Specifically, these studies investigated the processing of structured syllable sequences in healthy participants ( Friederici et al., 2006 Bahlmann et al., 2008 Mueller et al., 2012 Uddén and Bahlmann, 2012). Previous neuroimaging, electrophysiological and behavioral studies used AG learning paradigms to elucidate the human capacity to detect regularities in speech sequences. In previous studies, AG learning paradigms were mainly used as a model for syntactical and phonological processing ( Uddén, 2012). From the language acquisition perspective, AG learning represents one of a few language acquisition paradigms suitable for adult subjects. In the test phase, participants are usually presented with novel sequences and need to classify them as correct or incorrect according to the underlying rules acquired in the learning phase (cf. During learning, participants are exposed to a set of syllable or letter sequences and during testing, their abilities to detect the underlying rule system are assessed ( Lobina, 2014). These paradigms usually include a learning phase and a test phase.

One major advantage of AG learning paradigms relative to the study of natural language stimuli is that they allow for exploring the processing of rule-based regularities in a controlled way and unconfounded by semantic processes and prior knowledge ( Fitch and Friederici, 2012). It was thus argued that sequence learning mechanisms are important in language acquisition ( Christiansen and Chater, 2015).Īrtificial grammar (AG) learning paradigms provide a means to study sequence learning during language acquisition and its evolution both in human populations (children and adults) and in non-human species (e.g., primates or birds Fitch and Friederici, 2012). Converging evidence from genetic, non-human primate and cognitive neuroscience studies indicates that language and sequence learning have considerable overlap in the underlying neural mechanisms. Different elements of speech, such as phonemes, syllables, or words, are sequentially organized into speech streams, following language-specific constraints (or rules). Successful speech processing and language learning rests on efficient processing of sequential auditory information and establishing relationships between speech elements ( Hasson and Tremblay, 2015). Based on these activity patterns and the phonological strategies for rule acquisition and application, we argue that successful learning and processing of complex phonological rules in our paradigm is mediated via a fronto-parietal network for phonological processes. These areas were previously associated with phonological segmentation, phoneme comparison, and verbal working memory. Relative to non-learners, successful learners showed increased task-related activity in a fronto-parietal network of brain areas encompassing the left lateral premotor cortex as well as bilateral superior and inferior parietal cortices during both learning and rule application. As an outcome of four alternating learning and test fMRI sessions, participants split into successful learners and non-learners. We expected that successful learning and rule application would require phonological segmentation and phoneme comparison. During functional magnetic resonance imaging (fMRI), participants were exposed to a set of syllable sequences with an underlying phonological rule system, known to ensure performance differences between participants. incorrect sequences or between different levels of sequence complexity, here the focus is on the neural foundation of the actual learning success. While previous neuroimaging studies either focused on functional differences between the processing of correct vs. In the present study, we investigated the functional underpinnings of learning phonological regularities in auditorily presented syllable sequences. Artificial grammar learning paradigms explore pattern recognition abilities in a set of structured sequences (i.e., of syllables or letters).

Sensitivity to regularities plays a crucial role in the acquisition of various linguistic features from spoken language input.

3Institute of Cognitive Science, University of Osnabrück, Osnabrück, Germany.

2International Laboratory for Brain, Music and Sound Research, Department of Psychology, University of Montreal, Montreal, QC, Canada.

1Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.

Dariya Goranskaya 1*, Jens Kreitewolf 1,2, Jutta L.

Phonological processes