2.Data

Detailed Metadata

• MPI_Bundle
  • Title : 2.Data
  • DataType : EEG data
  • DataType : Statistical data
  • DataType : Other
  • ResearchField : Neurobiology
  • ResearchField : Psycholinguistics
  • Date : 2022-02-28
  • Description
    • Description : Bundle contains raw EEG recordings, pre-processed EEG data and data used for cluster-based permutation analysis. The bundle further contains the Matlab scripts used to carry out pre-processing and cluster-based permutation analysis.
  • Contributor
    • Name : Florian Hintz
    • Role : author
    • Role : researcher
    • EmailAddress : florian.hintz@mpi.nl
  • Institution
    • Department : Psychology of Language
    • Organisation : Max Planck Institute for Psycholinguistics
  • Project
    • ProjectName : Predictors of word recognition in noise
    • ProjectDescription : Successful spoken-word recognition relies on an interplay between lexical and sublexical processing. Previous research demonstrated that listeners readily shift between more lexically-biased and more sublexically-biased modes of processing in response to the situational context in which language comprehension takes place. Recognizing words in the presence of background noise reduces the perceptual evidence for the speech signal and – compared to the clear – results in greater uncertainty. It has been proposed that, when dealing with greater uncertainty, listeners rely more strongly on sublexical processing. The present study tested this proposal using behavioral and electroencephalography (EEG) measures. We reasoned that such an adjustment would be reflected in changes in the effects of variables predicting recognition performance with loci at lexical and sublexical levels, respectively. We presented native speakers of Dutch with words featuring substantial variability in (1) word frequency (locus at lexical level), (2) phonological neighborhood density (loci at lexical and sublexical levels) and (3) phonotactic probability (locus at sublexical level). Each participant heard each word in noise (presented at one of three signal-to-noise ratios) and in the clear and performed a two-stage lexical decision and transcription task while EEG was recorded. Using linear mixed-effects analyses, we observed behavioral evidence that listeners relied more strongly on sublexical processing when speech quality decreased. Mixed-effects modelling of the EEG signal in the clear condition showed that sublexical effects were reflected in early modulations of ERP components (e.g., within the first 300 ms post word onset). In noise, EEG effects occurred later and involved multiple regions activated in parallel. Taken together, we found evidence – especially in the behavioral data – supporting previous accounts that the presence of background noise induces a stronger reliance on sublexical processing.
  • Resource
    • Size : 67621708574