Language Sciences Annual Symposium 2025: Ambitions for language science in 2050

This event has already happened
Date/time:

Thursday 27 November 2025 12:00pm to 7:30pm

Location:

Cripps Court, Magdalene College, Cambridge

About

  • Thursday 27 November 2025 | 12:00 – 19:30
  • Cripps Court, Magdalene College, Cambridge

 

The Cambridge Language Sciences (CLS) Annual Symposium is an annual meeting of minds, bringing together language scientists of all disciplines from the University of Cambridge for an afternoon of talks, poster presentations and informal networking.

The theme of this year’s Symposium is ‘Ambitions for Language Science in 2050’.

Registration is now closed.

Event Programme (see below for abstracts)

  • 12:00 - 12:30 | Registration & Lunch

  • 12:30 - 12:35 | Welcome and Introductions

  • 12:35 - 14:20 | Research Dialogue 1: Enhancing Language Assessment and Rehabilitation: Using AI to model and support aphasia recovery

Speakers: 

  • Dr Fatemeh Geranmayeh: Clinical Research Fellow, Department of Brain Sciences (Imperial College London)

Session Chair: Dr Holly Robson (University College London)

 

  • 14:20 - 14:55 | Poster Slam (Part 1)

  • 14:55 - 15:40 | Poster Exhibition & Refreshments

  • 15:40 - 17:25 | Research Dialogue 2: Finding Meaning with Brains and Machines: Past, Present, Future

Speakers: 

  • Professor Tim Rogers: Professor of Psychology, Department of Psychology (University of Wisconsin-Madison)

  • Dr Guy Emerson: Departmental Early-Career Academic Fellow & Co-Director, Cambridge Language Sciences (Cambridge)

Session Chair: Dr Matt Davis (MRC Cognition and Brain Sciences Unit, Cambridge)

 

  • 17:25 - 18:00 | Poster Slam (Part 2)

  • 18:00 - 18:15 | Closing Remarks

  • 18:15 - 19:30 | Posters and Drinks Reception

 

We gratefully acknowledge the support of Cambridge University Press & Assessment in making this event possible.

For any queries, contact:
Cambridge Language Sciences – events@languagesciences.cam.ac.uk

Note: The call for posters has now closed 

Poster Session Organisers: Sammy Weiss-Cowie (MRC Cognition and Brain Sciences Unit): sammy.weiss-cowie@mrc-cbu.cam.ac.uk, Shanshan Hu (Theoretical and Applied Linguistics): sh2194@cam.ac.uk, and Suchir Salhan (Computer Science & Technology): sas245@cam.ac.uk

Posters:

  • The list of the posters isa available here.

Abstracts:

Title: Enhancing Language Assessment and Rehabilitation: Using AI to Model and Support Aphasia Recovery

Aphasia (acquired disorders of language) have a special place not only in the history of language sciences but also the neurosciences – as they have often been used as a vehicle for testing theoretical approaches to understanding the nature and mechanisms underpinning human higher cognitive functions and their neural bases. Aphasia offers a fascinating set of challenges to be solved including the contrastive yet graded variations amongst patients, the patterns of partial recovery and how this is underpinned by changes in the damage brain. This history of aphasia, going all the way back to its neurological pioneers, Wernicke, Meynert and Broca, is full of various notions and ideas about the nature of aphasia and the mechanisms of recovery (and thus, in turn, approaches for speech and language therapy). However, many of notions are actually contradictory and puzzling; indeed the mechanistic-computational bases have not been elucidated, implemented or tested. The rise of neural networks and AI modelling offers a new approach where mechanisms of language can be explicitly implemented and testing – thus providing a form of “open science” theories. By damaging such models, it is possible to assess them as a formal account of different kinds of aphasia; and long-term recovery can be viewed as a gradual re-optimisation of the remaining systems. Moreover, by constraining the models’ architecture with macro-level neuroanatomy (i.e., key brain regions and their interconnections) it is possible to bridge formally between brain and mind – in both their intact and damaged forms.

 

Title: Enhancing Language Assessment and Rehabilitation: Using AI to Model and Support Aphasia Recovery

Predicting and supporting language recovery after stroke remains a major clinical challenge. Traditional models based on lesion size and location explain only part of the variability in aphasia outcomes. AI has huge potential to enable more precise, scalable approaches to modelling and enhancing recovery. This talk outlines how multimodal biomarkers, from structural neuroimaging, network-level brain activity to behaviour, can inform individualised recovery predictions. Using digital platforms such as the Imperial Comprehensive Cognitive  assessment in Cerebrovascular disease  (IC3), longitudinal online testing captures cognitive and language changes remotely and at scale, facilitating better recovery modelling. Other applications of AI-based speech technologies will be discussed, including fine-tuned automatic speech recognition systems retrained on aphasic speech (SONIVA), that significantly improve speech-to-text accuracy and enable automated, objective assessment of language impairment in patients after stroke. Together, these advances support a roadmap toward personalised, data-driven language rehabilitation. Key challenges remain in fairness, interpretability, and clinical integration, but AI promises to transform how we predict, measure, and enhance recovery after brain injury.

 

Title: How would we know if a machine "has concepts"?

Contemporary AI technologies offer a remarkable simulacrum of human language, to the extent that they are now in regular use in contexts ranging from computer programming to essay writing to virtual dating. Given their general effectiveness in many settings, it seems reasonable to ask whether these systems "have concepts": do they "understand" their language input and the responses that they generate? Do they "know" about the world? If they do, how would we tell? These questions have also been posed of other intelligent behaving systems, including animals, infants and children, people with atypical patterns of neuro-cognitive development, and patients suffering from cognitive and language impairments following brain injury. Over the years scientists have innovated clever ways of assessing what kinds of language and cognitive behaviors such populations can/cannot do, which provide a toolbox for evaluating contemporary AI capabilities. In this talk I will suggest that current models show patterns of success and failure that are unlike any naturally intelligent system, and that such patterns highlight aspects of human cognition and language that, while not really recognized in most prior work, may be essential to what we mean by the word "concept."

 

Title: The possibility and impossibility of computing meaning

Past work has shown that many aspects of natural language semantics can be modelled using tools from logic and probability theory. I will first discuss how it is possible to train such a model in practice on various kinds of data, and how the probabilistic logical structure of the model is important for generalisation. Turning to the future, I will take stock of the bigger picture, and explain why it is (unfortunately) impossible for such a model to satisfy all the properties we might ideally expect, including computational tractability and logical/probabilistic coherence. I will sketch a new approach to probabilistic modelling, which maintains tractability by relaxing the strict demands of Bayesian inference. This has the potential to explain how patterns of language use arise as a result of computationally constrained minds interacting with a computationally demanding world.