S. Bavard

The functional form of value normalization in human reinforcement learning

This study investigates value normalization in human reinforcement learning and finds support for range normalization over divisive normalization.

The construction and deconstruction of sub-optimal preferences through range-adapting reinforcement learning

This study investigates how range adaptation in reinforcement learning leads to suboptimal choices and proposes a model to explain the observed effects.

Reference-point centering and range-adaptation enhance human reinforcement learning at the cost of irrational preferences

This study investigates reinforcement learning by manipulating outcome valence and magnitude, finding that reference point dependence and range adaptation improve learning but lead to irrational choices in new contexts.