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Project B1 - Language representations in prosodic processing
PI: Prof. Dr. Ulrike Domahs, Prof. Dr. Christina Kauschke, Prof. Dr. Mathias Scharinger
Ph.D.-student: Jonas Gerards
Research context
Recent theories in cognitive science center the prediction of upcoming states of the internal and external world and argue that prediction is the major underlying mechanism for the brain’s functioning („predictive processing“, Hohwy, 2020; Clark, 2013). Similarly, anticipatory processes are part of language processing (Kamide, 2008). Spoken language, in addition to segmental and contextual information, also contains suprasegmental information, which may denote differences in meaning in some languages, but is generally accepted to play a role in parsing and segmentation of the speech signal and marking of its sub-units. The cortical basis for the concerted processing of information are neuronal oscillations, which in turn have been connected to language processing in multiple ways (Buszaki & Vöröslakos, 2023; Meyer, 2018). Furthermore, suprasegmental information is not only relevant when processing larger linguistics units, but is already important in single word processing (see Höhle et al., 2009, for an overview of acquisition). Prosodic competency in L1 also influences the processing of suprasemgental information in L2 (Mennen & De Leeuw, 2014).
Current dissertation project
The first part of the current project adapts the study by Jesse et al. (2017) with German L2-learners of English and investigates the influence of L1 on single word representations of prosody in L2. In the experiment, English, polysyllabic words of Romance origin are presented and the processing of these words is then examined via eye-tracking and EEG. This not only enables the collection of eye movement timings towards the correct word, but also an exploratory account of neuronal differences in word processing based on oscillatory phenomena.
Aims
The goal of the project is an investigation into the neuronal modification of prosodic representation by L2 learning. Results will also be compared existing findings from L1 acquisition.
Methods
The project uses neuro- and psycho-linguistic research methods in its approach to language representations. Eye-tracking is a psycho-linguistic methods that is useful for the study of unconscious and anticipatory processes (Kamide, 2008). The project’s central neuro-linguistic method is electroencephalography (EEG). By recording the sum total of postsynaptic potentials at the scalp, EEG allows inferences on the activity of neuronal populations within the brain. The high temporal resolution of the method favors a step-wise identification of the correlates of language processing, and is furthermore advantageous for the analysis of oscillatory phenomena. The latter are indicative of synchronous activity of neurons, which is further relevant for language processing (Buszaki & Vöröslakos, 2023; Meyer, 2018). There is the additional possibility of using data-driven analyses like multivariate pattern analysis (MVPA) or representational similarity analysis (RSA; Grootswagers et al., 2017, for an overview).
Combining eye-tracking with EEG is not without its problems, as eye movements induce potential differences that are recorded by the EEG sensors, but which are regarded as noise during EEG analysis. Whileit is common practice in EEG research to reduce eye movements to a minimum by using appropriate experimental designs, recently, there is a move towards the investigation of neuronal processes in naturalistic environments. As part of this trend, there are recommendations to clean EEG data of the coinciding eye movements (Dimigen, 2020), which are also used in language research (Huizeling et al., 2023).
In addition, the project reflects its inherent assumptions and its results in light of ongoing debates within the research group and within the field (van der Burght et al., 2022) in the context of a critical neuroscience (Choudhury & Slaby, 2012).
Preliminary work
In Gerards (2023), prosodic representation were (among others) investigated during the third year of life in a neurophysiological study. Based on a re-analysis of this longitudinal data, a manuscript is being written, which outlines the typical development of lexical representation during this period.
Relations to other projects
There is a connection in methodology to projects A1, A2, A3, and C3, which are also using neurophysiological methods in their study of language representations. Additionally, the current project is related in substance to projects A3 and B3, which also study prosodic representations.
References
van der Burght, C. L., Friederici, A. D., Maran, M., Papitto, G., Pyatigorskaya, E., Schroën, J., ... & Zaccarella, E. (2022). Cleaning up the Brickyard: How Theory and Methodology Affect Experimental Outcome in Cognitive Neuroscience of Language.**
Buzsáki, G., & Vöröslakos, M. (2023). Brain rhythms have come of age. /Neuron/, /111/(7), 922-926.**
Choudhury, S., & Slaby, J. (Eds.). (2016). /Critical neuroscience: A handbook of the social and cultural contexts of neuroscience/. John Wiley & Sons.**
Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. /Behavioral and brain sciences/, /36/(3), 181-204.
Dimigen, O. (2020). Optimizing the ICA-based removal of ocular EEG artifacts from free viewing experiments. /NeuroImage/, /207/, 116117.
Gerards, J. (2023). The Development of Lexical Representation During the Third Year of Life: A Longitudinal EEG Study [Master’s Thesis, Philipps-Universität Marburg].
Grootswagers, T., Wardle, S. G., & Carlson, T. A. (2017). Decoding dynamic brain patterns from evoked responses: A tutorial on multivariate pattern analysis applied to time series neuroimaging data. /Journal of cognitive neuroscience/, /29/(4), 677-697.
Hohwy, J. (2020). New directions in predictive processing. /Mind & Language/, /35/(2), 209-223.
Höhle, B. (2009). Bootstrapping mechanisms in first language acquisition.
Huizeling, E., Alday, P. M., Peeters, D., & Hagoort, P. (2023). Combining EEG and 3D-eye-tracking to study the prediction of upcoming speech in naturalistic virtual environments: A proof of principle. /Neuropsychologia/, /191/, 108730.
Jesse, A., Poellmann, K., & Kong, Y. Y. (2017). English listeners use suprasegmental cues to lexical stress early during spoken-word recognition. /Journal of Speech, Language, and Hearing Research/, /60/(1), 190-198.
Kamide, Y. (2008). Anticipatory processes in sentence processing. /Language and Linguistics Compass/, /2/(4), 647-670.
Mennen, I., & De Leeuw, E. (2014). Beyond segments: Prosody in SLA. /Studies in Second Language Acquisition/, /36/(2), 183-194.
Meyer, L. (2018). The neural oscillations of speech processing and language comprehension: state of the art and emerging mechanisms. /European Journal of Neuroscience/, /48/(7), 2609-2621.