Self-objectification (i.e., viewing one’s own body from an outsider’s gaze and perceiving it as the primary aspect of one’s self-identity) can help explain why appearance-based social pressures translate into downstream outcomes such as body shame, reduced cognitive performance, disordered eating, and depressive symptoms. The Self-Objectification Beliefs and Behaviors Scale (SOBBS) is a prominent scale used to assess self-objectification. While translations into Chinese (Land & Ye, 2021) and Brazilian Portuguese (Almeida et al., 2024) are available, the majority of the literature using this scale is English-speaking, and some of the scale’s psychometric properties (e.g., measurement invariance across gender) have not yet been thoroughly investigated.
 
To address this, Pavlík et al. (2026) undertake a rigorous translation and adaptation of the SOBBS into Czech, spanning five independent translations, cognitive think-aloud interviewing, evaluations of factor structure (using dynamic fit indices), measurement invariance, and criterion validity. The authors identify limitations requiring certain adaptations to the scale – dropping one item, and highlighting reasons to be cautious about the structure of the “body as a representation of the Self” subscale – but broadly find the measure to be psychometrically satisfactory and useful for ongoing research.
 
Importantly, their work it is a translation, but also one that meaningfully extends the conceptual basis of the construct. I found the authors' preregisteration of risky interval predictions especially interesting, and these predictions were largely supported, informing future effect size expectations. Their exploration of gender differences adds further support that self-objectification is relevant for men and can be validly assessed with the same measurement tool. The authors uphold a high standard of transparency throughout.
 
The manuscript was evaluated over one round of in-depth review and a further round of more fine-grained editing with a recommender. Two reviewers positively appraised the study design and method; their comments largely involved focused suggestions for more robust statistical methods (e.g., more meaningful dynamic fit indices, consideration of missing-data handling and measurement invariance estimators), and more comprehensive reporting (e.g., clarifying the role of the pilot study and preregistration deviations). The revision comprehensively addressed these points; among other improvements, the revised manuscript more explicitly explains and applies the Messick validity framework and is clearer about the scope of validity and reliability claims. Following this revision, the recommender judged that the manuscript merited a positive recommendation. 
 
References

Jan Pavlík, Nikol Kvardová, Petr Palíšek (2026) Psychometric Properties of the Czech Version of the Self-Objectification Beliefs and Behaviors Scale (SOBBS). PsyArXiv, ver.3 peer-reviewed and recommended by PCI Psychology https://doi.org/10.31234/osf.io/4f2au_v3

Lang, M., & Ye, Y. (2021). Validation of the Chinese Version of the Self-Objectification Beliefs and Behaviors Scale. Frontiers in Psychology, 12, 724187. https://doi.org/10.3389/fpsyg.2021.724187
 
Almeida, M., Brown, T. A., Santos, C. G., De Oliveira Júnior, M. L., & De Carvalho, P. H. B. (2024). Psychometric evaluation and measurement invariance of the Self-Objectification Beliefs and Behaviors Scale among Brazilian cisgender gay and bisexual adult men. Psychology of Sexual Orientation and Gender Diversity. https://doi.org/10.1037/sgd0000780

This theory paper positions itself within the augmented judgment and decision-making (JDM) literature, where AI systems complement rather than replace human decision-making. The field has been dominated by a human-centric perspective that compares human versus algorithmic judgment (e.g., algorithm aversion vs. appreciation research). Rebholz argues this comparative framework is useful but incomplete. As users increasingly encounter multiple, heterogeneous AI systems—from linear models to large language models—the field needs an algorithm-centric lens that examines how people differentiate among various AI systems, not just between humans and algorithms generally.

My own interest in the work originates in my own human-centric studies of AI-augmented decision-making, especially the 2019 “algorithm appreciation” paper by Logg, Minson, and Moore. I appreciated the paper’s attempt to update our understanding of human-algorithm interaction in light of more recent technological developments.

The ways in which humans think about the machines with which they interact are constantly evolving, thanks to changes in the technologies and changes in the culture around their usage. Children born today with develop very different theories to understand the machines with which they interact than do those born 50 years ago.

I appreciate this paper’s systematic consideration of different artificial intelligences and its development of testable research propositions in five different research directions. Reviewers appreciated the paper’s development of a theoretical framework in which to understand how people think about them minds of AI systems.

References

Rebholz, T. R. (2025) Lay beliefs about artificial versus artificial intelligence: Rethinking theory of machine. PsyArXiv, ver.5 peer-reviewed and recommended by PCI Psychology https://doi.org/10.31234/osf.io/ekz9a_v5