Cognitive Neuroimaging Unit, CEA DRF/JOLIOT, INSERM, Université Paris-Sud, Université Paris-Saclay, NeuroSpin Centre, Gif-sur-Yvette, France.
Université de Paris, Paris, France.
Nat Hum Behav. 2022 Aug;6(8):1087-1103. doi: 10.1038/s41562-021-01259-6. Epub 2022 May 2.
Detecting and learning temporal regularities is essential to accurately predict the future. A long-standing debate in cognitive science concerns the existence in humans of a dissociation between two systems, one for handling statistical regularities governing the probabilities of individual items and their transitions, and another for handling deterministic rules. Here, to address this issue, we used finger tracking to continuously monitor the online build-up of evidence, confidence, false alarms and changes-of-mind during sequence processing. All these aspects of behaviour conformed tightly to a hierarchical Bayesian inference model with distinct hypothesis spaces for statistics and rules, yet linked by a single probabilistic currency. Alternative models based either on a single statistical mechanism or on two non-commensurable systems were rejected. Our results indicate that a hierarchical Bayesian inference mechanism, capable of operating over distinct hypothesis spaces for statistics and rules, underlies the human capability for sequence processing.
检测和学习时间规律对于准确预测未来至关重要。认知科学中长期存在的一个争论涉及人类是否存在两种系统的分离，一种用于处理个体项目及其转换的概率的统计规律，另一种用于处理确定性规则。在这里，为了解决这个问题，我们使用手指跟踪来连续监测序列处理过程中证据、信心、误报和思维改变的在线积累。所有这些行为方面都严格符合具有统计和规则不同假设空间的分层贝叶斯推理模型，但通过单一概率货币联系在一起。基于单一统计机制或两个不可通约系统的替代模型被拒绝。我们的结果表明，分层贝叶斯推理机制能够在统计和规则的不同假设空间上运行，是人类序列处理能力的基础。
