Combinatorial individual differences scaling within the city-block metric

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A new method is proposed for conducting individual differences scaling within the city-block metric that does not rely on gradient- or subgradient-based optimization. Instead, a combinatorial optimization scheme is utilized for identifying object coordinates minimizing the least-squares loss function. The illustrative application of combinatorial individual differences scaling within the city-block metric to schematic face stimuli suggests that the new method offers a promising alternative to gradient-based attempts for fitting city-block scaling models, which suffer from the well-documented difficulty of local minima.