ABSTRACT There is strong and diverse evidence for mental rotation (MR) abilities in adults. However, current evidence for MR in children rests on just a few behavioral paradigms adapted from the adult literature. Here, we leverage recent computational models of the development of children’s object recognition abilities to re‐assess the evidence for MR in children. The computational models simulate infants’ acquisition of object representations during embodied interactions with objects. We consider two different object recognition strategies, different from MRs, and assess their ability to replicate results from three classical MR tasks assigned to children between the ages of 6 months and 5 years. Our results show that MR may play no role in producing the results obtained from children younger than 5 years. In fact, we find that a simple recognition strategy that reflects a pixel‐wise comparison of stimuli is sufficient to model children’s behavior in the most used MR task. Thus, our study reopens the debate on how and when children develop genuine MR abilities. Summary We use a bio‐inspired machine learning models to examine evidence of mental rotation abilities in children We show that a simple recognition strategy suffices to solve habituation‐based tasks used to assess mental rotations in young children. We demonstrate that a model forming expectations about a rotation transformation can explain children’s results in mental rotation tasks with the violation‐of‐expectation paradigm. The investigated recognition strategies no longer work when the mental rotation tasks are made closer to adults’.

Self-Supervised Learning (SSL) has achieved impressive success in learning semantic visual representations, yet the underlying principles driving this success remain underexplored. In this work, we hypothesize that common SSL pretext tasks implicitly model object co-occurrence statistics, a fundamental cue for visual learning. To test this, we curate three datasets of segmented objects from existing vision benchmarks using a state-of-the-art segmentation model. Through experiments across many SSL models, we reveal a hierarchical encoding of semantic information: while the visual backbone captures object categories, the projection head specializes in encoding co-occurrences between object categories. As a result, the projection head outperforms the backbone in aligning with human judgments of inter-category similarity. Furthermore, by controlling co-occurrence patterns during pre-training, we demonstrate that encoding object co-occurrences can significantly accelerate the emergence of category-level representations. Our findings uncover a previously hidden learning principle in SSL and suggest a path toward designing more effective pretext tasks by explicitly leveraging object co-occurrence structure.