识别记忆中的空表强度效应:环境统计和联结主义的解释

S. Dennis
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引用次数: 0

摘要

在识别范例中,增加某些单词在研究列表中的出现次数或呈现时间可以提高对这些单词的识别性能(项目强度效应),但不会影响对其他单词的识别性能(空列表强度效应)。相反,添加新项会导致其他单词的性能下降(列表长度影响)。综上所述,这些结果对识别记忆模型提出了强烈的限制。为了解释这些数据,提出了一个基于环境优化的帐户。对环境分析的总结表明:(1)一个词在上下文中重复出现的可能性随着出现次数的增加而增加;(2)语境中其他词语的重复率对一个词语的重复概率无显著影响;(3)一个单词的重复出现概率作为自该单词最后一次出现以来的单词数的函数而下降。构建了一个拒绝这些约束的训练集,并将其呈现给一个优化的连接网络,该网络旨在提取递归统计(Heb-bian递归网络)。所得到的模型能够模拟上述所有三种情况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Null List Strength Effect in Recognition Memory: Environmental Statistics and Connectionist Accounts
In recognition paradigms, increasing the number of occurrences or presentation time in a study list of some words improves performance on these words (the item strength eeect), but does not aaect the performance on other words (null list strength eeect). In contrast, adding new items results in a deterioration of performance on the other words (list length eeect). Taken together these results place strong constraints on models of recognition memory. To explain these data an account based on optimisation to the environment is presented. A summary is given of environmental analyses which suggest that (1) the likelihood of recurrence of a word within a context increases as the number of occurrences increases; (2) the repetition rates of other words in a context has no signiicant eeect on the recurrence probability of a word; and (3) the recurrence probability of a word drops as a function of the number of words since the last occurrence of that word. A training set which reeected these constraints was constructed and presented to an optimising connectionist network which was designed to extract recurrence statistics (the Heb-bian Recurrent Network). The resultant model is able to model all three of the eeects outlined above.
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