Neuronal selectivity and geometric alignment in the human hippocampus support abstract generalization

Abstract representations allow the brain to extract shared structure across different experiences and generalize knowledge beyond individual situations. Although previous studies have shown that representational geometry plays a critical role in supporting abstraction, it remains unclear how the composition of neuronal populations gives rise to such generalizable representations. Here, we investigated how neuronal selectivity shapes the emergence of abstract representations by combining a controlled computational model with analyses of human hippocampal single-neuron recordings. We first manipulated the composition of artificial neural populations to test whether increasing task-related information alone is sufficient to improve cross-context generalization. Although increasing stimulus- and response-selective neurons enhanced encoding strength, it did not improve generalization across contexts. In contrast, introducing category-selective neurons increased cross-context generalization, demonstrating that the type of information represented by a population is critical for abstraction. Analyses of human hippocampal neurons revealed a similar principle: category-like and identity-like neurons produced comparable increases in stimulus encoding, but category-like neurons produced substantially stronger improvements in cross-context generalization. Further analyses showed that category-like neurons influenced abstraction by reshaping population geometry. Specifically, category-axis alignment across contexts, rather than the strength of category-related separation, was the geometric property most strongly associated with generalization. Mediation analysis further indicated that category-like neurons contribute to abstraction primarily through their ability to increase geometric alignment across contexts. Together, these findings reveal a population-level mechanism linking neuronal selectivity to abstract computation and suggest that flexible generalization depends not simply on increasing neural information, but on organizing information into geometries that preserve task-relevant relationships across changing conditions.