Forced-choice associative recognition: implications for global-memory models
Forced-choice associative recognition: implications for global-memory models
复制标题
强制选择联想识别：对全局记忆模型的影响
DOI：
10.1037/0278-7393.19.4.871
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发表时间：
1993
期刊：
影响因子：
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通讯作者：
D. Callan
中科院分区：
文献类型：
--
作者：
S. Clark;A. Hori;D. Callan
联想识别要求受试者区分完整的和重新排列的测试对。在三选项强制选择程序中，一个完整的测试对对两个重新排列的干扰物进行测试，这些干扰物可能重叠，通过在每个测试对选项中共享一个共同的单词（OLAP），或可能不共享单词（NOLAP）。除了Murdock（1982）的分布式联想记忆理论（TODAM）外，当前的全局匹配模型预测，OLAP的强制选择性能将优于NOLAP测试试验。TODAM既可以预测OLAP的优势，也可以预测OLAP和NOLAP测试条件之间没有差异。模型的性能是由基本统计属性决定的，除了TODAM之外，OLAP的优势不能通过改变参数来消除
Associative recognition requires subjects to discriminate intact from rearranged test pairs. In a 3-alternative forced-choice procedure, an intact test pair is tested against 2 rearranged distractors which may overlap, by sharing a common word in each test pair alternative (OLAP), or may not share words (NOLAP). With the exception of Murdock's (1982) theory of distributed associative memory (TODAM), current global matching models predict that forced-choice performance will be better for OLAP than for NOLAP test trials. TODAM can predict either an OLAP advantage or no difference between OLAP and NOLAP test conditions. The performance of the models is produced by fundamental statistical properties, and with the exception of TODAM, the OLAP advantage cannot be eliminated by varying parameters