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There are two unresolved puzzles in the literatures examining how people evaluate mates (i.e., prospective or current romantic/sexual partners). First, compatibility is theoretically crucial, but attempts to explain why certain perceivers are compatible with certain targets have revealed small effects. Second, features of partners (e.g., personality, consensually-rated attributes) affect perceivers’ evaluations strongly in initial-attraction contexts but weakly in established relationships. Mate Evaluation Theory (MET) addresses these puzzles, beginning with the Social Relations Model postulate that all evaluative constructs (e.g., attraction, relationship satisfaction) consist of target, perceiver, and relationship variance. MET then explains how people draw evaluations from mates’ attributes using four information sources: (a) shared evolved mechanisms and cultural scripts (common lens, which produces target variance); (b) individual differences that affect how a perceiver views all targets (perceiver lens, which produces perceiver variance); (c) individual differences that affect how a perceiver views some targets, depending on the targets’ features (feature lens, which produces some relationship variance); and (d) narratives about and idiosyncratic reactions to one particular target (target-specific lens, which produces most relationship variance). These two distinct sources of relationship variance (i.e., feature vs. target-specific) address puzzle #1: Previous attempts to explain compatibility used feature lens information, but relationship variance likely derives primarily from the (understudied) target-specific lens. MET also addresses puzzle #2 by suggesting that repeated interaction causes the target-specific lens to expand, which reduces perceivers’ use of the common lens. We conclude with new predictions and implications at the intersection of the human-mating and person-perception literatures. 

The science behind implicit bias tests (e.g., Implicit Association Test) has become the target of increased criticism. However, policymakers seeking to combat discrimination care about reducing bias in people’s actual behaviors, not about changing a person’s score on an implicit bias test. In line with this argument, we postulate that scientific controversies about implicit bias tests are irrelevant for antidiscrimination policy, which should instead focus on implicit bias in actual discriminatory behavior that occurs outside of awareness (in addition to instances of explicit bias). Two well-documented mechanisms can lead to implicit bias in actual discriminatory behavior: biased weighting and biased interpretation of information about members of particular social groups. The policy relevance of the two mechanisms is illustrated with their impact on hiring and promotion decisions, jury selection, and policing. Implications for education and bias intervention are discussed.