You will explore the barriers that have limited the uptake of computational methods in cognitive linguistics, such as steep learning curves, reliance on Big Data, and the demand for exact instruction. Through a rich mix of the “history of ideas” and hands-on examples, the workshop will demonstrate how computational models can be tailored to address linguistic complexity, delivering empirically testable predictions and actionable insights. During the hands-on sessions you will learn to use computational models based on research on learning from psychology. After an introduction to the principles of learning, we will demonstrate how error-correction models can be used for the analysis of linguistic data, using some of our own work. Using findings from work on L1 and L2, we will teach you how annotated corpus data can be used in the computational models, and how computational modelling can be used to generate hypotheses that can be tested in experimental or classroom settings. The workshop is tailored for participants with no programming experience, and make use of our cloud computation infrastructure. It includes hands-on work with existing data while also setting aside time for participants to apply what they have learned to their own data.
 
Level of participation: Beginners to Advanced
Software requirements: None, we will be using our cloud computing interface but the training can also be run in Python

Given a text of disputed or questioned authorship -- as is common in historical, literary, political, and forensic contexts -- a range of methods have been developed for inferring information about the author of that text through computational linguistic analysis. In this workshop, we introduce computational methods for linguistic authorship analysis. On day one, we introduce the field of linguistic authorship analysis, including defining different types of authorship problems, including attribution, verification, and profiling, and discussing the differences between manual and computational approaches to resolving these tasks. We then introduce methods for geolinguistic profiling, which involves predicting the geographic background of an author, with a focus on the German language. On day two, we discuss the task of authorship attribution, which involves selecting the most likely author of an anonymous text from a set of candidate authors, using large language models. We discuss how to fine-tune authorial large language models for authorship analysis and demonstrate this methodology using a range of standard English-language benchmarking corpora.

Level: Postgraduate students and higher
Software: We will be presenting techniques implemented in R/R Studio (geolinguistic profiling) and Python (authorship attribution) and will provide notebooks that students are welcome to follow along with during our sessions; however, students will not be required to run code themselves.

The workshop will present an overview of the (psycho)linguistic application of data-driven models trained on data of language usage. The issue will be addressed from both a historical and a methodological perspective, with a discussion ranging from traditional distributional approaches to modern large language models. The talk will highlight the continuity between such different modelling traditions, as well as their viability as instruments for scientific investigation and their degree of cognitive plausibility. The talk will mostly focus on systems trained on text corpora, but the possibility of using other types of data sources (such as databases of annotated images) will be also explored. Examples of empirical works will be provided, combining the analysis of such models with psychological and linguistic issues.

Level: Suitable for everyone
Software: N/A

Large language models (LLMs) based on the transformer architecture are deep neural networks trained on textual corpora for general-purpose language understanding and generation. This workshop will introduce attendees to popular Python libraries devoted to interrogating LLMs on measures of language processing and representation. Specifically, attendees will learn to obtain representations of linguistic units at various level of granularity (sub-word tokens, words, sentences) and to probe the impact of context on these representations (e.g., how the meaning of ambiguous words is modulated by the sentences in which they occur). Then, attendees will learn to extract LLMs’ predictability measures, which will be applied to obtain estimates of sentence grammaticality and semantic plausibility. Finally, the workshop will introduce the basic tools to probe the connection between language and vision with vision-language models. The workshop will cover foundational LLMs of different families (i.e., BERT encoder language models, GPT decoder language models, CLIP multimodal models). After the workshop, attendees will know how to use basic Python resources to start working independently with LLMs.

Level: Theoretically, the workshop assumes a very basic knowledge of large language models. Thus, while some theoretical concepts will be reviewed throughout the workshop, we highly recommend attendees to follow the theoretical lectures on LLMs. Practically, the workshop will not assume experience with Python; however, unexperienced attendees are encouraged to participate in the “Python for linguists” sessions in order to familiarize themselves with the coding environment.
Software: The workshop will be carried out on popular cloud-based coding platforms and thus will not require installing software on attendees' local machines. Since Google Colab will most likely be the cloud-based platform of choice, a Google account will be required.

This hands-on workshop is designed for students and researchers in psychology, linguistics, cognitive science, and related fields who are interested in applying computational methods to language research. The workshop introduces key computational approaches for testing different theories of child word learning using Python. By the end of the workshop, participants will have a foundation for integrating computational modelling into their research workflow, along with Jupyter notebooks covering all workshop activities (input preprocessing, implementation, output visualisation, and evaluation using real and simulated data). The workshop focuses on two influential theories in language acquisition: transitional probability, which explains how infants discover words in fluent speech using statistical cues, and chunking, which captures how children build a vocabulary through exposure to parental input. Participants will implement these theories using two major approaches: first, by testing a transitional probability model and aligning its output with infant behavioural data, and second, by running simulations on conversational corpus data to examine how manipulating a chunking-based learning system and its input affects vocabulary acquisition. Participants should bring their own laptop and can use Google Colab without installing any software. All that is needed is a modern web browser (e.g., Chrome, Safari), a Google account, and a stable internet connection. However, in case of internet dropouts, we recommend that participants install Python (version 3.8+) and Jupyter Notebook. A list of required packages, setup instructions, and further details about the workshop are available here.
 
Level: This hands-on workshop is designed for students and researchers in psychology, linguistics, cognitive science, and related fields who are interested in applying computational methods to language research.
Software: Participants should bring their own laptop and can use Google Colab without installing any software. All that is needed is a modern web browser (e.g., Chrome, Safari), a Google account, and a stable internet connection. However, in case of internet dropouts, we recommend that participants install Python (version 3.8+) and Jupyter Notebook. A list of required packages, setup instructions, and further details about the workshop are available here.
 

This workshop will introduce fundamental methods for analysing multimodal signals in conversation. On the first day, we will discuss how to process kinematic information (i.e., how to extract key body points) and automatically transcribe and align speech from dialogue video recordings. On the second day, we will build on this knowledge to develop methods that allow us to automatically detect gestures using speech and kinematic features. Each workshop day will consist of a short presentation of at most 45 minutes, followed by hands-on practical exercises and discussion.

Level: The workshop is suitable for anyone with an interest in multimodality. We expect students to have basic programming skills, preferably in Python.
Software: We plan to use the following software: Vscode, Python (e.g., Miniconda), MediaPipe, and Whisper-X. You do not need to install this software beforehand: we will help you out during the workshop.

In this workshop we will play with simulation models of the processes implicated in the emergence of language structure: individual learning, cultural transmission, and genetic evolution. I will provide simple recreations in python of two key models in the literature: a model showing that compositional structure in language arises as a trade-off between simplicity and expressivity; and a model that shows that strong linguistic nativism cannot evolve. Both are based on very simple Bayesian models of individuals that are placed in simulated populations that interact and learn from one another. We will explore the parameter space of the models and talk about how they might be extended.

Level: Beginners
Software: Jupyter notebooks with matplotlib, scipy, and numpy installed.

I will present an error-driven computational model for the mental lexicon that provides a set of algorithms for probing visual and auditory comprehension, as well as speech production. The first half of the workshop will introduce basic concepts and key elements of the DLM theory. The second half of the workshop will provide participants with hands-on experience with the open-source implementation of the model, the JudiLing package for the julia programming language. Participants will be guided through a jupyter notebook that illustrates how the DLM can be used both as a linguistic model and as a cognitive model generating detailed predictions for lexical processing.

Level: PhD students, postdocs
Softwares: R julia