Dynamic changes in network activations characterize early learning of a natural language

Abstract

Those who are initially exposed to an unfamiliar language have difficulty separating running speech into individual words, but over time will recognize both words and the grammatical structure of the language. Behavioral studies have used artificial languages to demonstrate that humans are sensitive to distributional information in language input, and can use this information to discover the structure of that language. This is done without direct instruction and learning occurs over the course of minutes rather than days or months. Moreover, learners may attend to different aspects of the language input as their own learning progresses. Here, we examine processing associated with the early stages of exposure to a natural language, using fMRI. Listeners were exposed to an unfamiliar language (Icelandic) while undergoing four consecutive fMRI scans. The Icelandic stimuli were constrained in ways known to produce rapid learning of aspects of language structure. After approximately 4 minutes of exposure to the Icelandic stimuli, participants began to differentiate between correct and incorrect sentences at above chance levels, with significant improvement between the first and last scan. An independent component analysis of the imaging data revealed four task-related components, two of which were associated with behavioral performance early in the experiment, and two with performance later in the experiment. This outcome suggests dynamic changes occur in the recruitment of neural resources even within the initial period of exposure to an unfamiliar natural language.

The nature of the language input affects brain activation during learning from a natural language

“…The initial phase of learning may be characterized by different cognitive strategies than employed at even slightly later times during the learning process, when the learner has begun to accumulate experience with a language corpus. This was demonstrated in a recent natural-language study of Icelandic sentence forms (Plante, Patterson, Dailey, Almryde, & Fridriksson, 2014). This study demonstrated that patterns of activation can be highly dynamic with learning, even within the initial 10 minutes of exposure to a new language.…”

“…Indeed, information-rich input may promote the recruitment of a neural network that is quite extensive, and highly dynamic over time (cf. Plante et al, 2014). Furthermore, because use of statistical information has resulted in rapid learning of words in prior artificial language studies, learners provided with input in which transitional probabilities predict word boundaries should show better learning than those provided with low predictability input.…”

“…Plante et al, 2014), we employed an analysis method well suited to understanding the pattern of coordination among brain regions as learning progresses. Independent Component Analysis (ICA) separates complex signals into simpler component signals that are statistically independent (see Beckman, 2012 or McKeown, Makeig, Brown, Jung, Kinderman, Bell, & Sejnowski, 1998 for overviews of the technique).…”

“…This is in contrast to use of a General Linear Model (GLM), in which all signal variation is tested against an ideal wave form intended to represent an a priori model of the shape and time course of the hemodynamic response. Because ICA separates the complex fMRI signal into component waveforms, this analysis can reveal correlated regions of activation, as well as separate time courses for activation that occurs within a single brain region (e.g., Karunanayaka, Schmithorst, Vannest, Szflarski, Plante, & Holland, 2010; Plante et al, 2014; Schmithorst, Holland, & Plante, 2006; Schmithorst, Holland, & Plante, 2007). Important to the design of this study, ICA is robust for detecting activation in response to auditory stimuli presented against background scanner noise (Langers & van Dijk, 2011).…”

Do infants retain the statistics of a statistical learning experience? Insights from a developmental cognitive neuroscience perspective

“…During auditory SL, the superior temporal gyrus-a region implicated in auditory perception, speech processing and forward speech prediction [52][53][54]-activates differentially to statistically predictable versus unpredictable transitions [55][56][57][58]. Auditory SL also activates the inferior frontal gyrus during test performance [55] and online learning of novel sequential structure [59] with some indication that this region may reflect recognition of accumulated statistical information [55,59]. There is also evidence that cortical regions activate to speech in three month olds [60].…”