Humans combine value learning and hypothesis testing strategically in multi-dimensional probabilistic reward learning.

ABSTRACT: Realistic and complex decision tasks often allow for many possible solutions. How do we find the correct one? Introspection suggests a process of trying out solutions one after the other until success. However, such methodical serial testing may be too slow, especially in environments with noisy feedback. Alternatively, the underlying learning process may involve implicit reinforcement learning that learns about many possibilities in parallel. Here we designed a multi-dimensional probabilistic active-learning task tailored to study how people learn to solve such complex problems. Participants configured three-dimensional stimuli by selecting features for each dimension and received probabilistic reward feedback. We manipulated task complexity by changing how many feature dimensions were re

SUBMITTER: Song M 

PROVIDER: S-EPMC9683628 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature