Models of retrieval in sentence comprehension: A computational evaluation using Bayesian hierarchical modeling
Models of retrieval in sentence comprehension: A computational evaluation using Bayesian hierarchical modeling
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DOI：
10.1016/j.jml.2017.08.004
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发表时间：
2018-04-01
影响因子：
4.3
通讯作者：
Vasishth, Shravan
中科院分区：
文献类型：
--
作者：
Nicenboim, Bruno;Vasishth, Shravan
对基于相似性的干扰的研究已经提供了广泛的证据表明，不相邻单词之间的依存关系的形成依赖于基于线索的检索机制。有两种不同的模型可以解释干扰的主要预测之一，即当几个项目共享与提取线索相关的特征时，提取站点的减速：Lewis和Vasishth(2005)的基于激活的模型和McElree(2000)的直接通达模型。尽管这两个模型几乎是互换使用的，但它们基于不同的假设，并预测阅读时间和回答准确性之间的关系存在差异。基于激活的模型遵循ACT-R框架的假设，其检索过程表现为具有单一方差的证据累加器之间的对数正态竞争。在该模型下，检索的准确性取决于比赛的获胜者，检索时间取决于比赛的累积率。相比之下，直接通达模型假设了一个记忆模型，其中只有检索概率会受到影响，而检索时间是从相同的分布中提取的；在这个模型中，延迟的差异是回溯和修复错误检索的可能性的副产品。我们在贝叶斯分层框架中实现了这两个模型，以便对它们进行评估和比较。数据显示，正确的提取比不正确的提取花费的时间更长，而且这种模式在直接访问模式下比在基于激活的模式下更适合。这一发现不排除检索可能表现为一个种族模型，其假设与ACT-R框架中的假设不那么接近。通过引入激活模型的修改，即通过假设用于提取错误物品的证据的积累不仅更慢而且更嘈杂(即，正确和错误物品的不同方差)，该模型可以提供与直接访问模型一样好的匹配。这是有史以来第一次对句子处理中提取过程的不同描述进行计算评估，为更广泛地研究依存关系完成理论开辟了道路。(C)2017 Elsevier Inc.保留所有权利。
Research on similarity-based interference has provided extensive evidence that the formation of dependencies between non-adjacent words relies on a cue-based retrieval mechanism. There are two different models that can account for one of the main predictions of interference, i.e., a slowdown at a retrieval site, when several items share a feature associated with a retrieval cue: Lewis and Vasishth's (2005) activation-based model and McElree's (2000) direct-access model. Even though these two models have been used almost interchangeably, they are based on different assumptions and predict differences in the relationship between reading times and response accuracy. The activation-based model follows the assumptions of the ACT-R framework, and its retrieval process behaves as a lognormal race between accumulators of evidence with a single variance. Under this model, accuracy of the retrieval is determined by the winner of the race and retrieval time by its rate of accumulation. In contrast, the direct access model assumes a model of memory where only the probability of retrieval can be affected, while the retrieval time is drawn from the same distribution; in this model, differences in latencies are a by-product of the possibility of backtracking and repairing incorrect retrievals. We implemented both models in a Bayesian hierarchical framework in order to evaluate them and compare them. The data show that correct retrievals take longer than incorrect ones, and this pattern is better fit under the direct access model than under the activation-based model. This finding does not rule out the possibility that retrieval may be behaving as a race model with assumptions that follow less closely the ones from the ACT-R framework. By introducing a modification of the activation model, i.e., by assuming that the accumulation of evidence for retrieval of incorrect items is not only slower but noisier (i.e., different variances for the correct and incorrect items), the model can provide a fit as good as the one of the direct-access model. This first ever computational evaluation of alternative accounts of retrieval processes in sentence processing opens the way for a broader investigation of theories of dependency completion. (C) 2017 Elsevier Inc. All rights reserved.