Using neuroimaging in combination with computational modeling, this study shows that decision threshold modulation for reward maximization is accompanied by a change in effective connectivity within corticostriatal and cerebellar-striatal brain …
Learning by following explicit advice is fundamental for human cultural evolution, yet the neurobiology of adaptive social learning is largely unknown. Here, we used simulations to analyze the adaptive value of social learning mechanisms, …
When we make decisions, the benefits of an option often need to be weighed against accompanying costs. Little is known, however, about the neural systems underlying such cost-benefit computations. Using functional magnetic resonance imaging and …
The mesocorticolimbic dopamine (DA) system linking the dopaminergic midbrain to the prefrontal cortex and subcortical striatum has been shown to be sensitive to reinforcement in animals and humans. Within this system, coexistent segregated …
Adaptive decision making depends on the accurate representation of rewards associated with potential choices. These representations can be acquired with reinforcement learning (RL) mechanisms, which use the prediction error (PE, the difference …
To make decisions based on the value of different options, we often have to combine different sources of probabilistic evidence. For example, when shopping for strawberries on a fruit stand, one uses their color and size to infer-with some …
In our everyday life, we often have to make decisions with risky consequences, such as choosing a restaurant for dinner or choosing a form of retirement saving. To date, however, little is known about how the brain processes risk. Recent …
Many decisions people make can be described as decisions under risk. Understanding the mechanisms that drive these decisions is an important goal in decision neuroscience. Two competing classes of risky decision making models have been proposed to …
The ability to rapidly and flexibly adapt decisions to available rewards is crucial for survival in dynamic environments. Reward-based decisions are guided by reward expectations that are updated based on prediction errors, and processing of these …