Open Peer Commentary

The target article provides important theoretical contributions to psychology and Bayesian modeling. Despite the article’s excellent points, we suggest that it succumbs to a few misconceptions about evolutionary psychology (EP). These include a mischaracterization of evolutionary psychology’s approach to optimality; failure to appreciate the centrality of mechanism in EP; and an incorrect depiction of hypothesis testing. An accurate characterization of EP offers more promise for successful integration with Bayesian modeling. Jones & Love (J&L) provide important theoretical contributions to psychology and Bayesian modeling. Especially illuminating is their discussion of whether Bayesian models are agnostic about psychology, serving mainly as useful scientific and mathematical tools, or instead make substantive claims about cognition. Despite its many strengths, the target article succumbs to some common misconceptions about evolutionary psychology (EP) (Confer et al. 2010). The first is an erroneous characterization of EP’s approach to optimality and constraints. Although the article acknowledges the importance of constraints in evolutionary theory, it lapses into problematic statements such as “evolutionary pressures tune a species’ genetic code such that the observed phenotype gives rise to optimal behaviors” (sect. 5, para. 3). J&L suggest that evolutionary psychologists reinterpret behavioral phenomena as “optimal” by engaging in a post hoc adjustment of their view of the relevant selection pressures operating in ancestral environments. These statements imply that a key goal of EP is to look for optimality in human behavior and psychology. On the contrary, the existence of optimized mechanisms is rejected by evolutionary psychologists, as this passage from Buss et al. (1998) illustrates: [T]ime lags, local optima, lack of genetic variation, costs, and limits imposed by adaptive coordination with other mechanisms all constitute major constraints on the design of adaptations. . . . Adaptations are not optimally designed mechanisms. They are . . . jerry-rigged, meliorative solutions to adaptive problems . . ., constrained in their quality and design by a variety of historical and current forces. (Buss et al. 1998,