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Related Experiment Video

Updated: Jul 2, 2026

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

Rich data drive generalization: Lessons from machine learning for linguistics and cognitive science.

Andrew Kyle Lampinen¹

¹Google DeepMind, USA lampinen@google.com.

The Behavioral and Brain Sciences

|June 30, 2026

Summary

This summary is machine-generated.

Data richness significantly impacts learning system generalization, even for unrelated variations. This data characteristic differentiates modern language models from older ones, yet may explain their continued linguistic data inefficiency.

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Area of Science:

Natural Language Processing
Machine Learning
Artificial Intelligence

Background:

Learning systems' generalization is sensitive to the diversity of training data.
Variation in data, even along orthogonal axes, can influence performance.
Prior linguistic models differed from current ones in data handling.

Purpose of the Study:

To explore the impact of data richness on learning system generalization.
To differentiate current language models from previous linguistic models based on data characteristics.
To identify potential reasons for remaining linguistic data inefficiency in modern models.

Main Methods:

Analysis of learning system generalization across diverse datasets.
Comparative study of data variation handling in current versus prior linguistic models.
Theoretical argumentation on the role of data richness in model performance.

Main Results:

Data richness is a key factor affecting generalization.
Diversity in training data, including orthogonal variations, is crucial.
Data richness distinguishes current language models (LMs) from prior linguistic models.

Conclusions:

The diversity of variation within training data is critical for robust generalization in learning systems.
Data richness is a defining feature of contemporary language models.
Despite advancements, linguistic data inefficiency in LMs may stem from limitations in data richness exploitation.