Conflict Paradigms and the Instance Theory of Automatization

本研究将探讨注意在控制和克服不相关信息自动加工所产生的冲突中的作用。在现实世界中，当我们当前的目标与既定的习惯背道而驰时，自动处理带来的冲突是很明显的，比如离开通常的回家路线去商店。Stroop任务提供了一个实验室的例子：人们被要求说出单词呈现的颜色，并且必须克服当单词本身表示冲突的颜色时的自动阅读反应（例如，红色写在绿色中）。Stroop任务的表现及其许多变体（例如，决定3 + 4 = 12是否为真）为注意力和自动性理论提供了必须解释的主要数据库。本研究将把洛根的自动性实例理论应用于冲突范式，该理论是为解释技能习得而发展起来的。实例理论假设新手的表现是基于执行任务的通用算法，并且随着实践，算法逐渐被特定解决方案的记忆所取代。这个学习过程在理论中以精确的数学方式建模。实例理论比其他技能习得理论更能解释学习，因为它解释了整个反应时间分布的变化，而不仅仅是平均表现。就目前而言，实例理论在冲突范式中做出了不正确的预测，预测反应时间不会减慢，错误率接近100%，而大量实验显示反应时间大幅减慢，错误率略有增加。本研究将发展理论来克服这一问题，然后测试新的发展。理论发展将涉及假设两种不同的表征，一种知觉表征和一种概念表征，它们通过注意过程联系在一起。这项研究将包括三个系列的实验。第一部分将研究将知觉表征与概念表征结合起来的过程；第二种是区分知觉表征和概念表征；第三章将通过考察学习效应来考察这种结合过程的长期后果。本研究将利用计算机模拟学习过程，对绑定过程进行理论研究，并将实例理论与目前其他技能习得理论进行比较。

This research will investigate the role of attention in controlling and overcoming conflict engendered by automatic processing of irrelevant information. Conflict from automatic processing is apparent in real-world situations in which our current goals run counter to established habits, such as departing from the usual route home to stop at the store. The Stroop task provides a laboratory example: People are asked to name the color in which words are presented and must overcome the automatic reading response when the word itself names a conflicting color (e.g., RED written in green). Performance on the Stroop task and its many variations (e.g., deciding whether 3 + 4 = 12 is true) provides a major data base which theories of attention and automaticity must explain. This research will apply Logan's instance theory of automaticity, developed to account for skill acquisition, to conflict paradigms. The instance theory assumes that novice performance is based on a general algorithm for performing the task and that, with practice, the algorithm is gradually replaced by memory for specific solutions. This learning process is modeled in the theory in a precise mathematical fashion. The instance theory accounts for learning better than other theories of skill acquisition because it accounts for changes in the entire distribution of reaction times, not just average performance. As it stands, the instance theory makes incorrect predictions in conflict paradigms, predicting no slowing of reaction time and an error rate close to 100%, in contrast to vast numbers of experiments which show a substantial slowing in reaction time and a slight increase in error rate. This research will develop the theory to overcome this problem and then test the new developments. Theory development will involve assuming two different representations, a perceptual representation and a conceptual representation, that are bound together by the process of attention. The research will involve three series of experiments. The first will examine the process that binds perceptual representations to conceptual representations; the second will separate perceptual and conceptual representations; and the third will examine long- term consequences of the binding process by examining learning effects. The research will investigate the binding process theoretically using computer simulations of the learning process that compare the instance theory with other current theories of skill acquisition.