Two determinants of dynamic adaptive learning for magnitudes and probabilities

Abstract

AbstractHumans face a dynamic world that requires them to constantly update their knowledge. Each observed outcome should influence their knowledge to a varying degree depending on whether it arises from a stochastic fluctuation or an environmental change. Thus, humans should dynamically adapt their learning rate based on each outcome. Although crucial for characterizing the learning process, these dynamic adjustments have only been investigated empirically in magnitude learning. Another important type of learning is probability learning. The latter differs from the former in that individual outcomes are much less informative and a single one is insufficient to distinguish environmental changes from stochasticity. Do humans dynamically adapt their learning rate for probabilities? What determinants drive their dynamic adjustments in magnitude and probability learning? To answer these questions, we measured the subjects’ learning rate dynamics directly through real-time continuous reports during magnitude and probability learning. We found that subjects dynamically adapt their learning rate in both types of learning. After a change point, they increase their learning rate suddenly for magnitudes and prolongedly for probabilities. Their dynamics are driven differentially by two determinants: change-point probability, the main determinant for magnitudes, and prior uncertainty, the main determinant for probabilities. These results are fully in line with normative theory, both qualitatively and quantitatively. Overall, our findings demonstrate a remarkable human ability for dynamic adaptive learning under uncertainty, and guide studies of the neural mechanisms of learning, highlighting different determinants for magnitudes and probabilities.Significance statementIn a dynamic world, we must constantly update our knowledge, but not all data we observe is equally important. How do humans adjust the weight given to each observation? Here, we’ve demonstrated two principles that underlie humans’ adjustments and their dynamic adaptive learning capabilities. Firstly, when observing a highly surprising event indicating a likely change in the environment, humans reset their knowledge and learn in one shot. Secondly, when their knowledge is more uncertain, humans update it more quickly. These two forces each dominate in one of two key learning contexts, magnitude and probability learning. Our findings advance understanding of the mechanisms of human learning, with implications for the brain and the development of adaptive machines.