Brain-like variability in convolutional neural networks reveals evidence-, uncertainty- and bias-driven decision-making
Nakuci, J.
Show abstract
Deep neural networks can appear confident even when weakly supported by inputs. Current approaches such as calibration and feature attribution relate confidence to error or map predictions to features, but do not quantify how the decision margin is supported. Here we introduce a layer-resolved Bias Dominance Index (BDI) that decomposes the margin into feature and bias components to localize where bias-dominance emerges. Across models - convolutional neural networks (CNN) and vision transformers (ViT)- BDI increases as feature support weakens and high confidence can coexist with bias-dominant decisions. Moreover, bias-dominance emerges in convolutional layers in CNNs and normalization layers in ViT. Perturbation analyses further show that bias components can be beneficial when readout weights are degraded. Finally, we operationalize decision composition as a two-parameter acceptance rule that combines confidence with BDI for auditing. Together, these results position BDI as a diagnostic for monitoring and triage, distinguishing feature-versus bias-driven decisions.
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