Gatsby Computational Neuroscience Unit, University College London, London, United Kingdom.
Department for Movement and Clinical Neurosciences, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom.
PLoS One. 2018 Oct 22;13(10):e0205974. doi: 10.1371/journal.pone.0205974. eCollection 2018.
Humans build models of their environments and act according to what they have learnt. In simple experimental environments, such model-based behaviour is often well accounted for as if subjects are ideal Bayesian observers. However, more complex probabilistic tasks require more sophisticated forms of inference that are sufficiently computationally and statistically taxing as to demand approximation. Here, we study properties of two approximation schemes in the context of a serial reaction time task in which stimuli were generated from a hierarchical Markov chain. One, pre-existing, scheme was a generically powerful variational method for hierarchical inference which has recently become popular as an account of psychological and neural data across a wide swathe of probabilistic tasks. A second, novel, scheme was more specifically tailored to the task at hand. We show that the latter model fit significantly better than the former. This suggests that our subjects were sensitive to many of the particular constraints of a complex behavioural task. Further, the tailored model provided a different perspective on the effects of cholinergic manipulations in the task. Neither model fit the behaviour on more complex contingencies that competently. These results illustrate the benefits and challenges that come with the general and special purpose modelling approaches and raise important questions of how they can advance our current understanding of learning mechanisms in the brain.
人类会建立环境模型，并根据所学内容采取行动。在简单的实验环境中，基于模型的行为通常可以很好地解释，就好像主体是理想的贝叶斯观察者一样。然而，更复杂的概率任务需要更复杂的推断形式，这些推断形式在计算和统计上都非常繁重，需要进行近似。在这里，我们在一个序列反应时任务的背景下研究了两种近似方案的性质，其中刺激是由分层马尔可夫链生成的。一种是现有的、通用的分层推断变分方法，它最近作为一种解释心理和神经数据的方法在广泛的概率任务中变得流行。第二种是新的、更专门针对手头任务的方案。我们表明，后一种模型的拟合明显优于前一种。这表明我们的实验对象对复杂行为任务的许多特定约束非常敏感。此外，定制模型还提供了一种看待任务中胆碱能操作影响的不同视角。这两种模型都不能很好地拟合更复杂的条件。这些结果说明了通用和专用建模方法的好处和挑战，并提出了重要的问题，即它们如何能推进我们目前对大脑学习机制的理解。
