Cnudde Kelsey, van Hees Sophia, Brown Sage, van der Wijk Gwen, Pexman Penny M, Protzner Andrea B
Department of Psychology, University of Calgary, Calgary, AB T2N 1N4, Canada.
Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada.
Entropy (Basel). 2021 Mar 4;23(3):304. doi: 10.3390/e23030304.
Visual word recognition is a relatively effortless process, but recent research suggests the system involved is malleable, with evidence of increases in behavioural efficiency after prolonged lexical decision task (LDT) performance. However, the extent of neural changes has yet to be characterized in this context. The neural changes that occur could be related to a shift from initially effortful performance that is supported by control-related processing, to efficient task performance that is supported by domain-specific processing. To investigate this, we replicated the British Lexicon Project, and had participants complete 16 h of LDT over several days. We recorded electroencephalography (EEG) at three intervals to track neural change during LDT performance and assessed event-related potentials and brain signal complexity. We found that response times decreased during LDT performance, and there was evidence of neural change through N170, P200, N400, and late positive component (LPC) amplitudes across the EEG sessions, which suggested a shift from control-related to domain-specific processing. We also found widespread complexity decreases alongside localized increases, suggesting that processing became more efficient with specific increases in processing flexibility. Together, these findings suggest that neural processing becomes more efficient and optimized to support prolonged LDT performance.
视觉单词识别是一个相对轻松的过程，但最近的研究表明，所涉及的系统具有可塑性，有证据表明在长时间执行词汇判断任务（LDT）后行为效率会提高。然而，在这种情况下，神经变化的程度尚未得到描述。发生的神经变化可能与从最初由控制相关处理支持的费力表现，转变为由特定领域处理支持的高效任务表现有关。为了对此进行研究，我们重复了英国词汇项目，并让参与者在几天内完成16小时的LDT。我们在三个时间间隔记录脑电图（EEG），以跟踪LDT执行过程中的神经变化，并评估事件相关电位和脑信号复杂性。我们发现，在LDT执行过程中反应时间缩短，并且在整个EEG记录期间，通过N170、P200、N400和晚期正成分（LPC）振幅有神经变化的证据，这表明从控制相关处理转变为特定领域处理。我们还发现，在局部增加的同时，广泛的复杂性降低，这表明随着处理灵活性的特定增加，处理变得更加高效。总之，这些发现表明神经处理变得更加高效和优化，以支持长时间的LDT表现。
