Université de Paris-Cité, LLF, CNRS, Paris, France.
Leiden University, Leiden, Netherlands.
PLoS One. 2022 Apr 28;17(4):e0266920. doi: 10.1371/journal.pone.0266920. eCollection 2022.
The approximate number system (a) views number as an imprecise signal that (b) functions equivalently regardless of a number's initial presentation. These features do not readily account for exact readings when a task calls for them. While profiting from insights in areas neighboring the number cognition literature, we propose that linguistic-pragmatic and cultural pressures operate on a number's upper bound in order to provide exact readings. With respect to (a), Experimental Pragmatic findings indicate that numbers appear to be semantically lower-bounded (Eleven candidates are coming means at least eleven) but fluid at its upper-bound; exactly readings emerge as a consequence of an additional pragmatic process that solidifies the upper bound. With respect to (b), studies from cognitive anthropology underline how symbolic representations of number are distinct from written codes. Here, we investigate a novel hypothesis proposing that symbolic expressions of number (such as "11") explicitly provide exactly readings unlike verbal (oral and written) ones, which engender at least readings. We then employ a Numerical Magnitude Task (NMT), in which French-speaking participants determine whether a presented number is lesser or greater than a benchmark (12) in one of three presentation conditions: i) Symbolic/Hindu-Arabic (e.g. "11" via screen), ii) Oral (e.g. "/'on.zə/" via headphones), or; iii) spelled-out-in-Letters (e.g. "onze" via screen). Participants also carry out a Number Identification Task (NIT) so that each participant's recognition speed per number can be removed from their NMT times. We report that decision reaction times to "onze" take longer to process (and prompt more errors) than "treize" whereas "11" and "13" are comparable. One prediction was not supported: Decision times to the critical oral forms ("/'on.zə/" and "[tʁ̥ɛːzə̆]") were comparable, making these outcomes resonate with those in the Symbolic condition.
近似数字系统 (a) 将数字视为一种不精确的信号,(b) 无论数字的初始表示如何,其功能都是等效的。这些特征在任务需要精确读数时不容易得到。虽然借鉴了数字认知文献邻域的见解,但我们提出语言-语用和文化压力作用于数字的上限,以提供精确读数。关于 (a),实验语用学发现表明,数字似乎在语义上是下限(“有十一位候选人前来”意味着至少有十一位),但其上限是灵活的;精确读数是由于一个额外的语用过程而出现的,该过程使上限固定。关于 (b),认知人类学的研究强调了数字的符号表示与书面代码的区别。在这里,我们提出了一个新的假设,即数字的符号表达(如“11”)明确提供精确读数,而不同于口头(口头和书面)表达,后者产生至少读数。然后,我们使用数值大小任务 (NMT),让说法语的参与者在三种呈现条件之一中确定呈现的数字是否小于或大于基准(12):i)符号/印度-阿拉伯数字(例如通过屏幕呈现的“11”),ii)口头(例如通过耳机呈现的“/'on.zə/'”),或;iii)拼出的字母(例如通过屏幕呈现的“onze”)。参与者还进行数字识别任务 (NIT),以便从他们的 NMT 时间中删除每个参与者对每个数字的识别速度。我们报告说,“onze”的决策反应时间比“treize”处理起来要长(并且错误更多),而“11”和“13”则是可比的。一个预测没有得到支持:关键口头形式(“/'on.zə/'”和“[tʁ̥ɛːzə̆]”)的决策时间相当,这使得这些结果与符号条件的结果产生共鸣。