bioRxivNeurosciencePreprint
Goals Drive Adaptive Analysis of Linguistic Features During Reading
Philip AlexiD, Julia SchwarziD, Benjamin W. TatleriD, Agnieszka E. KonopkaiD, Anastasia Klimovich‐GrayiD
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Abstract
Reading behaviour is sensitive to contextual demands. For example, when reading to study or answer questions, readers strategically allocate more attention to goal-relevant sections, read slower, and regress more frequently to extract specific information based on task demands. This suggests that readers flexibly adapt their strategies according to goals and situational constraints. However, the neurocognitive mechanisms supporting this flexibility remain unclear. To test this, we combined eye-tracking with EEG during naturalistic reading and examined how short-term reading goals modulated the neural tracking of hierarchical linguistic features at the orthographic (orthographic neighbourhood size), lexical (word frequency), and contextual-semantic (Surprisal) levels. Forty native English speakers read narratives in three goal conditions: Baseline (no goal), Proofreading (detect spelling errors), and Comprehension (attend to content). Fixation-aligned word-level EEG activity was modelled using multivariate temporal response function to estimate the strength of feature-specific neural tracking. Orthographic tracking was significantly enhanced during Proofreading relative to Baseline, and predicted better Proofreading accuracy. Conversely, stronger frequency tracking predicted better Comprehension accuracy. Contextual-semantic tracking was not modulated by task goal. These findings suggest that reading goals selectively bias neural processing towards linguistic features (sublexical vs. lexical) that support successful task performance, providing evidence for short-term functional plasticity in linguistic processing and supporting a flexible predictive processing account of goal-directed reading.
- organism: human
- assay: electronic health records
- research focus: differential expression
- approach: statistical model
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