The Forward-Forward (FF) algorithm trains networks layer-by-layer using a local "goodness function," yet the choice of activity statistic remains an important design question. We systematically study how selectivity and activity shape affect FF learning, introducing selective goodness functions based on top-$k$ and entmax-weighted energy alongside shape-sensitive objectives based on normalized higher-order moments. In particular, we propose burstiness goodness, a parameter-free, scale-invariant excess-kurtosis objective that rewards heavy-tailed activity patterns. Our study examines goodness functions together with activation and label-injection choices across six small-image datasets, and uses controlled experiments to isolate their effects. In validation-tuned, five-seed Fashion-MNIST experiments with matched ReLU activation, standard label injection, and per-layer scoring, burstiness improves accuracy from $73.2\pm0.9\%$ to $88.6\pm0.4\%$ over sum-of-squares. On CIFAR-10, burstiness improves block-local ResNet-18 accuracy from $28.9\pm0.5\%$ to $46.8\pm2.5\%$ under the same separate per-block label pathway. These results show that activity-shape statistics can provide effective local learning signals across the tested settings. Code is available at https://github.com/suayptalha/ff-selectivity-shape.
Bound states in the continuum (BIC) metasurfaces are typically described by geometry-specific parameters, hindering cross-geometry comparison, while ultranarrow quasi-BIC (qBIC) features are easily diluted in full-spectrum learning. Here, we map 2,015 samples from seven dielectric metasurface families onto a shared reciprocal-lattice grid. Two fixed low-order Fourier descriptors capture within-branch resonance shifts, with mean $R^2$ values of 0.871-0.999. Field-level analysis of two representative branches shows that these shifts are consistent with the Maxwell-Fourier perturbation picture. A five-channel Fourier-space backbone models the broadband spectrum. A local resonance expert uses complex Fourier coefficients to parameterize the qBIC resonance through a differentiable Fano layer. The expert reduces resonance-position mean absolute error (MAE) from 3.2 to 0.95 nm and reduces the minimum-transmission MAE by a factor of 14 on a geometry-blocked test set. The same coordinate supports spectrum-to-structure reconstruction.