Exploring the potential of naive discriminative learning for the analysis of (psycho)linguistic data

Date: 
Monday, December 6, 2010 - 14:00
Location: 
E 201, Prinsstraat 13, 2000 Antwerp
Presenter: 
Harald Baayen

In 1972, Rescorla and Wagner formulated recurrence equations for human and animal learning that have proved to be surprisingly fruitful in psychology.  Danks (2003) introduced a technical innovation that makes it possible to very efficiently estimate the state of the learning system when it is in equilibrium.  In my presentation, I will two present examples demonstrating that Rescorla-Wagner-Danks discriminative learning has much to offer for linguistic and psycholinguistic modelling as well as data analysis.

First, I will introduce a computational model predicting lexical decision latencies for visual comprehension based on naive discriminative learning.  The model is very sparse in free parameters, yet explains a wide range of empirical findings, including whole-word and phrasal frequency effects, without having to posit separate representations for complex words or phrases.  In other words, the model combines excellent predictions with extreme representational parsimony.

Second, I will discuss examples where naive discriminative learning appears to out-perform logistic mixed models fitted to the same data.  Furthermore, naive discriminative learning provides the researcher with sufficient detail to pinpoint a potential weakness of the mixed-effect regression modeling approach.  For the data set examined thus far in this line of research, it seems that naive discriminative learning has potential to be developed into a  statistical tool complementing other classifiers such as logistic and polytomous mixed-effects models, random forests, and nearest-neighbor based methods.

 

Sponsored by CLiF, the scientific research community for Computational Linguistics and Language Technology.

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