The evolution of category systems within and between learners
Evolution and learning are both processes that allow organisms to extract and store information about their environment. But how do the dynamics of these processes differ? In an abstract computational sense, both are optimization processes that search a space of possible explanations and previous work has identified deep parallels in the mathematical models used to describe them (Suchow, Bourgin, & Griffiths, 2017). We present the results of an iterated category learning experiment, where the number and placement of participants’ category boundaries are free to evolve over time. We contrast two evolutionary regimes: one where category systems are transmitted over multiple learners and one where they are developed within a single learner, for the same amount of time. We find that there are more constraints on the evolutionary process when systems are culturally transmitted among multiple learners. Single learners explore a wider range of category systems and converge on more complex systems, whereas transmission chains explore a more restricted set of systems and nearly always converge on a simple, but easily learnable, one-boundary category system.