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

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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… Click to show full abstract

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. To differentiate aspects of learning from environmental factors that affect motivation, we ran a series of experiments with varying task factors. We compared monkeys (Macaca mulatta) with VS lesions, amygdala lesions, and unoperated controls on reinforcement learning (RL) tasks that involve learning from both gains and losses as well as from deterministic and stochastic schedules of reinforcement. We found that for all three groups, performance varied by experiment. All three groups modulated their behavior in the same directions, to varying degrees, across the three experiments. This behavioral modulation is why we find deficits in some experiments, but not others. The amount of effort animals exhibited differed depending on the learning environment. Our results suggest that the VS is important for the amount of effort animals will give in rich deterministic and relatively leaner stochastic learning enivornments. We also showed that monkeys with amygdala lesions can learn stimulus-based RL in stochastic environments and environments with loss and conditioned reinforcers. These results show that learning environments shape motivation and that the VS is essential for distinct aspects of motivated behavior. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Keywords: learning gains; gains losses; ventral striatum; striatum amygdala

Journal Title: Behavioral neuroscience
Year Published: 2023

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