Liverpool John Moores University, Liverpool, L33AF, UK.
University of Keele, Staffordshire, ST5 5BG, UK.
Psychol Res. 2021 Nov;85(8):3048-3060. doi: 10.1007/s00426-020-01456-4. Epub 2020 Dec 17.
Performance similarities on tasks requiring the processing of different domains of magnitude (e.g. time, numerosity, and length) have led to the suggestion that humans possess a common processing system for all domains of magnitude (Bueti and Walsh in Philos Trans R Soc B 364:1831-1840, 2009). In light of this, the current study examined whether Wearden's (Timing Time Percept 3:223-245, 2015) model of the verbal estimation of duration could be applied to verbal estimates of numerosity and length. Students (n = 23) verbally estimated the duration, number, or physical length of items presented in visual displays. Analysis of the mean verbal estimates indicated the data were typical of that found in other studies. Analysis of the frequency of individual verbal estimates produced suggested that the verbal responses were highly quantized for duration and length: that is, only a small number of estimates were used. Responses were also quantized for number but to a lesser degree. The data were modelled using Wearden's (2015) account of verbal estimation performance, which simulates quantization effects, and good fits could be obtained providing that stimulus durations were scaled as proportions (0.75, 1.06, and 0.92 for duration, number, and length, respectively) of their real magnitudes. The results suggest that despite previous reports of similarities in the processing of magnitude, there appear to be differences in the way in which the underlying representations of the magnitudes are scaled and then transformed into verbal outputs.
在需要处理不同数量领域(例如时间、数量和长度)的任务上的表现相似性,导致人们提出了这样的假设,即人类拥有一个用于所有数量领域的通用处理系统(Bueti 和 Walsh 在 Philos Trans R Soc B 364:1831-1840, 2009)。有鉴于此,本研究考察了 Wearden(Timing Time Percept 3:223-245, 2015)关于口头估计持续时间的模型是否可以应用于口头估计数量和长度。学生(n=23)口头估计视觉显示中呈现的项目的持续时间、数量或物理长度。对平均口头估计值的分析表明,数据与其他研究中发现的数据典型相似。对个别口头估计值的频率分析表明,口头反应在持续时间和长度上高度量化:即,只使用了少数估计值。对于数量,反应也量化了,但程度较小。数据使用 Wearden(2015)的口头估计表现模型进行了建模,该模型模拟了量化效应,可以获得很好的拟合,只要刺激持续时间被缩放到它们实际大小的比例(分别为 0.75、1.06 和 0.92 用于持续时间、数量和长度)。结果表明,尽管先前有关于数量处理相似性的报告,但在将数量的基础表示进行缩放然后转换为口头输出的方式似乎存在差异。