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Moving beyond pure signal-detection models: Comment on Wixted (2007)

机译:超越纯粹的信号检测模型:对Wixted的评论(2007)

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The dual-process signal-detection (DPSD) model assumes that recognition memory is based on recollection of qualitative information or on a signal-detection-based familiarity process. The model has proven useful for understanding results from a wide range of memory research, including behavioral, neuropsychological, electrophysiological, and neuroimaging studies. However, a number of concerns have been raised about the model over the years, and it has been suggested that an unequal-variance signal-detection (UVSD) model that incorporates separate recollection and familiarity processes (J. T. Wixted, 2007) may provide an equally good, or even better, account of the data. In this article, the authors show that the results of studies that differentiate these models support the predictions of the DPSD model and indicate that recognition does not reflect the summing of 2 signal-detection processes, as the new UVSD model assumes. In addition, the assumptions, of the DPSD model are clarified in order to address some of the common misconceptions about the model. Although important challenges remain, hybrid models such as this provide a more useful framework within which to understand human memory than do pure signal-detection models.
机译:双过程信号检测(DPSD)模型假定识别存储器基于对定性信息的收集或基于信号检测的熟悉过程。该模型已被证明对理解广泛的记忆研究结果有用,包括行为,神经心理学,电生理和神经影像研究。然而,这些年来,人们对该模型提出了许多担忧,并且有人提出,将独立的回忆和熟悉过程结合在一起的不等方差信号检测(UVSD)模型(JT Wixted,2007)可以提供同样的效果。更好甚至更好的数据说明。在本文中,作者表明,区分这些模型的研究结果支持DPSD模型的预测,并表明识别不反映新的UVSD模型所假设的2个信号检测过程的总和。另外,阐明了DPSD模型的假设,以解决有关该模型的一些常见误解。尽管仍然存在重要的挑战,但是与纯信号检测模型相比,诸如此类的混合模型提供了一个更有用的框架来理解人类的记忆。

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