Complex natural systems from brains to bee swarms have evolved to make adaptive multifactorial decisions. Recent theoretical and empirical work suggests that many evolved systems may take advantage of common motifs across multiple domains. We are particularly interested in value sensitivity (i.e., sensitivity to the magnitude or intensity of the stimuli or reward under consideration) as a mechanism to resolve deadlocks adaptively. This mechanism favors long-term reward maximization over accuracy in a simple manner, because it avoids costly delays associated with ambivalence between similar options; speed–value trade-offs have been proposed to be evolutionarily advantageous for many kinds of decision. A key prediction of the value-sensitivity hypothesis is that choices between equally valued options will proceed faster when the options have a high value than when they have a low value. However, value sensitivity is not part of idealized choice models such as diffusion to bound. Here, we examine 2 different choice behaviors in 2 different species, perceptual decisions in humans and economic choices in rhesus monkeys, to test this hypothesis. We observe the same value sensitivity in both human perceptual decisions and monkey value-based decisions. These results endorse the idea that neural decision systems make use of the same basic principle of value sensitivity in order to resolve costly deadlocks and thus improve long-term reward intake.