This article proposes that patterns of proximity data that have been characterized in terms of "asymmetric similarity" may be alternatively characterized in terms of differential "bias." Bias is a characteristic pertaining to an individual object, as opposed to similarity, which is a relation between two objects. It is proposed that biases can be stimulus- based as well as response-based, and numerous examples are provided. Part 1 of the article reviews an additive similarity and bias model proposed by Holman (1979), which generalizes various extant models that have successfully characterized asymmetric proximities. Part 1 then discusses relations between asymmetric proximities and differences in self-proximities, and also discusses multidimensional scaling models that are supplemented with stimulus bias terms. Part 2 of the article reviews and integrates a variety of phenomena in the perceptual classification literature involving asymmetries that can be characterized in terms of symmetric similarity together with differential stimulus bias. Part 3 provides examples of limitations of the additive similarity and bias model. A main thesis of the article is that models of proximity and classification data that incorporate properties of the individual stimulus may not always require recourse to the positing of asymmetric similarities.