Timezone: »
A classic debate in cognitive science revolves around understanding how children learn complex linguistic rules, such as those governing restrictions on verb alternations, without negative evidence. Traditionally, formal learnability arguments have been used to claim that such learning is impossible without the aid of innate languagespecific knowledge. However, recently, researchers have shown that statistical models are capable of learning complex rules from only positive evidence. These two kinds of learnability analyses differ in their assumptions about the role of the distribution from which linguistic input is generated. The former analyses assume that learners seek to identify grammatical sentences in a way that is robust to the distribution from which the sentences are generated, analogous to discriminative approaches in machine learning. The latter assume that learners are trying to estimate a generative model, with sentences being sampled from that model. We show that these two learning approaches differ in their use of implicit negative evidence  the absence of a sentence  when learning verb alternations, and demonstrate that human learners can produce results consistent with the predictions of both approaches, depending on the context in which the learning problem is presented.
Author Information
Anne Hsu (UC Berkeley)
Tom Griffiths (Princeton)
More from the Same Authors

2017 Poster: A graphtheoretic 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 graphtheoretic approach to multitasking »
Noga Alon · Daniel Reichman · Igor Shinkar · Tal Wagner · Sebastian Musslick · Jonathan D Cohen · Tom Griffiths · Biswadip dey · Kayhan Ozcimder 
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: Burnin, 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: Boundedrational 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 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 Poster: A rational model of preference learning and choice prediction by children »
Chris Lucas · Tom Griffiths · Fei Xu · Christine Fawcett 
2008 Spotlight: Analyzing human feature learning as nonparametric Bayesian inference »
Joseph L Austerweil · Tom Griffiths 
2008 Spotlight: A rational model of preference learning and choice prediction by children »
Chris Lucas · Tom Griffiths · Fei Xu · Christine Fawcett 
2008 Spotlight: How memory biases affect information transmission: A rational analysis of serial reproduction »
Jing Xu · Tom Griffiths 
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 BouchardCôté · 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