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Past investigations into the connection between space and numbers have revealed its potential vulnerability to external influences such as cultural factors, including language. This study aims to examine whether language moderates the association between space and number in the spatial-numerical association of response codes (SNARC) effect, which is demonstrated in an interaction between number magnitude and response side. The SNARC effect has been observed across various stimuli. However, research on the influence of linguistic factors, such as reading direction, on the SNARC effect has yielded contradictory findings. We systematically examined the moderating effect of language on the SNARC effect in a cross-cultural design. A group of British English speakers and a group of Iranian Farsi speakers performed four SNARC tasks including both explicit (magnitude classification) and implicit (parity judgment) processing of number magnitude in two modalities of visual and auditory presentations. To the best of our knowledge, this is the first systematic investigation of language, magnitude processing, and sensory modalities altogether. While our registered analyses found no group differences in SNARC tasks, nonregistered analyses using a Bayesian ex-Gaussian framework revealed novel findings: a stronger SNARC effect in slower responses and auditory tasks. These findings challenge the idea of a substantial language role in shaping the SNARC effect but also indicate large uncertainty regarding the exact nature of language-induced effects, highlighting the need for further investigations of spatial-numerical interactions that may be differently influenced by linguistic and cultural factors. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Math performance is negatively related to math anxiety (MA), though MA may impact certain math skills more than others. We investigated whether the relation between MA and math performance is affected by task features, such as number type (e.g., fractions, whole numbers, percentages), number format (symbolic vs. nonsymbolic), and ratio component size (small vs. large). Across two large-scale studies (combined n = 3,822), the MA-performance relation was strongest for large whole numbers and fractions, and stronger for symbolic than nonsymbolic fractions. The MA-performance relation was also stronger for smaller relative to larger components, and MA relating to specific number types may be a better predictor of performance than general MA for certain tasks. The relation between MA and estimation performance changes depending on task features, which suggests that MA may relate to certain math skills more than others, which may have implications for how people reason with numerical information and may inform future interventions. (PsycInfo Database Record (c) 2023 APA, all rights reserved).