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Abstract Measurement of object recognition (OR) ability could predict learning and success in real-world settings, and there is hope that it may reduce bias often observed in cognitive tests. Although the measurement of visual OR is not expected to be influenced by the language of participants or the language of instructions, these assumptions remain largely untested. Here, we address the challenges of measuring OR abilities across linguistically diverse populations. In Study 1, we find that English–Spanish bilinguals, when randomly assigned to the English or Spanish version of the novel object memory test (NOMT), exhibit a highly similar overall performance. Study 2 extends this by assessing psychometric equivalence using an approach grounded in item response theory (IRT). We examined whether groups fluent in English or Spanish differed in (a) latent OR ability as assessed by a three-parameter logistic IRT model, and (2) the mapping of observed item responses on the latent OR construct, as assessed by differential item functioning (DIF) analyses. Spanish speakers performed better than English speakers, a difference we suggest is due to motivational differences between groups of vastly different size on the Prolific platform. That we found no substantial DIF between the groups tested in English or Spanish on the NOMT indicates measurement invariance. The feasibility of increasing diversity by combining groups tested in different languages remains unexplored. Adopting this approach could enable visual scientists to enhance diversity, equity, and inclusion in their research, and potentially in the broader application of their work in society. 

People with superior face recognition have relatively thin cortex in face-selective brain areas, whereas those with superior vehicle recognition have relatively thick cortex in the same areas. We suggest that these opposite correlations reflect distinct mechanisms influencing cortical thickness (CT) as abilities are acquired at different points in development. We explore a new prediction regarding the specificity of these effects through the depth of the cortex: that face recognition selectively and negatively correlates with thickness of the deepest laminar subdivision in face-selective areas. With ultrahigh resolution MRI at 7T, we estimated the thickness of three laminar subdivisions, which we term “MR layers,” in the right fusiform face area (FFA) in 14 adult male humans. Face recognition was negatively associated with the thickness of deep MR layers, whereas vehicle recognition was positively related to the thickness of all layers. Regression model comparisons provided overwhelming support for a model specifying that the magnitude of the association between face recognition and CT differs across MR layers (deep vs. superficial/middle) whereas the magnitude of the association between vehicle recognition and CT is invariant across layers. The total CT of right FFA accounted for 69% of the variance in face recognition, and thickness of the deep layer alone accounted for 84% of this variance. Our findings demonstrate the functional validity of MR laminar estimates in FFA. Studying the structural basis of individual differences for multiple abilities in the same cortical area can reveal effects of distinct mechanisms that are not apparent when studying average variation or development.