Interpretability in Exploratory Factor Analysis is characterized by the fundamental trade-off between structural sparseness and explained common variance. Lacking formal evaluation metrics, researchers frequently sacrifice parsimony in the pattern matrix to maximize the model's explanatory coverage. For this reason, we extend Hoyer's sparseness ratio into a flexible \texorpdfstring{$\mathcal{L}_{p_1}/\mathcal{L}_{p_2}$}{Lp1/Lp2} formulation to introduce the Interpretability Index $I$, defined as the geometric mean of explained common variance and the generalized sparseness index. We analyze the asymptotic behavior of $I$ as norm orders increase, establishing that raising $p_2$ alone yields a stable limit, whereas increasing $p_1$ and $p_2$ jointly causes the measure to saturate. The canonical choice $(p_1,p_2)=(2,4)$ balances numerical stability with a smooth, tractable objective. We finally demonstrate, on five benchmark datasets from the statistical literature, how the Interpretability Index provides a systematic criterion for selecting the optimal parametrization of the latent space according to the user's analytical goals.

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