Contribution of amygdala to dynamic model arbitration under uncertainty.

Intrinsic uncertainty in the reward environment requires the brain to run multiple models simultaneously to predict outcomes based on preceding cues or actions, commonly referred to as stimulus- and action-based learning. Ultimately, the brain also must adopt appropriate choice behavior using reliability of these models. Here, we combined multiple experimental and computational approaches to quantify concurrent learning in monkeys performing tasks with different levels of uncertainty about the model of the environment.

Computational Mechanisms Underlying Motivation to Earn Symbolic Reinforcers.

Reinforcement learning is a theoretical framework that describes how agents learn to select options that maximize rewards and minimize punishments over time. We often make choices, however, to obtain symbolic reinforcers (e.g.

Computational mechanisms underlying motivation to earn symbolic reinforcers.

Reinforcement learning (RL) is a theoretical framework that describes how agents learn to select options that maximize rewards and minimize punishments over time. We often make choices, however, to obtain symbolic reinforcers (e.g.

The motivational role of the ventral striatum and amygdala in learning from gains and losses.

The ventral striatum (VS) and amygdala are two structures often implicated as essential structures for learning. The literature addressing the contribution of these areas to learning, however, is not entirely consistent. We propose that these inconsistencies are due to learning environments and the effect they have on motivation.

Effects of Amygdala Lesions on Object-Based Versus Action-Based Learning in Macaques.

The neural systems that underlie reinforcement learning (RL) allow animals to adapt to changes in their environment. In the present study, we examined the hypothesis that the amygdala would have a preferential role in learning the values of visual objects. We compared a group of monkeys (Macaca mulatta) with amygdala lesions to a group of unoperated controls on a two-armed bandit reversal learning task.

Ventral striatum's role in learning from gains and losses.

Adaptive behavior requires animals to learn from experience. Ideally, learning should both promote choices that lead to rewards and reduce choices that lead to losses. Because the ventral striatum (VS) contains neurons that respond to aversive stimuli and aversive stimuli can drive dopamine release in the VS, it is possible that the VS contributes to learning about aversive outcomes, including losses.