Strategy execution in cognitive skill learning: an item-level test of candidate models.
Strategy execution in cognitive skill learning: an item-level test of candidate models.
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认知技能学习中的策略执行：候选模型的项目级测试。
DOI：
10.1037/0278-7393.30.1.65
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发表时间：
2004
期刊：
影响因子：
--
通讯作者：
Rickard,TimothyC
中科院分区：
文献类型：
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作者：
Rickard,TimothyC
本文研究了最初需要使用多步算法的任务在实践中向基于内存的性能的转变。在字母算术任务中，经过适度练习后，项目反应时间呈现出明显的阶梯函数式减少，这是由TC Rickard（1997）的分量幂律模型唯一预测的。研究结果挑战了迄今为止发展起来的并行策略执行模型，它们证明了向检索的转变是一种特定于项目的学习现象，而不是一般任务的学习现象。研究结果还对整个平滑加速函数类作为全局经验学习定律的问题提出了质疑。结果表明，将平均项目拟合叠加在平均数据上，可以为模型充分性提供一个灵敏的检验。策略探测与基于阶跃函数加速模式的策略推断一致，支持了探测技术的有效性。
This article investigates the transition to memory-based performance that commonly occurs with practice on tasks that initially require use of a multistep algorithm. In an alphabet arithmetic task, item response times exhibited pronounced step-function decreases after moderate practice that were uniquely predicted by TC Rickard’s (1997) component power laws model. The results challenge parallel strategy execution models as developed to date and they demonstrate that the shift to retrieval is an item-specific, as opposed to task-general, learning phenomenon. The results also call into question the entire class of smooth speed-up functions as global empirical learning laws. It is shown that overlaying of averaged item fits on averaged data can provide a sensitive test for model sufficiency. Strategy probes agreed with strategy inferences that were based on step-function speed-up patterns, supporting the validity of the probing technique.