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Context-specific proportion congruency effects : an episodic learning account and computational model

机译:特定于上下文的比例一致性效应:一种情节学习帐户和计算模型

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摘要

In the Stroop task, participants identify the print color of color words. The congruency effect is the observation that response times and errors are increased when the word and color are incongruent (e.g., the word "red" in green ink) relative to when they are congruent (e.g., "red" in red). The proportion congruent (PC) effect is the finding that congruency effects are reduced when trials are mostly incongruent rather than mostly congruent. This PC effect can be context-specific. For instance, if trials are mostly incongruent when presented in one location and mostly congruent when presented in another location, the congruency effect is smaller for the former location. Typically, PC effects are interpreted in terms of strategic control of attention in response to conflict, termed conflict adaptation or conflict monitoring. In the present manuscript, however, an episodic learning account is presented for context-specific proportion congruent (CSPC) effects. In particular, it is argued that context-specific contingency learning can explain part of the effect, and context-specific rhythmic responding can explain the rest. Both contingency-based and temporal-based learning can parsimoniously be conceptualized within an episodic learning framework. An adaptation of the Parallel Episodic Processing model is presented. This model successfully simulates CSPC effects, both for contingency-biased and contingency-unbiased (transfer) items. The same fixed-parameter model can explain a range of other findings from the learning, timing, binding, practice, and attentional control domains.
机译:在Stroop任务中,参与者识别颜色词的印刷颜色。一致性效应是观察到单词和颜色不一致时(例如,绿色墨水中的单词“红色”)相对于一致时(例如红色中的“红色”),响应时间和错误会增加。比例一致(PC)效应是当试验大多不一致而不是多数一致时,一致性效应会降低的发现。这种PC效果可能是特定于上下文的。例如,如果在一个位置进行试验时大部分时间不一致,而在另一位置进行试验时大部分时间一致,则对于前一个位置,一致性效果较小。通常,PC效应是根据对冲突的战略控制注意力来解释的,称为冲突适应或冲突监视。但是,在本手稿中,针对情景特定比例一致(CSPC)效果提供了一个情景学习帐户。特别是,有人认为上下文特定的应急学习可以解释部分效果,而上下文特定的节奏响应可以解释其余部分。基于偶然性的学习和基于时间的学习都可以在情节学习框架内简化。提出了一种并行情节加工模型。该模型成功地模拟了偶然性和非偶然性(转移)项目的CSPC效果。相同的固定参数模型可以解释来自学习,时间,约束,练习和注意力控制领域的一系列其他发现。

著录项

  • 作者

    Schmidt, James;

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  • 年度 2016
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  • 原文格式 PDF
  • 正文语种 eng
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