Using experiential optimization to build lexical representations.

Area of Science:

• Cognitive Science
• Computational Linguistics
• Artificial Intelligence

Background:

• Standard models optimize parameters to fit behavioral data, but often ignore variability from individual experience.
• Language processing theories recognize experience's importance, yet this is typically excluded from model evaluation.
• Existing methods fail to capture the full spectrum of knowledge influencing cognitive tasks.

Purpose of the Study:

• To introduce and validate a novel method, experiential optimization, for enhancing language model performance.
• To integrate the role of experience directly into the model fitting process.
• To improve the evaluation of models by accounting for variability in learned knowledge.

Main Methods:

• Experiential optimization was employed to fit language-based behavioral data.
• This method optimizes the input materials (linguistic sources) provided to the model.
• Model learning and processing mechanisms were retained while optimizing the training data.
• Optimal linguistic sources were selected to build a memory representation that maximizes task performance.

Main Results:

• The experiential optimization method was demonstrated on multiple benchmark tasks.
• Models optimized with experiential data showed improved performance.
• The approach successfully integrated experience-based variability into model fitting.
• Task performance was maximized by selecting optimal linguistic experiences.

Conclusions:

• Experiential optimization offers a more robust approach to modeling cognitive processes by incorporating experience.
• This method enhances the fit of computational models to behavioral data.
• Future research should explore experiential optimization across diverse cognitive domains.
• Accounting for experience is critical for developing more accurate and representative cognitive models.