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<p>Individuals typically employ multiple cognitive strategies rather than relying on a single approach in decision-making scenarios or problem-solving tasks. With recent advancements in measurement technology, the collection of process data has become increasingly common, with response times (RTs) and eye movement fixation counts (FCs) emerging as critical indicators of cognitive processing. Analysis of RTs and FCs can reveal problem-solving strategies that may not be discernible from response patterns alone. To enhance diagnostic accuracy and provide deeper insights into the cognitive processes underlying strategy selection, this study developed a multi-strategy cognitive diagnosis modeling framework that integrates individual RTs and FCs into a unified framework to define strategy selection (MS-CDM-RTFC). The empirical study utilized data from Raven’s Advanced Progressive Matrices (APM), a widely used measure of nonverbal reasoning and fluid intelligence, to evaluate the practical applicability of the MS-CDM-RTFC model. Simulation results based on the empirical analysis indicate that the MS-CDM-RTFC achieves higher parameter recovery and attribute classification accuracy, demonstrating significantly better performance than traditional multi-strategy models.</p>

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A multi-strategy cognitive diagnosis model based on response times and fixation counts

  • 期刊论文

  • 发表日期：

    2026年2月27日

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  • Junhuan Wei,
  • Chun Wang,
  • Yan Cai,
  • Peida Zhan,
  • Dongbo Tu

摘要

Individuals typically employ multiple cognitive strategies rather than relying on a single approach in decision-making scenarios or problem-solving tasks. With recent advancements in measurement technology, the collection of process data has become increasingly common, with response times (RTs) and eye movement fixation counts (FCs) emerging as critical indicators of cognitive processing. Analysis of RTs and FCs can reveal problem-solving strategies that may not be discernible from response patterns alone. To enhance diagnostic accuracy and provide deeper insights into the cognitive processes underlying strategy selection, this study developed a multi-strategy cognitive diagnosis modeling framework that integrates individual RTs and FCs into a unified framework to define strategy selection (MS-CDM-RTFC). The empirical study utilized data from Raven’s Advanced Progressive Matrices (APM), a widely used measure of nonverbal reasoning and fluid intelligence, to evaluate the practical applicability of the MS-CDM-RTFC model. Simulation results based on the empirical analysis indicate that the MS-CDM-RTFC achieves higher parameter recovery and attribute classification accuracy, demonstrating significantly better performance than traditional multi-strategy models.