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Evidence accumulation as a model for lexical selection.

R Anders¹, S Riès², L van Maanen³

¹LPC UMR 7290, Aix-Marseille Université, CNRS, France.

Cognitive Psychology

|September 17, 2015

Summary

This summary is machine-generated.

Evidence accumulation offers a new model for lexical selection in language processing. This approach explains how we choose words by accumulating evidence, aligning with psycholinguistic theories and neural network models.

Keywords:

Lexical retrieval Neural network models Psychometrics Response time analysis Sequential sampling Shifted Wald

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Area of Science:

Psycholinguistics
Cognitive Science
Computational Linguistics

Background:

Lexical selection is a key process in language production and comprehension.
Current theories often view lexical selection as choosing among alternatives based on activation levels.
The role of stimulus recognition in initiating lexical selection is widely acknowledged.

Purpose of the Study:

To propose and provide evidence for the evidence accumulation paradigm as a model for lexical selection.
To integrate the evidence accumulation model with existing psycholinguistic theories and computational instantiations.
To demonstrate the empirical utility of evidence accumulation for informing psycholinguistic theory.

Main Methods:

Theoretical exposition of the evidence accumulation paradigm for lexical selection.
Integration of the paradigm with established psycholinguistic theories and neural network models.
Empirical analysis of a large dataset to derive informative parameters.

Main Results:

The evidence accumulation model provides a plausible theoretical framework for lexical selection.
The paradigm can be directly related to and combined with conventional psycholinguistic theories.
Empirical application yielded parameters that inform leading psycholinguistic theories.

Conclusions:

Evidence accumulation is a viable theoretical and empirical model for lexical selection.
This approach offers a bridge between theoretical psycholinguistics and computational modeling.
Findings motivate further theoretical development in understanding word retrieval.