Michalareas, G. | bioRxiv Science

bioRxiv2019

The Anticipation of Events in Time

Humans use sensory input to anticipate events. The brains capacity to predict cues in time is commonly assumed to be modulated by two uncertainty parameters, the hazard rate (HR) of event probability and the uncertainty in time estimation, which increases with elapsed time. We investigate both assumptions by manipulating event probability density functions (PDF) in three sensory modalities. First we show, contrary to expectation, that perceptual systems use the reciprocal PDF - and not the HR - to model event probability density. Next we demonstrate that temporal uncertainty does not necessarily grow with elapsed time but also diminishes, depending on the event PDF. Finally we show that reaction time (RT) distributions comprise modality-specific and modality-independent components, the latter likely reflecting similarity in processing of probability density across sensory modalities. The results are consistent across vision, audition, and somatosensation, indicating that probability density is more fundamental than hazard rate in terms of the neural operations determining event anticipation and temporal uncertainty. Previous research identified neuronal activitity related to event probability in multiple levels of the cortical hierarchy such as early and higher sensory (V1, V4), association (LIP), motor and other areas. This work proposed that the elementary neuronal computation in estimation of probability across time is the HR. In contrast, our results suggest that the neurobiological implementation of probability estimation is based on a different, much simpler and more stable computation than HR: the reciprocal PDF of events in time.

neuroscience↗

bioRxiv2019

Dynamics of functional networks for syllable and word-level processing

Speech comprehension requires the ability to temporally segment the acoustic input for higher-level linguistic analysis. Oscillation-based approaches suggest that low-frequency auditory cortex oscillations track syllable-sized acoustic information and therefore emphasize the relevance of syllabic-level processing for speech segmentation. Most linguistic approaches, however, focus on mapping from acoustic-phonemic representations to the lexical level. How syllabic processing interacts with higher levels of speech processing, beyond segmentation, including the anatomical and neurophysiological characteristics of the networks involved, is debated. Here we investigate the effects of lexical processing and the interactions with (acoustic) syllable processing by examining MEG data recorded in two experiments using a frequency-tagging paradigm. Participants listened to disyllabic words presented at a rate of 4 syllables/sec. Two conjectures were evaluated: (i) lexical processing of words activates a network that interacts with syllable processing; and (ii) syllable transitions contribute to word-level processing. We show that lexical content activated a left-lateralized frontal and superior and middle temporal network and increased the interaction between left middle temporal areas and auditory cortex (phase-phase coupling). Mere syllable-transition information, in contrast, activated a bilateral superior-, middle temporal and inferior frontal network and increased the interaction between those areas. Word and syllable processing interacted in superior and middle temporal areas (cross-frequency coupling), whereas syllable tracking (cerebro-acoustic coherence) decreased when word-level information was present. The data provide a new perspective on speech comprehension by demonstrating a contribution of an acoustic-syllabic to lexical processing route.\n\nSignificance statementThe comprehension of speech requires integrating information at multiple time scales, including phonemic, syllabic, and word scales. Typically, we think of decoding speech in the service of recognizing words as a process that maps from phonemic units to words. Recent neurophysiological evidence, however, has highlighted the relevance of syllable-sized chunks for segmenting speech. Is there more to recognizing spoken language? We provide neural evidence for brain network dynamics that support an interaction of lexical with syllable-level processing. We identify cortical networks that differ depending on whether lexical-semantic information versus low-level syllable-transition information is processed. Word- and syllable-level processing interact within MTG and STG. The data enrich our understanding of comprehension by implicating a mapping from syllabic to lexical representations.

neuroscience↗