Welcome to SNL 2021


Can we predict responses to language interventions? Should we?

Organizers: Thomas M.H. Hope1 & Jenny Crinion1; 1University College London
Presenters: Jade Digman, Nadine Martin, Janina Wilmskoetter, Anne Billot, Sigfus Kristinsson, Thomas Hope

Traditional group studies of language (re)learning often fail to capture individuals’ variance in response and in the case of aphasia (acquired language disorder post stroke) we frequently see much stronger benefits in individuals than the group-level effects might suggest. If these individual effects are systematic and repeatable, they suggest that language learning (treatment) studies’ efficiency might be improved, perhaps dramatically, by targeting interventions to those who are most likely to benefit. This symposium aims to bring together those working (or interested in working) in this emerging field, seeking consensus on its key methodological and ethical challenges. We will ask whether and how we can derive credible, predictive results from the typically small samples employed in this domain. And we will consider how best to address patients’ concerns that these results might eventually be used to withhold treatments from those judged unlikely to benefit from them.

Semantic knowledge representations in the anterior temporal lobes and beyond

Organizers: Andrew Persichetti1 & Alex Martin1; 1Laboratory of Brain and Cognition, National Institute of Mental Health, NIH
Presenters: Rebecca Jackson, Andrew S. Persichetti, Elizabeth Jefferies, Srikanth Damera, Stefano Anzellotti, Galit Yovel

A better understanding of how the brain represents diverse knowledge about the world (e.g., people, places, things, and relations between them) is critical to the study of human thought and language. There are two prevalent competing theories about how the brain represents semantic knowledge. One theory proposes that a single region in the anterior temporal lobes (ATL) integrates information from diverse sensory and category-selective systems to represent all semantic knowledge (i.e., a domain-general semantic hub). The other theory proposes that knowledge about categories is represented in segregated systems that represent category-specific knowledge (i.e., domain-specific systems). According to the latter view, the ATL is not a convergence zone for all semantic knowledge, but rather a collection of functionally diverse regions. In this symposium, we hope to spur a fun and informative discussion on this important question by presenting data from multiple theoretical perspectives and methodologies, including fMRI, EEG, and computational modeling.

What can NLP systems teach us about language in the brain?

Organizer: Mariya Toneva1,2,3; 1Carnegie Mellon University, 2Postdoctoral Fellow, Princeton University, 3Assistant Professor, Max Planck Institute for Software Systems
Presenters: Evelina Fedorenko, Jixing Li, Jean-Rémi King, Leila Wehbe, Alexander Huth

In the last few years, new computational tools have emerged for natural language processing (NLP) that significantly outperform previous methods across linguistic tasks, ranging from predicting upcoming words to answering comprehension questions. In particular, these methods learn to represent individual words and to flexibly combine these representations to account for the surrounding context and the task at hand, without enforcing specific constraints from linguistics. Recent work in neurolinguistics shows that these models can also predict brain activity during language comprehension to an impressive degree. How these new methods can improve our understanding of the neurobiology of language remains an open question. In this symposium, the speakers will discuss their perspective on the benefits and limitations of utilizing recent NLP systems for improved understanding of language in the brain. Our target audience is researchers who want to make scientific inferences about the neurobiology of language using powerful but complex computational methods.