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

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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