Hadas Okon‐Singer

3.4k total citations
67 papers, 1.9k citations indexed

About

Hadas Okon‐Singer is a scholar working on Cognitive Neuroscience, Experimental and Cognitive Psychology and Social Psychology. According to data from OpenAlex, Hadas Okon‐Singer has authored 67 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Cognitive Neuroscience, 33 papers in Experimental and Cognitive Psychology and 12 papers in Social Psychology. Recurrent topics in Hadas Okon‐Singer's work include Neural and Behavioral Psychology Studies (27 papers), Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes (20 papers) and Mental Health Research Topics (16 papers). Hadas Okon‐Singer is often cited by papers focused on Neural and Behavioral Psychology Studies (27 papers), Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes (20 papers) and Mental Health Research Topics (16 papers). Hadas Okon‐Singer collaborates with scholars based in Israel, Germany and United States. Hadas Okon‐Singer's co-authors include Talma Hendler, Arno Villringer, Avishai Henik, Tatjana Aue, Alexander J. Shackman, Luiz Pessoa, Noga Cohen, Julia Sacher, Joseph Tzelgov and Thalia Richter and has published in prestigious journals such as Journal of Neuroscience, PLoS ONE and NeuroImage.

In The Last Decade

Hadas Okon‐Singer

63 papers receiving 1.9k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Hadas Okon‐Singer Israel 25 1.1k 776 326 321 199 67 1.9k
Carmen Morawetz Germany 21 919 0.9× 624 0.8× 351 1.1× 291 0.9× 253 1.3× 51 1.7k
Katherine Vytal United States 14 1.2k 1.1× 849 1.1× 306 0.9× 318 1.0× 154 0.8× 14 1.9k
Esther K. Diekhof Germany 20 935 0.9× 589 0.8× 296 0.9× 257 0.8× 278 1.4× 54 1.7k
Linda Van Leijenhorst Netherlands 18 1.2k 1.1× 584 0.8× 474 1.5× 303 0.9× 265 1.3× 22 2.1k
Richard B. Lopez United States 16 925 0.9× 840 1.1× 666 2.0× 285 0.9× 208 1.0× 37 1.9k
Nicole M. McDonald United States 18 1.4k 1.3× 535 0.7× 395 1.2× 244 0.8× 236 1.2× 37 1.8k
Sanda Dolcos United States 21 985 0.9× 565 0.7× 240 0.7× 309 1.0× 125 0.6× 57 1.7k
Rebecca M. Todd Canada 26 1.4k 1.3× 704 0.9× 482 1.5× 416 1.3× 228 1.1× 67 2.4k
Aprajita Mohanty United States 26 1.8k 1.7× 1.0k 1.3× 405 1.2× 329 1.0× 385 1.9× 63 2.6k
Robin L. Aupperle United States 26 828 0.8× 687 0.9× 782 2.4× 231 0.7× 323 1.6× 111 2.3k

Countries citing papers authored by Hadas Okon‐Singer

Since Specialization
Citations

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

Fields of papers citing papers by Hadas Okon‐Singer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hadas Okon‐Singer. 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 Hadas Okon‐Singer. The network helps show where Hadas Okon‐Singer may publish in the future.

Co-authorship network of co-authors of Hadas Okon‐Singer

This figure shows the co-authorship network connecting the top 25 collaborators of Hadas Okon‐Singer. A scholar is included among the top collaborators of Hadas Okon‐Singer 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 Hadas Okon‐Singer. Hadas Okon‐Singer 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.
Richter, Thalia, Nazanin Derakshan, Noga Cohen, et al.. (2025). Machine learning meta-analysis identifies individual characteristics moderating cognitive intervention efficacy for anxiety and depression symptoms. npj Digital Medicine. 8(1). 65–65. 1 indexed citations
2.
Aue, Tatjana, et al.. (2024). Expectancy and attention bias to spiders: Dissecting anticipation and allocation processes using ERPs. Psychophysiology. 61(6). e14546–e14546. 4 indexed citations
3.
Günther, Vivien, Anette Kersting, Karl‐Titus Hoffmann, et al.. (2023). Cognitive Avoidance Is Associated with Decreased Brain Responsiveness to Threat Distractors under High Perceptual Load. Brain Sciences. 13(4). 618–618. 2 indexed citations
4.
Aue, Tatjana, et al.. (2023). Spider vs. guns: expectancy and attention biases to phylogenetic threat do not extend to ontogenetic threat. Frontiers in Psychology. 14. 1232985–1232985. 3 indexed citations
5.
6.
Richter, Thalia, et al.. (2022). How Social Experiences Affect Interpretation Bias Among Individuals With Non-clinical Depression: The Role of Ostracism. Frontiers in Psychiatry. 13. 819143–819143. 2 indexed citations
7.
Derakshan, Nazanin, Noga Cohen, Philip M. Enock, et al.. (2021). Personalized cognitive training: Protocol for individual-level meta-analysis implementing machine learning methods. Journal of Psychiatric Research. 138. 342–348. 11 indexed citations
8.
Ram, Yael, et al.. (2021). Is there a COVID-19 vaccination effect? A three-wave cross-sectional study. Current Issues in Tourism. 25(3). 379–386. 23 indexed citations
9.
Aue, Tatjana, et al.. (2021). Cognitive Biases in Blood-Injection-Injury Phobia: A Review. Frontiers in Psychiatry. 12. 678891–678891. 11 indexed citations
10.
Goldstein, Pavel, et al.. (2020). Blood pressure reaction to negative stimuli: Insights from continuous recording and analysis. Psychophysiology. 57(4). e13525–e13525. 5 indexed citations
11.
Aue, Tatjana & Hadas Okon‐Singer. (2020). Cognitive biases in health and psychiatric disorders: Neurophysiological foundations. Elsevier eBooks. 17 indexed citations
12.
Richter, Thalia, et al.. (2020). Using machine learning-based analysis for behavioral differentiation between anxiety and depression. Scientific Reports. 10(1). 16381–16381. 59 indexed citations
13.
Dolcos, Florin, Yuta Katsumi, Matthew Moore, et al.. (2019). Neural correlates of emotion-attention interactions: From perception, learning, and memory to social cognition, individual differences, and training interventions. Neuroscience & Biobehavioral Reviews. 108. 559–601. 138 indexed citations
14.
Zilcha‐Mano, Sigal, et al.. (2019). Can Machine Learning Approaches Lead Toward Personalized Cognitive Training?. Frontiers in Behavioral Neuroscience. 13. 64–64. 17 indexed citations
15.
Aue, Tatjana, et al.. (2018). Expectancies influence attention to neutral but not necessarily to threatening stimuli: An fMRI study.. Emotion. 19(7). 1244–1258. 10 indexed citations
16.
Okon‐Singer, Hadas, Noga Cohen, Doron Todder, et al.. (2016). Attentional bias in clinical depression and anxiety: The impact of emotional and non-emotional distracting information. Biological Psychology. 122. 4–12. 34 indexed citations
17.
Simon, Eti Ben, Noga Oren, Haggai Sharon, et al.. (2015). Losing Neutrality: The Neural Basis of Impaired Emotional Control without Sleep. Journal of Neuroscience. 35(38). 13194–13205. 78 indexed citations
18.
Okon‐Singer, Hadas, Jan Mehnert, Jürgen Hoyer, et al.. (2014). Neural Control of Vascular Reactions: Impact of Emotion and Attention. Journal of Neuroscience. 34(12). 4251–4259. 26 indexed citations
19.
Okon‐Singer, Hadas, et al.. (2014). The modern search for the Holy Grail: is neuroscience a solution?. Frontiers in Human Neuroscience. 8. 388–388. 5 indexed citations
20.
Rohr, Christiane S., Hadas Okon‐Singer, R. Cameron Craddock, Arno Villringer, & Daniel S. Margulies. (2013). Affect and the Brain's Functional Organization: A Resting-State Connectivity Approach. PLoS ONE. 8(7). e68015–e68015. 28 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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