English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Representing absence of evidence: why algorithms and representations matter in models of language and cognition

MPS-Authors
/persons/resource/persons241087

Bröker,  F
Department of Computational Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Bröker, F., & Ramscar, M. (2020). Representing absence of evidence: why algorithms and representations matter in models of language and cognition. Language, Cognition and Neuroscience, Epub ahead. doi:10.1080/23273798.2020.1862257.


Cite as: http://hdl.handle.net/21.11116/0000-0007-9E7A-F
Abstract
Theories of language and cognition develop iteratively from ideas, experiments and models. The abstract nature of “cognitive processes” means that computational models play a critical role in this, yet bridging the gaps between models, data, and interpretations is challenging. While the how and why computations are performed is often the primary research focus, the conclusions drawn from models can be compromised by the representations chosen for them. To illustrate this point, we revisit a set of empirical studies of language acquisition that appear to support different models of learning from implicit negative evidence. We examine the degree to which these conclusions were influenced by the representations chosen and show how a plausible single mechanism account of the data can be formulated for representations that faithfully capture the task design. The need for input representations to be incorporated into model conceptualisations, evaluations, and comparisons is discussed.