{"title":"Erratum for “Chunking Versus Transitional Probabilities: Differentiating Between Theories of Statistical Learning”","authors":"","doi":"10.1111/cogs.13472","DOIUrl":null,"url":null,"abstract":"<p>Emerson, S. N. & Conway, C. M. (2023). Chunking versus transitional probabilities: Differentiating between theories of statistical learning. <i>Cognitive Science</i>, <i>47</i>(5), e13284. https://doi.org/10.1111/cogs.13284</p><p>Pre-Registration section lists an incorrect website for the project data. Data for the study can be found at https://osf.io/tnzky or through the full project page at https://osf.io/dr4ec.</p><p>We apologize for the oversight.</p>","PeriodicalId":48349,"journal":{"name":"Cognitive Science","volume":"48 6","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cogs.13472","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cognitive Science","FirstCategoryId":"102","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/cogs.13472","RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PSYCHOLOGY, EXPERIMENTAL","Score":null,"Total":0}
引用次数: 0
Abstract
Emerson, S. N. & Conway, C. M. (2023). Chunking versus transitional probabilities: Differentiating between theories of statistical learning. Cognitive Science, 47(5), e13284. https://doi.org/10.1111/cogs.13284
Pre-Registration section lists an incorrect website for the project data. Data for the study can be found at https://osf.io/tnzky or through the full project page at https://osf.io/dr4ec.
Emerson, S. N. & Conway, C. M. (2023).分块概率与过渡概率：统计学习理论的区别。Cognitive Science, 47(5), e13284. https://doi.org/10.1111/cogs.13284Pre-Registration 部分列出的项目数据网站不正确。该研究的数据可在 https://osf.io/tnzky 或通过 https://osf.io/dr4ec.We 的完整项目页面找到，对这一疏忽深表歉意。
期刊介绍：
Cognitive Science publishes articles in all areas of cognitive science, covering such topics as knowledge representation, inference, memory processes, learning, problem solving, planning, perception, natural language understanding, connectionism, brain theory, motor control, intentional systems, and other areas of interdisciplinary concern. Highest priority is given to research reports that are specifically written for a multidisciplinary audience. The audience is primarily researchers in cognitive science and its associated fields, including anthropologists, education researchers, psychologists, philosophers, linguists, computer scientists, neuroscientists, and roboticists.
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