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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. 

Abstract Scientific evidence regularly guides policy decisions¹, with behavioural science increasingly part of this process². In April 2020, an influential paper³proposed 19 policy recommendations (‘claims’) detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms ‘physical distancing’ and ‘social distancing’. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization.