »
Joseph L Austerweil · Tom Griffiths -
2008 Spotlight: How memory biases affect information transmission: A rational analysis of serial reproduction »
Jing Xu · Tom Griffiths -
2008 Poster: An ideal observer model of infant object perception »
Charles Kemp · Fei Xu -
2008 Poster: Modeling human function learning with Gaussian processes »
Tom Griffiths · Chris Lucas · Joseph Jay Williams · Michael Kalish -
2007 Oral: Markov Chain Monte Carlo with People »
Adam Sanborn · Tom Griffiths -
2007 Poster: Markov Chain Monte Carlo with People »
Adam Sanborn · Tom Griffiths -
2007 Poster: A Probabilistic Approach to Language Change »
Alexandre Bouchard-Côté · Percy Liang · Tom Griffiths · Dan Klein -
2006 Poster: Particle Filtering for Nonparametric Bayesian Matrix Factorization »
Frank Wood · Tom Griffiths -
2006 Poster: Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Mod »
Mark Johnson · Tom Griffiths · Sharon Goldwater -
2006 Poster: A Nonparametric Bayesian Method for Inferring Features From Similarity Judgments »
Daniel Navarro · Tom Griffiths