Burgess Jed D, Major Brendan P, McNeel Claire, Clark Gillian M, Lum Jarrad A G, Enticott Peter G
Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, VIC, Australia.
Front Hum Neurosci. 2019 Jul 4;13:215. doi: 10.3389/fnhum.2019.00215. eCollection 2019.
Sensory experiences, such as sound, often result from our motor actions. Over time, repeated sound-producing performance can generate sensorimotor associations. However, it is not clear how sensory and motor information are associated. Here, we explore if sensory prediction is associated with the formation of sensorimotor associations during a learning task. We recorded event-related potentials (ERPs) while participants produced index and little finger-swipes on a bespoke device, generating novel sounds. ERPs were also obtained as participants heard those sounds played back. Peak suppression was compared to assess sensory prediction. Additionally, transcranial magnetic stimulation (TMS) was used during listening to generate finger-motor evoked potentials (MEPs). MEPs were recorded before and after training upon hearing these sounds, and then compared to reveal sensorimotor associations. Finally, we explored the relationship between these components. Results demonstrated that an increased positive-going peak (e.g., P2) and a suppressed negative-going peak (e.g., N2) were recorded during action, revealing some sensory prediction outcomes (P2: = 0.050, = 0.208; N2: = 0.001, = 0.474). Increased MEPs were also observed upon hearing congruent sounds compared with incongruent sounds (i.e., associated to a finger), demonstrating precise sensorimotor associations that were not present before learning (Index finger: < 0.001, = 0.614; Little finger: < 0.001, = 0.529). Consistent with our broad hypotheses, a negative association between the MEPs in one finger during listening and ERPs during performance of the other was observed (Index finger MEPs and Fz N1 action ERPs; = -0.655, = 0.003). Overall, data suggest that predictive mechanisms are associated with the fine-tuning of sensorimotor associations.
诸如声音之类的感官体验通常源于我们的运动行为。随着时间的推移，重复的发声行为会产生感觉运动关联。然而，目前尚不清楚感觉信息和运动信息是如何关联的。在此，我们探究在学习任务中感觉预测是否与感觉运动关联的形成有关。我们记录了参与者在一个定制设备上进行食指和小指滑动时产生新声音的事件相关电位（ERP）。当参与者听到回放的这些声音时，也获取了ERP。通过比较峰值抑制来评估感觉预测。此外，在聆听过程中使用经颅磁刺激（TMS）来产生手指运动诱发电位（MEP）。在训练前后听到这些声音时记录MEP，然后进行比较以揭示感觉运动关联。最后，我们探究了这些成分之间的关系。结果表明，在动作过程中记录到正向峰值增加（例如P2）和负向峰值抑制（例如N2），揭示了一些感觉预测结果（P2： = 0.050， = 0.208；N2： = 0.001， = 0.474）。与不一致的声音（即与手指无关的声音）相比，听到一致的声音时也观察到MEP增加，表明学习前不存在的精确感觉运动关联（食指： < 0.001， = 0.614；小指： < 0.001， = 0.529）。与我们的总体假设一致，观察到在聆听过程中一个手指的MEP与另一个手指动作时的ERP之间存在负相关（食指MEP和Fz N1动作ERP； = -0.655， = 0.003）。总体而言，数据表明预测机制与感觉运动关联的微调有关。
