6 found
Order:
  1.  37
    Linguistic entrenchment: Prior knowledge impacts statistical learning performance.Noam Siegelman, Louisa Bogaerts, Amit Elazar, Joanne Arciuli & Ram Frost - 2018 - Cognition 177 (C):198-213.
    No categories
    Direct download (3 more)  
     
    Export citation  
     
    Bookmark   23 citations  
  2.  46
    Redefining “Learning” in Statistical Learning: What Does an Online Measure Reveal About the Assimilation of Visual Regularities?Noam Siegelman, Louisa Bogaerts, Ofer Kronenfeld & Ram Frost - 2018 - Cognitive Science 42 (S3):692-727.
    From a theoretical perspective, most discussions of statistical learning have focused on the possible “statistical” properties that are the object of learning. Much less attention has been given to defining what “learning” is in the context of “statistical learning.” One major difficulty is that SL research has been monitoring participants’ performance in laboratory settings with a strikingly narrow set of tasks, where learning is typically assessed offline, through a set of two-alternative-forced-choice questions, which follow a brief visual or auditory familiarization (...)
    No categories
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark   16 citations  
  3.  26
    When the “Tabula” is Anything but “Rasa:” What Determines Performance in the Auditory Statistical Learning Task?Amit Elazar, Raquel G. Alhama, Louisa Bogaerts, Noam Siegelman, Cristina Baus & Ram Frost - 2022 - Cognitive Science 46 (2):e13102.
    Cognitive Science, Volume 46, Issue 2, February 2022.
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark   3 citations  
  4.  25
    Individual Differences in Learning Abilities Impact Structure Addition: Better Learners Create More Structured Languages.Tamar Johnson, Noam Siegelman & Inbal Arnon - 2020 - Cognitive Science 44 (8):e12877.
    Over the last decade, iterated learning studies have provided compelling evidence for the claim that linguistic structure can emerge from non‐structured input, through the process of transmission. However, it is unclear whether individuals differ in their tendency to add structure, an issue with implications for understanding who are the agents of change. Here, we identify and test two contrasting predictions: The first sees learning as a pre‐requisite for structure addition, and predicts a positive correlation between learning accuracy and structure addition, (...)
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark   5 citations  
  5.  18
    What exactly is learned in visual statistical learning? Insights from Bayesian modeling.Noam Siegelman, Louisa Bogaerts, Blair C. Armstrong & Ram Frost - 2019 - Cognition 192 (C):104002.
    No categories
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark   5 citations  
  6.  19
    What Determines Visual Statistical Learning Performance? Insights From Information Theory.Noam Siegelman, Louisa Bogaerts & Ram Frost - 2019 - Cognitive Science 43 (12):e12803.
    In order to extract the regularities underlying a continuous sensory input, the individual elements constituting the stream have to be encoded and their transitional probabilities (TPs) should be learned. This suggests that variance in statistical learning (SL) performance reflects efficiency in encoding representations as well as efficiency in detecting their statistical properties. These processes have been taken to be independent and temporally modular, where first, elements in the stream are encoded into internal representations, and then the co‐occurrences between them are (...)
    No categories
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark