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Meaning project

Linguistic meaning is highly influenced by the conventions and practices of cultural groups. One manifestation are worldwide oscillations of de- and recontextualization, apparent when individual languages are monitored over sufficiently long time periods. Asking how the capacity for meaning has evolved, thus, requires not only probing how the neural apparatus represents reality and associated symbolic systems — the main focus of this Project — but also how and why humans have evolved a capacity for norms. Representational power and ‘normophilia’ are thus at the core of the century-old problem of linguistic meaning. With three interlinked work packages, we tackle these issues with our signature tripartite toolkit of empirical work with humans, animals and silicon systems.

WP Meaning Behaviour

WP MeaningBehaviour will focus on the human capacity to acquire and continuously change conventionalised abstract meanings from reality-bound contexts, perpetually coining new words and replacing old ones, both within and between populations. We investigate what constrains this capacity and to what extent it is grounded in more widespread forms of representational flexibility and conceptual (re-)mapping.

Meaning Change Task
PIs: Sennrich, Widmer; Collaborating PIs: Bickel, van der Plas, Stadler, Shimizu, Laganaro, Stoll; Senior Researcher: Cathcart

Lexical Innovation Task
PIs: van der Plas, Migliano, Jäger; Collaborating PIs: Widmer, Mansfield, Borghesani, Bangerter, Bickel

Animal Meaning Task
PIs: Manser, Zuberbühler, Stoll; Collaborating PIs: Burkart, Townsend, Widmer; Senior Advisor: Van Schaik

WP Meaning Computation

WP MeaningComputation explores the neuro-computational substrate of lexical and compositional semantics. We study the spatio-temporal dynamics of the neural mechanisms mapping forms (words) to meanings (concepts) across the lifespan and in neuro-typical conditions. Furthermore, we compare form-meaning mapping as expressed in biological versus artificial networks. We also investigate how compositional semantics arise, by examining neural principles of encoding linear and hierarchical relations between words during real-time language processing and during sleep consolidation.

Meaning Representation Task
PIs: Schwartz, Kazanina; Collaborating PIs: Giraud, Meyer; Senior Researcher: Olasagasti

Meaning Mapping Task
PIs: Borghesani, Laganaro; Collaborating PI: Guggisberg, Brem, Berthele

Silicon Meaning Task
PIs: Borghesani, Sennrich; Collaborating PI: Guggisberg

Latest publication:

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WP World Knowledge

WP WorldKnowledge investigates the potentially bidirectional relation between our world knowledge and the linguistic symbolic representations that allow us to share it. Do more complex communication systems foster more complex representations? We tackle the question by comparing neural data collected from humans, behavioural data from animals and computational data from artificial systems.

Comparative Cognition Task
PIs: Clément, Townsend, Zuberbühler; Collaborating PIs: Bavelier, Bickel

Rules Task
PIs: Borghesani, Bavelier; Collaborating PI: van der Plas; Senior Advisor: Merlo

Instructions Task
PIs: Borghesani, Bavelier; Collaborating PIs: Henderson, van der Plas

Silicon Knowledge Task
PIs: Henderson, Sennrich; Collaborating PIs: Borghesani, van der Plas; Senior Advisor: Merlo