A Foundation for Empirical Multimodality Research
FOUNDATIONS develops a novel methodology for empirical research on multimodality by creating large, annotated corpora and using AI to analyze human communication across diverse cultural artifacts.
Projectdetails
Introduction
This project lays a foundation for conducting data-driven empirical research on multimodality, that is, how human communication and interaction rely on combinations of 'modes' of expression. Theories of multimodality are rapidly gaining currency in diverse fields concerned with human communication, interaction, and cultural production. However, most theories of multimodality are based on conjecture and remain without an empirical foundation due to the lack of large, richly-annotated multimodal corpora and methods for their analysis.
Methodology Development
FOUNDATIONS solves this problem by developing a novel methodology for conducting empirical research on multimodality in everyday cultural artefacts, such as newspapers, textbooks, magazines, and social media videos. This methodology allows for critically examining key theoretical concepts in the field – medium, semiotic mode, and genre – and their joint contribution to meaning-making. This renewal enhances our understanding of key theoretical concepts in multimodality research and places their definitions on a solid empirical foundation.
Crowdsourcing and Data Collection
To do so, FOUNDATIONS creates large and reproducible multimodal corpora using microtask crowdsourcing, which breaks complex tasks into piecemeal work and distributes this effort to non-expert workers on online platforms. These crowdsourced descriptions are combined with computational representations into graphs that represent the structure of multimodal discourse.
Analysis Techniques
For analysing these corpora, the project develops novel methods based on neuro-symbolic artificial intelligence, which enables combining crowdsourced human insights with the pattern recognition capability of neural networks.
Impact and Future Directions
The groundbreaking theoretical and methodological advances in FOUNDATIONS go far beyond the state of the art by enabling large-scale empirical research while preserving the analytical depth needed for multimodality research. This opens up new domains of inquiry for studying multimodality across cultures, situations, artefacts, and timescales.
Financiële details & Tijdlijn
Financiële details
Subsidiebedrag | € 1.999.974 |
Totale projectbegroting | € 1.999.974 |
Tijdlijn
Startdatum | 1-5-2024 |
Einddatum | 30-4-2029 |
Subsidiejaar | 2024 |
Partners & Locaties
Projectpartners
- HELSINGIN YLIOPISTOpenvoerder
Land(en)
Vergelijkbare projecten binnen European Research Council
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MANUNKIND: Determinants and Dynamics of Collaborative Exploitation
This project aims to develop a game theoretic framework to analyze the psychological and strategic dynamics of collaborative exploitation, informing policies to combat modern slavery.
Elucidating the phenotypic convergence of proliferation reduction under growth-induced pressure
The UnderPressure project aims to investigate how mechanical constraints from 3D crowding affect cell proliferation and signaling in various organisms, with potential applications in reducing cancer chemoresistance.
Uncovering the mechanisms of action of an antiviral bacterium
This project aims to uncover the mechanisms behind Wolbachia's antiviral protection in insects and develop tools for studying symbiont gene function.
The Ethics of Loneliness and Sociability
This project aims to develop a normative theory of loneliness by analyzing ethical responsibilities of individuals and societies to prevent and alleviate loneliness, establishing a new philosophical sub-field.
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Subliminal learning in the Mandarin lexicon
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This project aims to uncover how the brain approximates causal inference in complex multisensory environments using interdisciplinary methods, potentially informing AI and addressing perceptual challenges in clinical populations.
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This project aims to reconstruct early European languages by analyzing low-frequency linguistic variants in historical texts, integrating philology with deep learning to uncover cultural interactions.