Scholarlink

<p>This study examined the Weibull model for individual learning curves in Pavlovian conditioning from a theoretical perspective. Although the Weibull model effectively captures variability in individual learning, it remains unclear what the parameters in this model represent. First, we identified similarities in the mathematical representations of the Weibull and error-correction models and showed that two of the three parameters in the Weibull model can be interpreted in the same manner as those in the error-correction model. Next, to explore possible psychological interpretations of the remaining parameter, <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\alpha\)</EquationSource> <EquationSource Format="MATHML"><math> <mi>α</mi> </math></EquationSource> </InlineEquation>, and to examine how <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\alpha\)</EquationSource> <EquationSource Format="MATHML"><math> <mi>α</mi> </math></EquationSource> </InlineEquation> is distributed across individual learning curves, we analyzed four fear-conditioning datasets obtained from both humans and animals. Although it was difficult to establish a definitive interpretation of <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\alpha\)</EquationSource> <EquationSource Format="MATHML"><math> <mi>α</mi> </math></EquationSource> </InlineEquation> in this paper, we discuss possible variables that may be associated with <InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(\alpha\)</EquationSource> <EquationSource Format="MATHML"><math> <mi>α</mi> </math></EquationSource> </InlineEquation>.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Interpretation of Weibull model for individual learning curves in Pavlovian conditioning

  • 期刊论文

  • 发表日期：

    2026年7月20日

查看全文
  • Masato Nihei,
  • Noboru Matsumoto,
  • Daiki Hojo,
  • Kosuke Sawa

摘要

This study examined the Weibull model for individual learning curves in Pavlovian conditioning from a theoretical perspective. Although the Weibull model effectively captures variability in individual learning, it remains unclear what the parameters in this model represent. First, we identified similarities in the mathematical representations of the Weibull and error-correction models and showed that two of the three parameters in the Weibull model can be interpreted in the same manner as those in the error-correction model. Next, to explore possible psychological interpretations of the remaining parameter, \(\alpha\) α , and to examine how \(\alpha\) α is distributed across individual learning curves, we analyzed four fear-conditioning datasets obtained from both humans and animals. Although it was difficult to establish a definitive interpretation of \(\alpha\) α in this paper, we discuss possible variables that may be associated with \(\alpha\) α .