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Learning from past experiences helps orient the exploration of unknown environments. Yet, in real-world environments with a vast number of possibilities, it is simply not feasible to try out and explore all options. Instead of exploring randomly, a growing body of research shows that exploration is guided by predictive generalizations about expected rewards and uncertainty-directed exploration. These same mechanisms appear across a variety of spatial, conceptual, graph-structured, and social environments. The paradigm is both intuitive and richly complex, allowing us to characterize developmental changes in learning and exploration across a wide range of ages, from 5 to 55. Through both behavioral and model-based analyses, we provide the first empirical test of the "simulated annealing" analogy of aging. We show that “cooling off” does not only apply to the single dimension of randomness (i.e., decision-noise), but rather, development resembles a stochastic optimization process in the space of learning strategies. What begins as large tweaks during childhood, plateaus and converges in adulthood. Remarkably, none of the optimization algorithms discovered reliably better regions of the strategy space than adult participants, suggesting an incredible efficiency of human development. Some notable differences between solutions discovered by human development vs. stochastic optimization provide valuable insights into how we make the most of limited cognitive resources.
For a one-to-one meeting with Charley, please contact Magdalena Soukupova: firstname.lastname@example.org