Department of Psychology, Institute of Psychology, University of Münster Münster, Germany ; Motor Cognition Group, Max Planck Institute for Neurological Research Cologne, Germany.
Department of Sport and Health Science, Technische Universität München Munich, Germany ; Department of Cognitive Neurology, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany.
Front Hum Neurosci. 2014 May 7;8:273. doi: 10.3389/fnhum.2014.00273. eCollection 2014.
The present fMRI study investigated whether human observers spontaneously exploit the statistical structure underlying continuous action sequences. In particular, we tested whether two different statistical properties can be distinguished with regard to their neural correlates: an action step's predictability and its probability. To assess these properties we used measures from information theory. Predictability of action steps was operationalized by its inverse, conditional entropy, which combines the number of possible action steps with their respective probabilities. Probability of action steps was assessed using conditional surprisal, which increases with decreasing probability. Participants were trained in an action observation paradigm with video clips showing sequences of 9-33 s length with varying numbers of action steps that were statistically structured according to a Markov chain. Behavioral tests revealed that participants implicitly learned this statistical structure, showing that humans are sensitive toward these probabilistic regularities. Surprisal (lower probability) enhanced the BOLD signal in the anterior intraparietal sulcus. In contrast, high conditional entropy, i.e., low predictability, was correlated with higher activity in dorsomedial prefrontal cortex, orbitofrontal gyrus, and posterior intraparietal sulcus. Furthermore, we found a correlation between the anterior hippocampus' response to conditional entropy with the extent of learning, such that the more participants had learnt the structure, the greater the magnitude of hippocampus activation in response to conditional entropy. Findings show that two aspects of predictions can be dissociated: an action's predictability is reflected in a top-down modulation of attentional focus, evident in increased fronto-parietal activation. In contrast, an action's probability depends on the identity of the stimulus itself, resulting in bottom-up driven processing costs in the parietal cortex.
本 fMRI 研究旨在探讨人类观察者是否会自发地利用连续动作序列的基础统计结构。具体来说，我们测试了两个不同的统计属性是否可以根据其神经相关性进行区分：动作步骤的可预测性及其概率。为了评估这些属性，我们使用了信息论的测量方法。动作步骤的可预测性通过其倒数条件熵来操作，条件熵结合了可能的动作步骤数量及其各自的概率。动作步骤的概率使用条件惊讶度来评估，惊讶度随着概率的降低而增加。参与者在一个动作观察范式中接受训练，该范式使用视频剪辑展示长度为 9-33 秒的序列，这些序列的动作步骤数量根据马尔可夫链的规则进行统计结构化。行为测试表明，参与者在潜意识中学习了这种统计结构，表明人类对这些概率规律很敏感。惊讶度（低概率）增强了前内顶叶皮层的 BOLD 信号。相比之下，高条件熵（即低可预测性）与背内侧前额叶皮层、眶额回和后内顶叶皮层的更高活动相关。此外，我们发现前海马体对条件熵的反应与学习程度之间存在相关性，即参与者学习结构的程度越大，海马体对条件熵的激活幅度就越大。研究结果表明，预测的两个方面可以分离：动作的可预测性反映在注意力焦点的自上而下调节中，这在额顶叶激活增加中很明显。相比之下，动作的概率取决于刺激本身的身份，导致顶叶皮层的自下而上驱动的处理成本。
