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Language experience predicts semantic priming of lexical decision.

Harinder Aujla¹

¹Department of Psychology, University of Winnipeg.

Canadian Journal of Experimental Psychology = Revue Canadienne De Psychologie Experimentale

|May 6, 2021

Summary

This study introduces a novel method for creating personalized semantic memory models using individual news consumption data. These corpus-specific models accurately predict how news readership influences cognitive processes like semantic priming.

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Area of Science:

Cognitive Science
Computational Linguistics
Psychology

Background:

Computational models of semantic memory typically use generalized language corpora.
This approach limits the ability to model individual differences in semantic representations.
Previous research has not validated corpus-specific predictions for semantic memory.

Purpose of the Study:

To develop and validate a method for generating corpus-specific semantic representations.
To investigate if individual news readership influences semantic representations.
To test if these representations predict differential priming effects.

Main Methods:

Utilized internet news sites as corpora to generate personalized semantic representations.
Recruited subjects based on their specific news site readership.
Administered lexical decision tasks to measure priming effects.

Main Results:

Identified word similarities that are specific to particular news sites.
Demonstrated that news site-specific semantic representations successfully predict priming effects.
Showed that differential priming effects correlate with news readership.

Conclusions:

Corpus-specific semantic representations are feasible and valuable.
Individual language experiences, reflected in news consumption, shape semantic memory.
This approach offers a pathway to more personalized cognitive modeling.