Melody Dye

1.3k total citations
25 papers, 687 citations indexed

About

Melody Dye is a scholar working on Developmental and Educational Psychology, Artificial Intelligence and Cognitive Neuroscience. According to data from OpenAlex, Melody Dye has authored 25 papers receiving a total of 687 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Developmental and Educational Psychology, 10 papers in Artificial Intelligence and 7 papers in Cognitive Neuroscience. Recurrent topics in Melody Dye's work include Language and cultural evolution (7 papers), Child and Animal Learning Development (7 papers) and Reading and Literacy Development (6 papers). Melody Dye is often cited by papers focused on Language and cultural evolution (7 papers), Child and Animal Learning Development (7 papers) and Reading and Literacy Development (6 papers). Melody Dye collaborates with scholars based in United States, Germany and United Kingdom. Melody Dye's co-authors include Michael Ramscar, Daniel Yarlett, Stewart M. McCauley, Kirsten Thorpe, Brendan T. Johns, Joseph Klein, Michael N. Jones, Teenie Matlock, Michael N. Jones and Richard Futrell and has published in prestigious journals such as PLoS ONE, Child Development and Psychological Science.

In The Last Decade

Melody Dye

22 papers receiving 643 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Melody Dye United States 11 414 293 254 187 111 25 687
Stewart M. McCauley United States 14 453 1.1× 300 1.0× 223 0.9× 120 0.6× 92 0.8× 22 626
Toben H. Mintz United States 15 941 2.3× 257 0.9× 302 1.2× 248 1.3× 186 1.7× 31 1.1k
Isabelle Dautriche France 14 333 0.8× 178 0.6× 225 0.9× 200 1.1× 176 1.6× 34 708
Todd M. Bailey United Kingdom 14 548 1.3× 371 1.3× 242 1.0× 404 2.2× 55 0.5× 17 934
Eliana Colunga United States 12 451 1.1× 172 0.6× 106 0.4× 137 0.7× 67 0.6× 37 618
Rushen Shi Canada 16 996 2.4× 268 0.9× 140 0.6× 371 2.0× 91 0.8× 48 1.2k
Mika Braginsky United States 9 679 1.6× 179 0.6× 117 0.5× 157 0.8× 43 0.4× 16 820
Tamara Nicol Medina United States 3 509 1.2× 147 0.5× 133 0.5× 91 0.5× 91 0.8× 3 614
Alan Bale Canada 16 273 0.7× 161 0.5× 242 1.0× 174 0.9× 75 0.7× 42 771
Simona Amenta Italy 11 369 0.9× 365 1.2× 104 0.4× 153 0.8× 32 0.3× 30 573

Countries citing papers authored by Melody Dye

Since Specialization
Citations

This map shows the geographic impact of Melody Dye's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Melody Dye with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Melody Dye more than expected).

Fields of papers citing papers by Melody Dye

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Melody Dye. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Melody Dye. The network helps show where Melody Dye may publish in the future.

Co-authorship network of co-authors of Melody Dye

This figure shows the co-authorship network connecting the top 25 collaborators of Melody Dye. A scholar is included among the top collaborators of Melody Dye based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Melody Dye. Melody Dye is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Johns, Brendan T. & Melody Dye. (2019). Gender bias at scale: Evidence from the usage of personal names. Behavior Research Methods. 51(4). 1601–1618. 12 indexed citations
2.
Dye, Melody, Petar Milin, Richard Futrell, & Michael Ramscar. (2017). Cute Little Puppies and Nice Cold Beers: An Information Theoretic Analysis of Prenominal Adjectives.. Cognitive Science. 4 indexed citations
3.
Dye, Melody, Michael N. Jones, & Richard M. Shiffrin. (2017). Vanishing the mirror effect: The influence of prior history & list composition on recognition memory.. Cognitive Science. 1 indexed citations
4.
Dye, Melody, Michael Ramscar, & Michael N. Jones. (2017). Representing the Richness of Linguistic Structure in Models of Episodic Memory.. Cognitive Science. 1 indexed citations
5.
Dye, Melody, Brendan T. Johns, Michael N. Jones, & Michael Ramscar. (2016). The Structure of Names in Memory: Deviations from Uniform Entropy Impair Memory for Linguistic Sequences.. Cognitive Science. 3 indexed citations
6.
Ramscar, Michael, Melody Dye, Petar Milin, & Richard Futrell. (2015). The Social Evolution and Communicative Function of Noun Classification.. Cognitive Science. 1 indexed citations
7.
Riordan, Brian, Melody Dye, & Michael N. Jones. (2015). Grammatical number processing and anticipatory eye movements are not tightly coordinated in English spoken language comprehension. Frontiers in Psychology. 6. 590–590. 5 indexed citations
8.
Johns, Brendan T., Melody Dye, & Michael N. Jones. (2015). The influence of contextual diversity on word learning. Psychonomic Bulletin & Review. 23(4). 1214–1220. 69 indexed citations
9.
Johns, Brendan T., Melody Dye, & Michael N. Jones. (2014). The Influence of Contextual Variability in Word Learning.. Cognitive Science. 1 indexed citations
10.
Dye, Melody, et al.. (2014). Refining the distributional hypothesis: A role for time and context in semantic representation.. Cognitive Science. 36(36). 1 indexed citations
11.
Ramscar, Michael, Melody Dye, Richard Futrell, et al.. (2013). The ‘universal’ structure of name grammars and the impact of social engineering on the evolution of natural information systems. Cognitive Science. 35(35). 3245–3250. 7 indexed citations
12.
Ramscar, Michael, Melody Dye, & Stewart M. McCauley. (2013). Error and Expectation in Language Learning: The Curious Absence of Mouses in Adult Speech. Language. 89(4). 760–793. 118 indexed citations
13.
Ramscar, Michael, et al.. (2013). Dual Routes to Cognitive Flexibility: Learning and Response-Conflict Resolution in the Dimensional Change Card Sort Task. Child Development. 84(4). 1308–1323. 42 indexed citations
14.
Ramscar, Michael, Melody Dye, & Joseph Klein. (2013). Children Value Informativity Over Logic in Word Learning. Psychological Science. 24(6). 1017–1023. 78 indexed citations
15.
Ramscar, Michael, et al.. (2011). Informativity versus logic: Children and adults take different approaches to word learning. Cognitive Science. 33(33).
16.
Ramscar, Michael, et al.. (2011). The Enigma of Number: Why Children Find the Meanings of Even Small Number Words Hard to Learn and How We Can Help Them Do Better. PLoS ONE. 6(7). e22501–e22501. 48 indexed citations
17.
Dye, Melody. (2011). Why Johnny Can't Name His Colors. Scientific American Mind. 22(2). 48–51. 2 indexed citations
18.
Ramscar, Michael, Daniel Yarlett, Melody Dye, & Nal Kalchbrenner. (2010). The feature-label-order effect in symbolic learning. Proceedings of the Annual Meeting of the Cognitive Science Society. 31(31). 1 indexed citations
19.
Kao, Justine, Robert J. Ryan, Melody Dye, & Michael Ramscar. (2010). An acquired taste: How reading literature affects sensitivity to word distributions when judging literary texts. eScholarship (California Digital Library). 32(32). 1 indexed citations
20.
Ramscar, Michael, et al.. (2010). The Effects of Feature‐Label‐Order and Their Implications for Symbolic Learning. Cognitive Science. 34(6). 909–957. 192 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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