Bernstein Center for Computational Neuroscience, Philippstr, Haus, Berlin, Germany.
Prog Brain Res. 2013;202:415-39. doi: 10.1016/B978-0-444-62604-2.00021-6.
Recent research suggests that novelty has an influence on reward-related learning. Here, we showed that novel stimuli presented from a pre-familiarized category can accelerate or decelerate learning of the most rewarding category, depending on the condition. The extent of this influence depended on the individual trait of novelty seeking. Different reinforcement learning models were developed to quantify subjects' choices. We introduced a bias parameter to model explorative behavior toward novel stimuli and characterize individual variation in novelty response. The theoretical framework allowed us to test different assumptions, concerning the motivational value of novelty. The best fitting-model combined all novelty components and had a significant positive correlation with both the experimentally measured novelty bias and the independent novelty-seeking trait. Altogether, we have not only shown that novelty by itself enhances behavioral responses underlying reward processing, but also that novelty has a direct influence on reward-dependent learning processes, consistently with computational predictions.
最近的研究表明，新奇性对与奖励相关的学习有影响。在这里，我们表明，根据条件的不同，从预先熟悉的类别中呈现的新刺激可以加速或减缓最有奖励性的类别学习。这种影响的程度取决于个体对新奇的寻求特质。开发了不同的强化学习模型来量化被试的选择。我们引入了一个偏差参数来对新刺激的探索性行为进行建模，并对个体对新奇的反应进行特征化。该理论框架允许我们测试关于新奇性的动机价值的不同假设。拟合最好的模型结合了所有新奇性成分，与实验测量的新奇性偏差和独立的新奇性寻求特征都有显著的正相关。总的来说，我们不仅表明新奇性本身可以增强奖励处理的基础行为反应，而且新奇性对依赖奖励的学习过程有直接影响，这与计算预测一致。
