1. Testing two-step models of negative quantification using a novel machine learning analysis of EEG.
- Author
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Ramotowska, S., Archambeau, K., Augurzky, P., Schlotterbeck, F., Berberyan, H.S., Van Maanen, L., and Szymanik, J.
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READING , *TASK performance , *RESEARCH funding , *T-test (Statistics) , *ELECTROENCEPHALOGRAPHY , *PHONOLOGICAL awareness , *NEUROSCIENCES , *MULTIVARIATE analysis , *DESCRIPTIVE statistics , *RESEARCH , *MACHINE learning , *REACTION time , *SEMANTICS , *COGNITION , *NONPARAMETRIC statistics - Abstract
The sentences "More than half of the students passed the exam" and "Fewer than half of the students failed the exam" describe the same set of situations, and yet the former results in shorter reaction times in verification tasks. The two-step model explains this result by postulating that negative quantifiers contain hidden negation, which involves an extra processing stage. To test this theory, we applied a novel EEG analysis technique focused on detecting cognitive stages (HsMM-MVPA) to data from a picture-sentence verification task. We estimated the number of processing stages during reading and verification of quantified sentences (e.g. "Fewer than half of the dots are blue") that followed the presentation of pictures containing coloured geometric shapes. We did not find evidence for an extra step during the verification of sentences with fewer than half. We provide an alternative interpretation of our results in line with an expectation-based pragmatic account. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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