Cracking the Language Code: Neural Mechanisms Underlying Speech Parsing
ABSTRACT Word segmentation, detecting word boundaries in continuous speech, is a critical aspect of language learning. Previous research in infants and adults demonstrated that a stream of speech can be readily segmented based solely on the statistical and speech cues afforded by the input. Using functional magnetic resonance imaging (fMRI), the neural substrate of word segmentation was examined on-line as participants listened to three streams of concatenated syllables, containing either statistical regularities alone, statistical regularities and speech cues, or no cues. Despite the participants' inability to explicitly detect differences between the speech streams, neural activity differed significantly across conditions, with left-lateralized signal increases in temporal cortices observed only when participants listened to streams containing statistical regularities, particularly the stream containing speech cues. In a second fMRI study, designed to verify that word segmentation had implicitly taken place, participants listened to trisyllabic combinations that occurred with different frequencies in the streams of speech they just heard ("words," 45 times; "partwords," 15 times; "nonwords," once). Reliably greater activity in left inferior and middle frontal gyri was observed when comparing words with partwords and, to a lesser extent, when comparing partwords with nonwords. Activity in these regions, taken to index the implicit detection of word boundaries, was positively correlated with participants' rapid auditory processing skills. These findings provide a neural signature of on-line word segmentation in the mature brain and an initial model with which to study developmental changes in the neural architecture involved in processing speech cues during language learning.
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ABSTRACT: Coding for the degree of disorder in a temporally unfolding sensory input allows for optimized encoding of these inputs via information compression and predictive processing. Prior neuroimaging work has examined sensitivity to statistical regularities within single sensory modalities and has associated this function with the hippocampus, anterior cingulate, and lateral temporal cortex. Here we investigated to what extent sensitivity to input disorder, quantified by Markov entropy, is subserved by modality-general or modality-specific neural systems when participants are not required to monitor the input. Participants were presented with rapid (3.3 Hz) auditory and visual series varying over four levels of entropy, while monitoring an infrequently changing fixation cross. For visual series, sensitivity to the magnitude of disorder was found in early visual cortex, the anterior cingulate, and the intraparietal sulcus. For auditory series, sensitivity was found in inferior frontal, lateral temporal, and supplementary motor regions implicated in speech perception and sequencing. Ventral premotor and central cingulate cortices were identified as possible candidates for modality-general uncertainty processing, exhibiting marginal sensitivity to disorder in both modalities. The right temporal pole differentiated the highest and lowest levels of disorder in both modalities, but did not show general sensitivity to the parametric manipulation of disorder. Our results indicate that neural sensitivity to input disorder relies largely on modality-specific systems embedded in extended sensory cortices, though uncertainty-related processing in frontal regions may be driven by both input modalities. Hum Brain Mapp, 2013. © 2013 Wiley Periodicals, Inc.Human Brain Mapping 04/2014; 35(4). DOI:10.1002/hbm.22238 · 6.92 Impact Factor
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ABSTRACT: Neuroimaging research has identified several brain systems sensitive to statistical regularities within environmental input. However, the continuous input impinging on sensory organs is rarely stationary and its degree of regularity may itself change over time. The goals of the current fMRI study were to identify systems sensitive to changes in statistical regularities within an ongoing stimulus, and determine to what extent sensitivity to such changes depends on intentional monitoring of order. We predicted that changes in regularity would be coded for in systems previously associated with statistical coding (hippocampus and middle frontal regions) or event segmentation (posterior medial regions). Participants listened to a rapid train of four different tones whose order levels fluctuated over time. In an active task, participants monitored the tones and indicated when they perceived a change in regularity; in a passive task, they performed a concurrent visuo-motor task and could ignore the auditory input. Behavioral responses in the active task were used to define points of consensus between participants regarding changes in regularity. Activity in 7.5s epochs that preceded these order-change points was contrasted with activity during matched-length epochs where no participant indicated a change in order. We found that brain regions differentiating these two types of epochs matched those identified in prior research as mediating event segmentation in narratives and movies. These consisted mainly of medial posterior parietal and occipital regions, with limited involvement of temporal and lateral frontal cortices and no hippocampal involvement. In both tasks, order-change epochs were associated with a higher BOLD response than stable-order epochs, but the specific regions showing this pattern varied across tasks. We suggest that partitioning an input stream on the basis of statistical shifts constitutes a basic neural function underlying the ability to segment both semantic and non-semantic inputs. We further discuss the implications of these findings for neurobiological theories of statistical coding and event segmentation.NeuroImage 08/2012; 63(3):1730-42. DOI:10.1016/j.neuroimage.2012.08.017 · 6.13 Impact Factor
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ABSTRACT: Recent formalizations suggest that the human brain codes for the degree of order in the environment and utilizes this knowledge to optimize perception and performance in the immediate future. However, the neural bases of how the brain spontaneously codes for order are poorly understood. It has been shown that activity in lateral temporal cortex and the hippocampus is linearly correlated with the order of short visual series under tasks requiring attention to the input and when series order is invariant over time. Here, we examined if sensitivity to order is manifested in both linear and non-linear BOLD response profiles, quantified the degree to which order-sensitive regions operate as a functional network, and evaluated these questions using a paradigm in which performance of the ongoing task could be completed without any attention to the stimulus whose order was manipulated. Participants listened to a 10-minute sequence of tones characterized by non-stationary order, and fMRI identified cortical regions sensitive to time-varying statistical features of this input. Activity in perisylvian regions was negatively correlated with input diversity, quantified via Shannon's Entropy. Activity in ventral premotor, lateral temporal, and insular regions was correlated linearly, parabolically, or via a step-function with the strength of transition constraints in the series, quantified via Markov Entropy. Granger-causality analysis revealed that order-sensitive regions form a functional network, with regions showing non-linear responses to order associated with more afferent connectivity than those showing linear responses. These findings identify networks that spontaneously code and respond to diverse aspects of order via multiple response profiles, and that play a central role in generating and gating predictive neural activity.NeuroImage 01/2012; 60(2):991-1005. DOI:10.1016/j.neuroimage.2012.01.041 · 6.13 Impact Factor