Zaki Safa R, Kleinschmidt Dave F
Williams College, Williamstown, MA, USA,
Mem Cognit. 2014 Apr;42(3):508-24. doi: 10.3758/s13421-013-0375-9.
According to an influential multiple-systems model of category learning, an implicit procedural system governs the learning of information-integration category structures, whereas a rule-based system governs the learning of explicit rule-based categories. Support for this idea has come in part from demonstrations that motor interference, in the form of inconsistent mapping between response location and category labels, results in observed deficits, but only for learning information-integration category structures. In this article, we argue that this response location manipulation results in a potentially more cognitively complex task in which the feedback is difficult to interpret. In one experiment, we attempted to attenuate the cognitive complexity by providing more information in the feedback, and demonstrated that this eliminates the observed performance deficit for information-integration category structures. In a second experiment, we demonstrated similar interference of the inconsistent mapping manipulation in a rule-based category structure. We claim that task complexity, and not separate systems, might be the source of the original dissociation between performance on rule-based and information-integration tasks.
根据一种有影响力的类别学习多系统模型,一个隐性程序系统支配信息整合类别结构的学习,而一个基于规则的系统支配显性基于规则类别的学习。对这一观点的支持部分来自这样的证明:以反应位置和类别标签之间不一致映射形式存在的运动干扰会导致观察到的缺陷,但仅针对信息整合类别结构的学习。在本文中,我们认为这种反应位置操纵导致了一个潜在认知上更复杂的任务,其中反馈难以解释。在一个实验中,我们试图通过在反馈中提供更多信息来减轻认知复杂性,并证明这消除了信息整合类别结构中观察到的表现缺陷。在第二个实验中,我们在基于规则的类别结构中证明了不一致映射操纵的类似干扰。我们声称,任务复杂性而非单独的系统,可能是基于规则任务和信息整合任务表现最初分离的根源。