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Frequency effects in linear discriminative learning.

Maria Heitmeier1,2, Yu-Ying Chuang1, Seth D Axen2

  • 1Quantitative Linguistics, University of Tübingen, Tübingen, Germany.

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Summary
This summary is machine-generated.

This study introduces Frequency-Informed Learning (FIL), an efficient method to model word frequency effects in lexical processing. FIL accurately predicts reaction times and priming effects, offering a computationally cheaper alternative to incremental learning.

Keywords:
distributional semanticsincremental learninglexical decisionlinear discriminative learningmental lexiconweighted regressionword frequency

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Area of Science:

  • Cognitive Science
  • Psycholinguistics
  • Computational Linguistics

Background:

  • Word frequency significantly impacts lexical processing and word recognition models.
  • Existing models like the Discriminative Lexicon Model (DLM) struggle to efficiently incorporate frequency effects.
  • Current solutions include computationally expensive incremental learning or frequency-agnostic end-state learning (EL).

Purpose of the Study:

  • To develop an efficient and frequency-informed learning method for lexical processing models.
  • To evaluate the performance of this new method (Frequency-Informed Learning; FIL) against existing approaches.
  • To investigate how FIL accounts for frequency effects in reaction times and priming.

Main Methods:

  • Developed and implemented the Frequency-Informed Learning (FIL) algorithm.
  • Modeled reaction times using the Dutch Lexicon Project data with a Gaussian Location Scale Model.
  • Analyzed priming effects in an auditory lexical decision task with Mandarin Chinese data.
  • Compared FIL mappings with incremental learning using CHILDES data.

Main Results:

  • FIL provides an efficient approximation of incremental learning, significantly reducing computational cost.
  • FIL demonstrates high token-accuracy, effectively processing common words.
  • FIL accurately predicts the S-shaped frequency-reaction time relationship but underestimates variance for low-frequency words.
  • FIL better explains priming effects than EL and shows high correlation with incremental learning mappings, though some word ordering nuances are lost.

Conclusions:

  • Frequency-Informed Learning (FIL) offers an efficient way to simulate frequency effects in cognitive models of word recognition.
  • FIL presents a viable, computationally cheaper alternative to incremental learning for modeling lexical processing.
  • Further research is needed to refine the modeling of low-frequency words and word ordering effects within cognitive models.