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Effects of word-evoked object size on covert numerosity estimations

机译:单词诱发的对象大小对隐秘数字估计的影响

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We investigated whether the size and number of objects mentioned in digit-word expressions influenced participants’ performance in covert numerosity estimations (i.e., property probability ratings). Participants read descriptions of big or small animals standing in short, medium, and long rows (e.g., There are 8 elephants/ants in a row) and subsequently estimated the probability that a health statement about them was true (e.g., All elephants/ants are healthy). Statements about large animals scored lower than statements about small animals, confirming classical findings that humans perceive groups of large objects as being more numerous than groups of small objects (Binet, 1890) and suggesting that object size effects in covert numerosity estimations are particularly robust. Also, statements about longer rows scored lower than statements about shorter rows (cf. Sears, 1983) but no interaction between factors obtained, suggesting that quantity information is not fully retrieved in digit—word expressions or that their values are processed separately.
机译:我们调查了数字单词表达式中提到的对象的大小和数量是否会影响参与者在秘密数字估计(即,属性概率等级)中的表现。参加者阅读了关于大型,小型动物在短,中,长排站立的描述(例如,一排有八只大象/蚂蚁),随后估计了关于它们的健康声明为真的概率(例如,所有大象/蚂蚁)健康)。关于大型动物的陈述得分低于关于小型动物的陈述,这证实了经典的发现,即人类认为大型物体的组比小型物体的组要多(Binet,1890),并暗示在隐蔽的数字估计中物体尺寸的影响特别可靠。同样,关于较长行的陈述的得分要低于关于较短行的陈述(参见Sears,1983),但是在获得的因素之间没有相互作用,这表明数量信息未完全以数字-单词表达式检索或它们的值被单独处理。

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