Sygal Amitay

2.3k total citations · 1 hit paper
34 papers, 1.6k citations indexed

About

Sygal Amitay is a scholar working on Cognitive Neuroscience, Experimental and Cognitive Psychology and Speech and Hearing. According to data from OpenAlex, Sygal Amitay has authored 34 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Cognitive Neuroscience, 8 papers in Experimental and Cognitive Psychology and 6 papers in Speech and Hearing. Recurrent topics in Sygal Amitay's work include Hearing Loss and Rehabilitation (22 papers), Neuroscience and Music Perception (19 papers) and Neural dynamics and brain function (15 papers). Sygal Amitay is often cited by papers focused on Hearing Loss and Rehabilitation (22 papers), Neuroscience and Music Perception (19 papers) and Neural dynamics and brain function (15 papers). Sygal Amitay collaborates with scholars based in United Kingdom, United States and Israel. Sygal Amitay's co-authors include David R. Moore, David Hawkey, J Barry, Amy Irwin, Piers Dawes, Andrew Stewart, Kevin J. Munro, Ronan McGarrigle, Israel Nelken and Merav Ahissar and has published in prestigious journals such as Nature Neuroscience, PLoS ONE and Brain.

In The Last Decade

Sygal Amitay

34 papers receiving 1.6k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Listening effort and fatigue: What exactly are we measuring? A British Society of Audiology Cognition in Hearing Special Interest Group ‘white paper’

2014 371 citations Ronan McGarrigle, Kevin J. Munro et al. International Journal of Audiology profile →

Peers

Sygal Amitay

CN

DEP

SH

ECP

SS

Karen Banai Israel

Martina Huss United Kingdom

Mridula Sharma Australia

Craig A. Champlin United States

Stefanie E. Kuchinsky United States

Thomas D. Carrell United States

Leigh A. Thorpe Canada

Alexandra Parbery‐Clark United States

Jane A. Baran United States

Noah H. Silbert United States

Karen Banai Israel

Sygal Amitay

1.8k ×1.2

CN

665 ×1.6

DEP

290 ×0.7

SH

536 ×1.5

ECP

326 ×1.4

SS

Citations per year, relative to Sygal Amitay Sygal Amitay (= 1×) peers Karen Banai

Countries citing papers authored by Sygal Amitay

Since Specialization

Citations

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

Fields of papers citing papers by Sygal Amitay

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sygal Amitay. 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 Sygal Amitay. The network helps show where Sygal Amitay may publish in the future.

Co-authorship network of co-authors of Sygal Amitay

This figure shows the co-authorship network connecting the top 25 collaborators of Sygal Amitay. A scholar is included among the top collaborators of Sygal Amitay 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 Sygal Amitay. Sygal Amitay is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Casaponsa, Aina, et al.. (2019). Does training with amplitude modulated tones affect tone-vocoded speech perception?. PLoS ONE. 14(12). e0226288–e0226288. 4 indexed citations

2.

Moore, David R., et al.. (2016). Auditory Discrimination Learning: Role of Working Memory. PLoS ONE. 11(1). e0147320–e0147320. 32 indexed citations

3.

Jones, Pete R., David R. Moore, & Sygal Amitay. (2015). Development of auditory selective attention: Why children struggle to hear in noisy environments.. Developmental Psychology. 51(3). 353–369. 43 indexed citations

4.

Jones, Pete R., et al.. (2015). The role of response bias in perceptual learning.. Journal of Experimental Psychology Learning Memory and Cognition. 41(5). 1456–1470. 32 indexed citations

5.

Stewart, Hannah J. & Sygal Amitay. (2015). Modality-specificity of Selective Attention Networks. Frontiers in Psychology. 6. 1826–1826. 13 indexed citations

6.

Amitay, Sygal, et al.. (2015). Feedback Valence Affects Auditory Perceptual Learning Independently of Feedback Probability. PLoS ONE. 10(5). e0126412–e0126412. 10 indexed citations

7.

McGarrigle, Ronan, Kevin J. Munro, Piers Dawes, et al.. (2014). Listening effort and fatigue: What exactly are we measuring? A British Society of Audiology Cognition in Hearing Special Interest Group ‘white paper’. International Journal of Audiology. 53(7). 433–445. 371 indexed citations breakdown →

8.

Amitay, Sygal, Jeanne Guiraud, Ediz Sohoglu, et al.. (2013). Human Decision Making Based on Variations in Internal Noise: An EEG Study. PLoS ONE. 8(7). e68928–e68928. 21 indexed citations

9.

Amitay, Sygal, et al.. (2013). Perceptual learning: Top to bottom. Vision Research. 99. 69–77. 34 indexed citations

10.

Banai, Karen & Sygal Amitay. (2012). Stimulus uncertainty in auditory perceptual learning. Vision Research. 61. 83–88. 9 indexed citations

11.

Moore, David R., et al.. (2012). Less Is More: Latent Learning Is Maximized by Shorter Training Sessions in Auditory Perceptual Learning. PLoS ONE. 7(5). e36929–e36929. 65 indexed citations

12.

Zhang, Yuxuan, J Barry, David R. Moore, & Sygal Amitay. (2012). A New Test of Attention in Listening (TAIL) Predicts Auditory Performance. PLoS ONE. 7(12). e53502–e53502. 32 indexed citations

13.

Amitay, Sygal, et al.. (2012). Asymmetric Transfer of Auditory Perceptual Learning. Frontiers in Psychology. 3. 508–508. 4 indexed citations

14.

Halliday, Lorna F., David R. Moore, Jenny Taylor, & Sygal Amitay. (2011). Dimension-specific attention directs learning and listening on auditory training tasks. Attention Perception & Psychophysics. 73(5). 1329–1335. 15 indexed citations

15.

Amitay, Sygal, Lorna F. Halliday, Jenny Taylor, Ediz Sohoglu, & David R. Moore. (2010). Motivation and Intelligence Drive Auditory Perceptual Learning. PLoS ONE. 5(3). e9816–e9816. 30 indexed citations

16.

Amitay, Sygal, Amy Irwin, & David R. Moore. (2006). Discrimination learning induced by training with identical stimuli. Nature Neuroscience. 9(11). 1446–1448. 130 indexed citations

17.

Amitay, Sygal, David Hawkey, & David R. Moore. (2005). Auditory frequency discrimination learning is affected by stimulus variability. Perception & Psychophysics. 67(4). 691–698. 89 indexed citations

18.

Hawkey, David, Sygal Amitay, & David R. Moore. (2004). Early and rapid perceptual learning. Nature Neuroscience. 7(10). 1055–1056. 139 indexed citations

19.

Amitay, Sygal, Merav Ahissar, & Israel Nelken. (2002). Auditory Processing Deficits in Reading Disabled Adults. Journal of the Association for Research in Otolaryngology. 3(3). 302–320. 129 indexed citations

20.

Amitay, Sygal. (2002). Disabled readers suffer from visual and auditory impairments but not from a specific magnocellular deficit. Brain. 125(10). 2272–2285. 173 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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