Amy S. Finn

2.6k total citations
50 papers, 1.7k citations indexed

About

Amy S. Finn is a scholar working on Cognitive Neuroscience, Developmental and Educational Psychology and Experimental and Cognitive Psychology. According to data from OpenAlex, Amy S. Finn has authored 50 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Cognitive Neuroscience, 18 papers in Developmental and Educational Psychology and 15 papers in Experimental and Cognitive Psychology. Recurrent topics in Amy S. Finn's work include Memory and Neural Mechanisms (11 papers), Language Development and Disorders (10 papers) and Child and Animal Learning Development (9 papers). Amy S. Finn is often cited by papers focused on Memory and Neural Mechanisms (11 papers), Language Development and Disorders (10 papers) and Child and Animal Learning Development (9 papers). Amy S. Finn collaborates with scholars based in United States, Canada and Estonia. Amy S. Finn's co-authors include John D. E. Gabrieli, Margaret A. Sheridan, Julia Leonard, Carla L. Hudson Kam, Martin R. West, Christopher F. O. Gabrieli, Katie A. McLaughlin, Matthew Peverill, Rebecca Martin and Matthew A. Kraft and has published in prestigious journals such as Nature Communications, Journal of Neuroscience and PLoS ONE.

In The Last Decade

Amy S. Finn

49 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Amy S. Finn United States 19 565 414 406 396 356 50 1.7k
Julia Leonard United States 20 570 1.0× 706 1.7× 355 0.9× 579 1.5× 379 1.1× 43 1.8k
Laura E. Engelhardt United States 20 410 0.7× 300 0.7× 424 1.0× 366 0.9× 354 1.0× 27 1.4k
Linda Van Leijenhorst Netherlands 18 1.2k 2.0× 361 0.9× 584 1.4× 149 0.4× 474 1.3× 22 2.1k
Brad E. Sheese United States 19 863 1.5× 330 0.8× 558 1.4× 425 1.1× 1.1k 3.2× 26 2.8k
Suparna Choudhury Canada 15 1.0k 1.8× 547 1.3× 492 1.2× 346 0.9× 827 2.3× 26 2.9k
Anna C. K. van Duijvenvoorde Netherlands 29 1.3k 2.4× 311 0.8× 812 2.0× 211 0.5× 792 2.2× 67 2.8k
Jennifer Byrd‐Craven United States 17 253 0.4× 898 2.2× 621 1.5× 882 2.2× 356 1.0× 63 2.3k
Lisa J. Knoll United Kingdom 10 367 0.6× 155 0.4× 229 0.6× 168 0.4× 444 1.2× 12 1.4k
Meghan L. Meyer United States 23 1.1k 1.9× 384 0.9× 686 1.7× 198 0.5× 826 2.3× 53 2.8k
Lucy Foulkes United Kingdom 23 409 0.7× 209 0.5× 479 1.2× 230 0.6× 1.1k 3.1× 45 2.1k

Countries citing papers authored by Amy S. Finn

Since Specialization
Citations

This map shows the geographic impact of Amy S. Finn'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 Amy S. Finn with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Amy S. Finn more than expected).

Fields of papers citing papers by Amy S. Finn

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Amy S. Finn. 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 Amy S. Finn. The network helps show where Amy S. Finn may publish in the future.

Co-authorship network of co-authors of Amy S. Finn

This figure shows the co-authorship network connecting the top 25 collaborators of Amy S. Finn. A scholar is included among the top collaborators of Amy S. Finn 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 Amy S. Finn. Amy S. Finn 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.
Finn, Amy S., et al.. (2024). Directing Attention Shapes Learning in Adults but Not Children. Psychological Science. 35(10). 1139–1154. 2 indexed citations
2.
Finn, Amy S., et al.. (2023). Memories of structured input become increasingly distorted across development. Child Development. 94(5). e279–e295. 2 indexed citations
3.
Duncan, Katherine, et al.. (2023). What sticks after statistical learning: The persistence of implicit versus explicit memory traces. Cognition. 236. 105439–105439. 9 indexed citations
4.
Vainik, Uku, Budhachandra Khundrakpam, Katherine Duncan, et al.. (2023). Polygenic risk for depression and anterior and posterior hippocampal volume in children and adolescents. Journal of Affective Disorders. 344. 619–627. 4 indexed citations
5.
Siegelman, Noam, et al.. (2022). Attention Shifts to More Complex Structures With Experience. Psychological Science. 33(12). 2059–2072. 12 indexed citations
6.
Duncan, Katherine, et al.. (2022). Pay attention and you might miss it: Greater learning during attentional lapses. Psychonomic Bulletin & Review. 30(3). 1041–1052. 13 indexed citations
7.
Ren, Jie, et al.. (2021). Events structure information accessibility less in children than adults. Cognition. 217. 104878–104878. 1 indexed citations
8.
Finn, Amy S., et al.. (2020). What is represented in memory after statistical learning. Cognitive Science. 1 indexed citations
9.
Leonard, Julia, Rachel Romeo, Anne T. Park, et al.. (2019). Associations between cortical thickness and reasoning differ by socioeconomic status in development. Developmental Cognitive Neuroscience. 36. 100641–100641. 35 indexed citations
10.
Finn, Amy S., et al.. (2019). Superior learning in synesthetes: Consistent grapheme-color associations facilitate statistical learning. Cognition. 186. 72–81. 6 indexed citations
11.
Galla, Brian M., Elizabeth P. Shulman, Margo Gardner, et al.. (2019). Why High School Grades Are Better Predictors of On-Time College Graduation Than Are Admissions Test Scores: The Roles of Self-Regulation and Cognitive Ability. American Educational Research Journal. 56(6). 2077–2115. 84 indexed citations
12.
Finn, Amy S., et al.. (2018). Attention Selectively Boosts Learning of Statistical Structure.. Cognitive Science. 2 indexed citations
13.
Peverill, Matthew, Katie A. McLaughlin, Amy S. Finn, & Margaret A. Sheridan. (2016). Working memory filtering continues to develop into late adolescence. Developmental Cognitive Neuroscience. 18. 78–88. 20 indexed citations
14.
Cain, Matthew S., Julia Leonard, John D. E. Gabrieli, & Amy S. Finn. (2016). Media multitasking in adolescence. Psychonomic Bulletin & Review. 23(6). 1932–1941. 126 indexed citations
15.
Finn, Amy S., et al.. (2015). Developmental dissociation between the maturation of procedural memory and declarative memory. Journal of Experimental Child Psychology. 142. 212–220. 48 indexed citations
16.
Leonard, Julia, Allyson P. Mackey, Amy S. Finn, & John D. E. Gabrieli. (2015). Differential effects of socioeconomic status on working and procedural memory systems. Frontiers in Human Neuroscience. 9. 554–554. 49 indexed citations
17.
West, Martin R., Christopher F. O. Gabrieli, Amy S. Finn, Matthew A. Kraft, & John D. E. Gabrieli. (2014). What Effective Schools Do.. Education next. 14(4). 72–79. 6 indexed citations
18.
Finn, Amy S., et al.. (2014). When It Hurts (and Helps) to Try: The Role of Effort in Language Learning. PLoS ONE. 9(7). e101806–e101806. 34 indexed citations
19.
Finn, Amy S., Carla L. Hudson Kam, Marc Ettlinger, Jason Vytlacil, & Mark D’Esposito. (2013). Learning language with the wrong neural scaffolding: the cost of neural commitment to sounds. Frontiers in Systems Neuroscience. 7. 85–85.
20.
Finn, Amy S. & Carla L. Hudson Kam. (2006). Use of Word Segmentation Cues in Adults: L1 Phonotactics versus L2 Transitional Probabilities. eScholarship (California Digital Library). 28(28). 1 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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