Integrating speech in time depends on temporal expectancies and attention

Publication date: Available online 19 May 2017
Source:Cortex
Author(s): Mathias Scharinger, Johanna Steinberg, Alessandro Tavano
Sensory information that unfolds in time, such as in speech perception, relies on efficient chunking mechanisms in order to yield optimally-sized units for further processing. Whether or not two successive acoustic events receive a one-unit or a two-unit interpretation seems to depend on the fit between their temporal extent and a stipulated temporal window of integration. However, there is ongoing debate on how flexible this temporal window of integration should be, especially for the processing of speech sounds. Furthermore, there is no direct evidence of whether attention may modulate the temporal constraints on the integration window. For this reason, we here examine how different word durations, which lead to different temporal separations of sound onsets, interact with attention. In an Electroencephalography (EEG) study, participants actively and passively listened to words where word-final consonants were occasionally omitted. Words had either a natural duration or were artificially prolonged in order to increase the separation of speech sound onsets. Omission responses to incomplete speech input, originating in left temporal cortex, decreased when the critical speech sound was separated from previous sounds by more than 250 ms, i.e. when the separation was larger than the stipulated temporal window of integration (125-150 ms). Attention, on the other hand, only increased omission responses for stimuli with natural durations. We complemented the event-related potential (ERP) analyses by a frequency-domain analysis on the stimulus presentation rate. Notably, power of stimulation frequency showed the same duration and attention effects than the omission responses. We interpret these findings on the background of existing research on temporal integration windows and further suggest that our findings may be accounted for within the framework of predictive coding.



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