Mental Number Representations Are Spatially Mapped Both by Their Magnitudes and Ordinal Positions

Collabra Pub Date : 2023-01-01 DOI:10.1525/collabra.67908
Nadine N. Koch, Julia F. Huber, Johannes Lohmann, Krzysztof Cipora, Martin V. Butz, Hans-Christoph Nuerk
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引用次数: 3

Abstract

The Spatial-Numerical Association of Response Codes (SNARC) effect – i.e., faster responses to small numbers with the left compared to the right side and to large numbers with the right compared to the left side – suggests that numbers are associated with space. However, it remains unclear whether the SNARC effect evolves from a number’s magnitude or the ordinal position of a number in working memory. One problem is that, in different paradigms, the task demands influence the role of ordinality and magnitude. While single-task setups in which participants judge the parity of a displayed number indicate the importance of magnitude for the SNARC effect, evidence for ordinal influences usually comes from experiments where ordinal sequences have to be memorized or setups in which participants possess pre-existing knowledge of the ordinality of stimuli. Therefore, in this preregistered study, we employed a SNARC task without secondary ordinal sequence memorization. We dissociate ordinal and magnitude accounts by carefully manipulating experimental stimulus sets. The results indicate that even though the magnitude model better accounts for the observed data, the ordinal position seems to matter as well. Hence, numbers are associated with space in both a magnitude- and an order-respective manner, yielding a mixture of both compatibility effects. Moreover, a multiple coding framework may most accurately explain the roots of the SNARC effect.
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心数表征是由它们的大小和序数位置在空间上映射的
反应代码的空间-数字关联效应(SNARC)表明,数字与空间有关——也就是说,用左手比用右手更快地反应小的数字,用右手比用左手更快地反应大的数字。然而,SNARC效应是由数字的大小还是数字在工作记忆中的顺序位置演变而来,目前还不清楚。一个问题是,在不同的范式中,任务需求的影响作用是普通性和重要性。在单任务设置中,参与者判断所显示的数字的奇偶性表明了SNARC效应的重要性,而序数影响的证据通常来自于必须记住序数序列的实验或参与者对刺激的序数性具有预先存在的知识的实验。因此,在这个预注册的研究中,我们采用了一个没有二次有序序列记忆的SNARC任务。我们通过小心地操纵实验刺激集来分离序数和数量级。结果表明,即使星等模型更好地解释了观测数据,序数位置似乎也很重要。因此,数字以大小和顺序的方式与空间相关联,从而产生两种兼容效果的混合。此外,多重编码框架可能最准确地解释SNARC效应的根源。
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