# Cue Combination: Beyond Optimality

# Cue Combination: Beyond Optimality

This chapter briefly introduces the robust-weak-fusion model, which offers an exceptionally clear and elegant framework within which to understand empirical studies on cue combination. Research on cue combination is an area in the cognitive neurosciences where *quantitative* models and predictions are the norm rather than the exception—and this is certainly a development that this book welcomes wholeheartedly. What they view critically, however, is the strong emphasis on so-called optimal cue combination. Optimal in the context of human cue combination typically refers to the minimum-variance unbiased estimator for multiple sources of information, corresponding to maximum-likelihood estimation when the probability distribution of the estimates based on each cue are Gaussian, independent, and the prior of the observer is uniform (noninformative). The central aim of this chapter is to spell out worries regarding both the term *optimality* as well as against the use of the minimum-variance unbiased estimator as the statistical tool to go from the reliability of a cue to its weight in robust weak fusion.

*Keywords:*
robust-weak-fusion model, cue combination, optimality, minimum-variance unbiased estimator, reliability, robust weak fusion

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