Increased Neural Efficiency in Visual Word Recognition: Evidence from Alterations in Event-Related Potentials and Multiscale Entropy

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 i...

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Bibliographic Details
Published in:Entropy (Basel, Switzerland) Switzerland), 2021-03, Vol.23 (3), p.304
Main Authors: Cnudde, Kelsey, van Hees, Sophia, Brown, Sage, van der Wijk, Gwen, Pexman, Penny M, Protzner, Andrea B
Format: Article
Language:English
Subjects:
Brain research
brain signal complexity
Complexity
Efficiency
Electrodes
Electroencephalography
event-related potential (ERP)
lexical decision task (LDT)
Memory
multiscale entropy
Neural networks
Recognition
Semantics
visual word recognition
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Summary: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.
ISSN:1099-4300
1099-4300
DOI:10.3390/e23030304