VLDB 2026 Research / reviewers in the wild / expert
Michael Ramscar
dblp:12/2261
· DBLP profile ↗
22ranked-venue papers
6as first author
4since 2021 · last 2025
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 21 · 6 first-author · 4 since 2021Applied, interdisciplinary, general and emerging computing · 21 · 6 first-author · 4 since 2021Human-computer interaction and ubiquitous computing · 1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | How grammatical gender supports efficient communication
Dorothée B. Hoppe, Edward Gibson, Jacolien van Rij, Petra Hendriks, Michael Ramscar |
CogSci | 5 |
| 2025 | Interactions Between Linear Order and Lexical Distributions in Artificial Language Learning
Holly Jenkins, Michael Ramscar, Elizabeth Wonnacott |
CogSci | 2 |
| 2024 | Availability, informatively and burstiness: Why average corpus measures are an inaccurate guide to surprisal in language
Edward Gibson, Michael Ramscar |
CogSci | 3 |
| 2024 | Rethinking Probabilities: Why Corpus Frequencies Cannot Capture Speakers' Dynamic Linguistic Behavior
Santina Simone Kemper, Holly Jenkins, Elizabeth Wonnacott, Michael Ramscar |
CogSci | 4 |
| 2017 | Refining the distributional hypothesis: A role for time and context in semantic representation
Melody Dye, Michael N. Jones, Daniel Yarlett, Michael Ramscar |
CogSci | 4 |
| 2017 | Cute Little Puppies and Nice Cold Beers: An Information Theoretic Analysis of Prenominal Adjectives
Melody Dye, Petar Milin, Richard Futrell, Michael Ramscar |
CogSci | 4 |
| 2017 | Representing the Richness of Linguistic Structure in Models of Episodic Memory
Melody Dye, Michael Ramscar, Michael N. Jones |
CogSci | 2 |
| 2016 | The Structure of Names in Memory: Deviations from Uniform Entropy Impair Memory for Linguistic Sequences
Melody Dye, Brendan T. Johns, Michael N. Jones, Michael Ramscar |
CogSci | 4 |
| 2016 | The mismeasurement of mind: How neuropsychological testing creates a false picture of cognitive aging
Michael Ramscar, Ching Chu Sun, Peter Hendrix, R. Harald Baayen |
CogSci | 1 |
| 2015 | Generative and Discriminative Models in Cognitive Science
Bradley C. Love, Michael Ramscar, Thomas L. Griffiths 0001, Matt Jones 0002 |
CogSci | 2 |
| 2015 | The Social Evolution and Communicative Function of Noun Classification
Michael Ramscar, Melody Dye, Petar Milin, Richard Futrell |
CogSci | 1 |
| 2014 | How not to name your baby: Social engineering and the structure of names in memory
Melody Dye, Brendan T. Johns, Suyog H. Chandramouli, Michael Ramscar |
CogSci | 4 |
| 2014 | Refining the distributional hypothesis: A role for time and context in semantic representation
Cotie Long, Melody Dye, Michael Ramscar |
CogSci | 3 |
| 2013 | The Myth of Cognitive Decline
Michael Ramscar, Peter Hendrix, R. Harald Baayen |
CogSci | 1 |
| 2013 | The 'universal' structure of name grammars and the impact of social engineering on the evolution of natural information systems
Michael Ramscar, Asha Halima Smith, Melody Dye, Richard Futrell, Peter Hendrix, R. Harald Baayen, Rebecca Starr |
CogSci | 1 |
| 2011 | For the price of a song: How pitch category learning comes at a cost to absolute frequency representations
Melody Dye, Michael Ramscar, Edward Suh |
CogSci | 2 |
| 2011 | Breaking the World into Symbols
Adam November, Nicolas Davidenko, Michael Ramscar |
CogSci | 3 |
| 2011 | Investigating how infants learn to search in the A-not-B task
Hanna Popick, Melody Dye, Natasha Z. Kirkham, Michael Ramscar |
CogSci | 4 |
| 2011 | Informativity versus logic: Children and adults take different approaches to word learning
Michael Ramscar, Melody Dye, Joseph Klein, Linda Diana Ruiz, Nicole Aguirre, Lily Sadaat |
CogSci | 1 |
| 2011 | How children learn to value numbers: Information structure and the acquisition of numerical understanding
Michael Ramscar, Melody Dye, Hanna Popick, Fiona O'Donnell-McCarthy |
CogSci | 1 |
| 2001 | A Quantitative Model of Counterfactual ReasoningabstractIn this paper we explore two quantitative approaches to the modelling of counterfactual reasoning – a linear and a noisy-OR model – based on in- formation contained in conceptual dependency networks. Empirical data is acquired in a study and the fit of the models compared to it. We con- clude by considering the appropriateness of non-parametric approaches to counterfactual reasoning, and examining the prospects for other para- metric approaches in the future. Daniel Yarlett, Michael Ramscar |
NIPS | 2 |
| 1998 | Modeling the Cognitive Effects of Participative Learner Modeling
Rafael Morales-Gamboa, Helen Pain, Michael Ramscar |
Intelligent Tutoring Systems | 3 |