Signal analysis for linguists

The objective is to introduce the students to the basics of signal analysis in order to be able to understand and interpret results of quantitative measurements of various types of linguistic signals in the form of time series, while concentrating on particular markers of time series that have been proven to carry linguistic significance. The course prepares the students for potential engagement in experimental investigations of language using sophisticated brain imaging technologies such as functional MRI, Event Related Potentials (EEG, MEG) etc.

The acquired competences are:
• Ability to interpret various types of signal analysis pertaining to psycholinguistics and neurolinguistics
• Ability to understand and critically evaluate present-day literature in experimental psycholinguistics and neuroscience of language
• The ability to critically relate the results of experimental investigations of language, while being informed of the possibilities and limitations of the current methods of analyzing linguistic signal.

Introduction to phonology, Introduction to psycholinguistics, Uvod v kognitivne znanosti

The course will focus on at least two of the following types of signal analysis:
• acoustic signal analysis (as relevant to experimental phonetics);
• electrophysiological signal analysis in electroencephalograms (EEG), particularly within the paradigm of Event Related Potentials
• signal analysis in magnitoencephalography (MEG)
• visuo-spatial signal analysis as employed in fMRI brain imaging research on language.

-Knowledge of the neurological markers of specific kinds of linguistic (grammar-related) behaviour
-Knowledge of the specific areas in experimental linguistics that rely on signal analysis

• Tohyama M., and T. Koike. 1998. Fundamentals of Acoustic Signal Processing. Elsevier. E-version
• Allefeld, C., P. beim Graben und J. Kurths. 2008. Advanced Methods of Electrophysiological Signal Analysis and Symbol Grounding: Dynamical Systems Approaches to Language. Nova Science Publishers Inc. Catalogue
• Russell A. Poldrack, Jeanette A. Mumford und Thomas E. Nichols. 2011. Handbook of Functional MRI Data Analysis. Cambridge: Cambridge University Press. Catalogue
• Related to the specific content of the course (articles from scientific journals within the framework of neurolinguistics and psycholinguistics)

• Active participation at the lectures (50%), • final project on the topic connected with the course content (50%).

Full professor of Language Science at the University of Reading.

Bibliography:

1. Schmid S, Saddy D, Franck J. 2023. Finding Hierarchical Structure in Binary Sequences: Evidence from Lindenmayer Grammar Learning. Cognitive Science 47(1):e13242. doi: 10.1111/cogs.13242. PMID: 36655988.
2. Saddy, D. 2020. Syntax and uncertainty. In: Gallego, Á. and Martin, R. , (eds.) Language, Syntax, and the Natural Sciences. Cambridge University Press , Cambridge. pp. 316-332.
3. Vender, M., Krivochen, D. G., Compstella, A., Phillips, B., Delfitto, D. and Saddy, D. (2020) Disentangling sequential from hierarchical learning in artificial grammar learning: evidence from a modified Simon task. PLoS ONE, 15 (5). e0232687. ISSN 1932-6203 doi: https://doi.org/10.1371/journal.pone.0232687
4. Alswaihli, J., Potthast, R., Bojak, I., Saddy, D. and Hutt, A. (2018) Kernel reconstruction for delayed neural field equations. The Journal of Mathematical Neuroscience, 8(3). https://doi.org/10.1186/s13408-018-0058-8.
5. Williams, N, Nasuto, S.J., Saddy, J.D. (2015) Method for exploratory cluster analysis and visualisation of single-trial ERP ensembles, Journal of Neuroscience Methods (2015), http://dx.doi.org/10.1016/j.jneumeth.2015.02.007