Feature identification learning both shapes and is shaped by spatial object-similarity representations.

Object knowledge is bound together in semantic networks that can be spatially represented. How these knowledge representations shape and are in turn shaped by learning remains unclear. Here, we directly examined how object similarity representations impact implicit learning of feature dimensions and how learning, in turn, influences these representations. In a pre-experiment, 237 adult participants arranged object-pictures in a spatial arena, revealing semantic relatedness of everyday objects across categories: activity, fashion, and foods. The subsequent experiment assessed whether these semantic relationships played a role in implicitly learning specific object features in a separate adult participant group (N = 82). Participants inferred the meanings of two pseudo-words through feedback. Using computational modeling, we tested various learning strategies and established that learning was guided by semantic relationships quantified in the pre-experiment. Post-learning arrangements reflected object similarity representations as well as the learned feature. We directly show that similarity representations guide implicit learning and that learning in turn reshapes existing knowledge representations.