Pollinator-driven selection is thought to drive much of the extraordinary diversity of flowering plants. Plants that produce floral traits preferred by particular pollinators are more likely to receive conspecific pollen and to evolve further adaptations to those pollinators that enhance pollination and ultimately generate floral diversity. Two mechanisms in particular, sensory bias and learning, are thought to explain how pollinator preference can contribute to divergence and speciation in flowering plants. While the preferences of pollinators, such as bees, flies, and birds, are frequently implicated in patterns of floral trait evolution, the role of learning in generating reproductive isolation and trait divergence for different floral types within plant populations is not well understood. Floral color polymorphism in particular provides an excellent opportunity to examine how pollinator behavior and learning might maintain the different floral morphs. In this study we asked if bumble bees showed innate preferences for different color morphs of the pollen-only plant Solanum tridynamum, whether bees formed preferences for the morphs with which they had experience collecting pollen from, and the strength of those learned preferences. Using an absolute conditioning protocol, we gave bees experience collecting pollen from a color polymorphic plant species that offered only pollen rewards. Despite initially-naïve bees showing no apparent innate bias toward human-white versus human-purple flower morphs, we did find evidence of a bias in learning. Specifically, bees learned strong preferences for purple corollas, but learned only weak preferences for hypochromic (human-white) corollas. We discuss how our results might explain patterns of floral display evolution, particularly as they relate to color polymorphisms. Additionally, we propose that the ease with which floral visual traits are learned—i.e., biases in learning—can influence the evolution of floral color as a signal to pollinators.

Cultural artifacts, such as language, survive and replicate by passing from mind to mind. Cultural evolution always proceeds by an inductive process, where behaviors are never directly copied, but reverse engineered by the cognitive mechanisms involved in learning and production. I will refer to this type of evolutionary change as inductive evolution and explain how this represents a broader class of evolutionary processes that can include both neutral and selective evolution. This thesis takes a mechanistic approach to understanding the forces of evolution underlying change in culture over time, where the mechanisms of change are sought within human cognition. I define culture as anything that replicates by passing through a cognitive system and take language as a premier example of culture, because of the wealth of knowledge about linguistic behaviors (external language) and its cognitive processing mechanisms (internal language). Mainstream cultural evolution theories related to social learning and social transmission of information define culture ideationally, as the subset of socially-acquired information in cognition that affects behaviors. Their goal is to explain behaviors with culture and avoid circularity by defining behaviors as markedly not part of culture. I take a reductionistic approach and argue that all there is to culture is brain states and behaviors, and further, that a complete explanation of the forces of cultural change can not be explained by a subset of cognition related to social learning, but necessarily involves domain-general mechanisms, because cognition is an integrated system. Such an approach should decompose culture into its constituent parts and explore 1) how brains states effect behavior, 2) how behavior effects brain states, and 3) how brain states and behaviors change over time when they are linked up in a process of cultural transmission, where one person's behavior is the input to another. I conduct several psychological experiments on frequency learning with adult learners and describe the behavioral biases that alter the frequencies of linguistic variants over time. I also fit probabilistic models of cognition to participant data to understand the inductive biases at play during linguistic frequency learning. Using these inductive and behavioral biases, I infer a Markov model over my empirical data to extrapolate participants' behavior forward in cultural evolutionary time and determine equivalences (and divergences) between inductive evolution and standard models from population genetics. As a key divergence point, I introduce the concept of non-binomial cultural drift, argue that this is a rampant form of neutral evolution in culture, and empirically demonstrate that probability matching is one such inductive mechanism that results in non-binomial cultural drift. I argue further that all inductive problems involving representativeness are potential drivers of neutral evolution unique to cultural systems. I also explore deviations from probability matching and describe non-neutral evolution due to inductive regularization biases in a linguistic and non-linguistic domain. Here, I offer a new take on an old debate about the domain-specificity vs -generality of the cognitive mechanisms involved in language processing, and show that the evolution of regularity in language cannot be predicted in isolation from the general cognitive mechanisms involved in frequency learning. Using my empirical data on regularization vs probability matching, I demonstrate how the use of appropriate non-binomial null hypotheses offers us greater precision in determining the strength of selective forces in cultural evolution.