Decomposing predictability: Semantic feature overlap between words and the dynamics of reading for meaning
Markus J. Hofmann, Mareike A. Kleemann, Andre Roelke, Christian, Vorstius, and Ralph Radach

TL;DR
This study employs a computational approach to explore how semantic feature overlap influences reading dynamics, revealing that semantic associations affect eye movements and processing times during sentence comprehension.
Contribution
It introduces a novel method controlling for direct word associations to isolate semantic feature overlap effects on reading, advancing understanding of semantic processing in natural reading.
Findings
Verb-noun overlap reduces fixation durations.
Adjective-noun overlap shortens go-past durations.
Semantic feature overlap influences eye-movement patterns during reading.
Abstract
The present study uses a computational approach to examine the role of semantic constraints in normal reading. This methodology avoids confounds inherent in conventional measures of predictability, allowing for theoretically deeper accounts of semantic processing. We start from a definition of associations between words based on the significant log likelihood that two words co-occur frequently together in the sentences of a large text corpus. Direct associations between stimulus words were controlled, and semantic feature overlap between prime and target words was manipulated by their common associates. The stimuli consisted of sentences of the form pronoun, verb, article, adjective and noun, followed by a series of closed class words, e. g. "She rides the grey elephant on one of her many exploratory voyages". The results showed that verb-noun overlap reduces single and first fixation…
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Taxonomy
TopicsAdvanced Text Analysis Techniques · Reading and Literacy Development · Text Readability and Simplification
