Neurolinguistic & psycholinguistic investigations on evidentiality in Turkish


participants were provided with feedback and the opportunity to ask



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participants were provided with feedback and the opportunity to ask 
questions if they had any. Before the main eye-tracking experiment began, 
participants were reminded not to turn their gaze off the screen. When 
participants responded, the presentation of the next stimulus was initiated 
manually by the experimenter. Eye movements were monitored and 
sampled at a rate of 60 Hz, one frame per 16 ms, by a remote SMI eye-
tracking system positioned underneath the stimulus screen. The research 
was approved by the ethics committee of the University of Potsdam 
(application number 37/2011). 
5.2.5.
 
Analysis 
Three types of dependent variables were obtained and analyzed separately: 
accuracy of clicks, response times (RTs), and proportion of looks. The 
accuracy data were analyzed using generalized linear mixed-effects 
regression models, and the RTs data using linear mixed-effects regression 
models (Baayen, 2008). RTs that exceeded three standard deviations beyond 
the group means were excluded. Any responses made before the onset of 
critical verbs were rejected (around 1.5%). For the proportions of looks 
analysis, a time window of 2000 ms from the onset of critical verb was 
selected.
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The first 200 ms after verb onset were excluded from this time 
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The mean onset of the critical verbs was 4162 ms after each trial began, minus 
1000 ms silence, and the mean sentence offset time was as 5470 ms from the 
beginning of the sentences.


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window, since it takes about 200 ms to program and execute an eye 
movement (Rayner, Slowiaczek, Clifton, & Bertera, 1983). Proportion of 
looks was a binary variable indicating whether the participants fixated on 
the target picture or not. We excluded 0.92% of the data due to off-screen 
looks. The analyses were done on non-aggregated data. Participants' 
proportion of looks were analyzed with mixed-effects multilevel logistic 
regression models (Barr, 2008), using the ‘lme4’ and ‘multcomp’ statistical 
packages of R version 3.1.1 (R-Core-Team, 2012).

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