Language experience predicts semantic priming of lexical decision.

Computational models of semantic memory have been successful in accounting for a wide range of cognitive phenomena, including word categorization, semantic priming, and release from proactive interference. Conventionally, the texts input to these models have been curated to represent the average individual's language experience. While this approach has proven successful for making predictions that generalize across individuals, it prevents consideration of situations in which individuals have divergent semantic representations. The use of a representative corpus prevents the generation of predictions specific to the language experience of an individual. While this limitation has been discussed in the literature, previous investigations have not yet validated such corpus-specific predictions. I present an approach to generate corpus-specific semantic representations using internet news sites as corpora. I then validate the semantic representations against subjects that read specific news sites. Results demonstrate that similarities between news sites are specific to the words under consideration and that news site-specific representations successfully predict differential priming effects in lexical decision as a function of news readership. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

• Publication year

  2021

• Venue

  Canadian journal of experimental psychology = Revue canadienne de psychologie experimentale

• Publication date

  2021-05-06

• Fields of study

  Medicine, Linguistics, Psychology

• Identifiers
  DOI 10.1037/cep0000255PMID 33956462
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

• No references are available for this paper.

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