1. Home
  2. Browse
  3. Measuring the Semantic Priming Eff…
Preregistration

Measuring the Semantic Priming Effect Across Many Languages

PSA007: Semantic Priming Across Many Languages (SPAML)

Author(s) / Creator(s)

Buchanan, Erin

Other kind(s) of contributor

Psychological Science Accelerator

Abstract / Description

Semantic priming has been studied for nearly 50 years across various experimental manipulations and theoretical frameworks. These studies provide insight into the cognitive underpinnings of semantic representations in both healthy and clinical populations; however, they have suffered from several issues including generally low sample sizes and a lack of diversity in linguistic implementations. Here, we will test the size and the variability of the semantic priming effect across ten languages by creating a large database of semantic priming values, based on an adaptive sampling procedure. Differences in response latencies between related word-pair conditions and unrelated word-pair conditions (i.e., difference score confidence interval is greater than zero) will allow quantifying evidence for semantic priming, whereas improvements in model fit with the addition of a random intercept for language will provide support for variability in semantic priming across languages.

Keyword(s)

semantic priming cognitive psychology psycholinguistics computational linguistics response latencies

Persistent Identifier

PsychArchives acquisition timestamp

2022-07-06 13:44:32 UTC

Publisher

PsychArchives

Citation

Download BibTex
Download as Text
  • Author(s) / Creator(s)
    Buchanan, Erin
  • Other kind(s) of contributor
    Psychological Science Accelerator
    en
  • PsychArchives acquisition timestamp
    2022-07-06T13:44:32Z
  • Made available on
    2022-07-06T13:44:32Z
  • Date of first publication
    2022-07-06
  • Abstract / Description
    Semantic priming has been studied for nearly 50 years across various experimental manipulations and theoretical frameworks. These studies provide insight into the cognitive underpinnings of semantic representations in both healthy and clinical populations; however, they have suffered from several issues including generally low sample sizes and a lack of diversity in linguistic implementations. Here, we will test the size and the variability of the semantic priming effect across ten languages by creating a large database of semantic priming values, based on an adaptive sampling procedure. Differences in response latencies between related word-pair conditions and unrelated word-pair conditions (i.e., difference score confidence interval is greater than zero) will allow quantifying evidence for semantic priming, whereas improvements in model fit with the addition of a random intercept for language will provide support for variability in semantic priming across languages.
    en
  • Publication status
    other
    en
  • Review status
    peerReviewed
    en
  • Table of contents
    Registered Report accepted in principle at Nature Human Behaviour
    en
  • Persistent Identifier
    https://hdl.handle.net/20.500.12034/6375
  • Persistent Identifier
    https://doi.org/10.23668/psycharchives.7074
  • Language of content
    eng
  • Publisher
    PsychArchives
    en
  • Is referenced by
    https://doi.org/10.31219/osf.io/q4fjy
  • Requires
    https://github.com/SemanticPriming/SPAML
  • Is related to
    https://doi.org/10.23668/psycharchives.12555
  • Is related to
    https://doi.org/10.23668/psycharchives.12556
  • Keyword(s)
    semantic priming
    en
  • Keyword(s)
    cognitive psychology
    en
  • Keyword(s)
    psycholinguistics
    en
  • Keyword(s)
    computational linguistics
    en
  • Keyword(s)
    response latencies
    en
  • Dewey Decimal Classification number(s)
    150
  • Title
    Measuring the Semantic Priming Effect Across Many Languages
    en
  • Alternative title
    PSA007: Semantic Priming Across Many Languages (SPAML)
    en
  • DRO type
    preregistration
    en
  • Visible tag(s)
    registered report
    en
  • Visible tag(s)
    PsychLab
    en