Specificity ratings for English data

Area of Science:

• Cognitive Psychology
• Psycholinguistics
• Lexical Semantics

Background:

• Existing research often examines word properties like concreteness.
• The role of word specificity in cognitive tasks requires further investigation.
• Cross-linguistic comparability of rating data is crucial for broader understanding.

Purpose of the Study:

• To present and analyze a novel dataset of specificity ratings for English words.
• To investigate the contribution of specificity and concreteness to decision latencies in lexical and semantic tasks.
• To facilitate cross-linguistic comparisons by aligning with established datasets.

Main Methods:

• Collected specificity ratings using the best-worst scaling method.
• Utilized words from the Affective Norms for English Words (ANEW) dataset.
• Analyzed data for individual contributions to decision latencies.

Main Results:

• Specificity ratings demonstrate significant explanatory power for decision latencies.
• Specificity and concreteness show independent contributions to task performance.
• The dataset is compatible with existing resources and Italian specificity data.

Conclusions:

• Word specificity is a critical factor in cognitive word processing.
• Future research on concreteness effects should incorporate specificity.
• This dataset enables robust cross-linguistic investigations into word properties.