Exact(2)
Post-hoc analyses with Bonferroni corrections showed significant Notation main effect for small numerals, Error rates, p<.001; RTs: p<.001.
As shown in Fig. 2, participants made fewer errors and responded faster for small Chinese numerals (Error rates: Mean = 13.26%; RTs: Mean = 600.01 msec) than small Arabic numerals (Error rates: Mean = 32.23%; RTs: Mean = 640.22 msec) when they were presented at the parafoveal location, especially for the numerals one, two and three.
Similar(58)
To maximize readability and reduce errors, we use the largest size codes available (Version 1-H), capable of encoding test identifiers of up to 17-digit numerals under a high error code correction (ECC) capability.
Then for each dot numerosity and for each numeral, we fit an error function to the data and calculated the 50% choice probability, which gives us a numeral equivalent for each dot numerosity, and vice versa (Fig. 4c).
In addition, an interesting finding in our behavioral results is that small numerals generally showed more errors than large numerals when being presented at the parafoveal condition (Fig. 2), which has been consistently found in our previous studies using different paradigms (e.g., the Posner task) [55], [56], [57].
The Arabic numeral 4 received extremely high error rates (44%) in the parafoveal condition (Fig. 2), probably due to its visual similarity to the Chinese large-value numeral ten 十, with only an additional slash at the upper left corner, which could severely disrupt the participant when those numerals were presented so fast at the parafoveal location.
Since RSS is widely used as the measurement data, we propose a concrete numeral upper bound of the measurement error of a LLS-based RSS (LLS-RSS).
Participants made fewer errors and responded faster for small Chinese numerals than small Arabic numerals, especially for the numerals one, two and three (Fig. 2).
Adult humans estimating large numbers of items show increasing errors for proportionately smaller differences, but our accuracy using numerals is almost entirely independent of number magnitude, i.e. it is linear.
Mean error rates and correct reaction times (RTs) for Chinese and Arabic numerals in the foveal and parafoveal conditions were plotted (Fig. 2).
While the error rates for verbs, adjectives, and pronouns decreased, the error rates for nouns and numerals increased.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

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