2022
DOI: 10.1111/desc.13305
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No intrinsic number bias: Evaluating the role of perceptual discriminability in magnitude categorization

Abstract: Accumulating evidence suggests that there is a spontaneous preference for numerical, compared to non‐numerical (e.g., cumulative surface area), information. However, given a paucity of research on the perception of non‐numerical magnitudes, it is unclear whether this preference reflects a specific bias towards number, or a general bias towards the more perceptually discriminable dimension (i.e., number). Here, we found that when the number and area of visual dot displays were matched in mathematical ratio, num… Show more

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Cited by 3 publications
(2 citation statements)
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“…In our results, number was not consistently the most‐used nontarget feature on other comparisons, and it was the least strongly relied‐upon feature on its own task (compared to the weight on surface area during the surface area task, or the weight on convex hull during the convex hull task). Instead, consistent with recent work indicating that number may not have a privileged role once feature perceptibility is taken into account (Aulet & Lourenco, 2023), these results suggest that number is not more easily focused on when other features conflict with it.…”
Section: Discussionsupporting
confidence: 90%
“…In our results, number was not consistently the most‐used nontarget feature on other comparisons, and it was the least strongly relied‐upon feature on its own task (compared to the weight on surface area during the surface area task, or the weight on convex hull during the convex hull task). Instead, consistent with recent work indicating that number may not have a privileged role once feature perceptibility is taken into account (Aulet & Lourenco, 2023), these results suggest that number is not more easily focused on when other features conflict with it.…”
Section: Discussionsupporting
confidence: 90%
“…Recently, Lourenco (2021, 2022) suggested an alternative approach to match the perceptual rather than mathematical discriminability of quantity and surface area. In a numerical comparison task with children (Aulet & Lourenco, 2022), when decimal-scale mathematical ratios were matched, a bias for numerosity over surface area emerged. However, when perceptual discriminability was matched, ensuring changes in quantity were equally easy to discriminate as changes in total surface area, a bias for surface area over numerosity emerged.…”
Section: Additional Generation Methods Frameworkmentioning
confidence: 99%