Dutch norm data for 13 semantic categories and 338 exemplars

Area of Science:

• Cognitive Psychology
• Psycholinguistics
• Natural Language Processing

Background:

• Understanding semantic categories is crucial for cognitive science.
• Existing datasets often lack comprehensive feature and association data.
• Cross-linguistic data is essential for generalizable cognitive models.

Purpose of the Study:

• To create a rich, norm-based dataset for 13 common Dutch superordinate categories.
• To provide feature generation frequencies, typicality, similarity, and acquisition data for category exemplars.
• To facilitate research in semantic memory, language acquisition, and computational linguistics.

Main Methods:

• Collected data on 338 Dutch exemplars across 13 superordinate categories (animals, artifacts, etc.).
• Gathered feature generation frequencies, typicality, similarity, age-of-acquisition, and word associations for each exemplar.
• Utilized exemplar and feature generation tasks, alongside frequency and association data collection.

Main Results:

• A structured dataset with eight variables for 13 natural language categories and 338 exemplars.
• Detailed norms including feature frequencies, typicality, similarity, and age-of-acquisition for Dutch words.
• Reliability estimates and additional measures are provided for the collected data.

Conclusions:

• The dataset offers a valuable resource for studying semantic representation and language processing in Dutch.
• Researchers can leverage these norms to test cognitive theories and develop computational models.
• The availability of this data in Excel format promotes accessibility and further research.