Formal Models at the Core - Archive ouverte HAL
Article Dans Une Revue Cognitive Science Année : 2023

Formal Models at the Core

Isabelle Charnavel
  • Fonction : Auteur
David Embick
  • Fonction : Auteur
Fred Lerdahl
  • Fonction : Auteur
Pritty Patel-Grosz
  • Fonction : Auteur
David Poeppel
  • Fonction : Auteur

Résumé

The grammatical paradigm used to be a model for entire areas of cognitive science. Its primary tenet was that theories are axiomatic‐like systems. A secondary tenet was that their predictions should be tested quickly and in great detail with introspective judgments. While the grammatical paradigm now often seems passé, we argue that in fact it continues to be as efficient as ever. Formal models are essential because they are explicit, highly predictive, and typically modular. They make numerous critical predictions, which must be tested efficiently; introspective judgments do just this. We further argue that the grammatical paradigm continues to be fruitful. Within linguistics, implicature theory is a recent example, with a combination of formal explicitness, modularity, and interaction with experimental work. Beyond traditional linguistics, the grammatical paradigm has proven fruitful in the study of gestures and emojis; literature (“Free Indirect Discourse”); picture semantics and comics; music and dance cognition; and even reasoning and concepts. We argue, however, that the grammatical paradigm must be adapted to contemporary cognitive science. Computational methods are essential to derive quantitative predictions from formal models (Bayesian pragmatics is an example). And data collection techniques offer an ever richer continuum of options, from introspective judgments to large‐scale experiments, which makes it possible to optimize the cost/benefit ratio of the empirical methods that are chosen to test theories.
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Dates et versions

hal-04281495 , version 1 (13-11-2023)

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Emmanuel Chemla, Isabelle Charnavel, Isabelle Dautriche, David Embick, Fred Lerdahl, et al.. Formal Models at the Core. Cognitive Science, 2023, 47 (3), ⟨10.1111/cogs.13267⟩. ⟨hal-04281495⟩
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