Matching the emotional expressions of pairs of face photos was slower with pixelated and blurred photos than with original, untransformed photos. Matching the identities of the same face pairs was unaffected by pixelation and blurring. Because pixelation and blurring degrade higher spatial frequencies carrying edge-based information that define feature shape more than lower frequencies carrying configural properties, these findings converge with findings for line drawings and negative photos in showing that expression and face recognition processes differ in their reliance on edge-based and configural information.

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The robust meta‐analytical‐predictive (rMAP) prior is a popular method to robustly leverage external data. However, a mixture coefficient would need to be pre‐specified based on the anticipated level of prior‐data conflict. This can be very challenging at the study design stage. We propose a novel empirical Bayes robust MAP (EB‐rMAP) prior to address this practical need and adaptively leverage external/historical data. Built on Box's prior predictive p‐value, the EB‐rMAP prior framework balances between model parsimony and flexibility through a tuning parameter. The proposed framework can be applied to binomial, normal, and time‐to‐event endpoints. Implementation of the EB‐rMAP prior is also computationally efficient. Simulation results demonstrate that the EB‐rMAP prior is robust in the presence of prior‐data conflict while preserving statistical power. The proposed EB‐rMAP prior is then applied to a clinical dataset that comprises 10 oncology clinical trials, including the prospective study.

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