Harald Lachnit

2.4k total citations
105 papers, 1.8k citations indexed

About

Harald Lachnit is a scholar working on Cognitive Neuroscience, Developmental and Educational Psychology and Sensory Systems. According to data from OpenAlex, Harald Lachnit has authored 105 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 72 papers in Cognitive Neuroscience, 37 papers in Developmental and Educational Psychology and 21 papers in Sensory Systems. Recurrent topics in Harald Lachnit's work include Memory and Neural Mechanisms (60 papers), Child and Animal Learning Development (31 papers) and Olfactory and Sensory Function Studies (21 papers). Harald Lachnit is often cited by papers focused on Memory and Neural Mechanisms (60 papers), Child and Animal Learning Development (31 papers) and Olfactory and Sensory Function Studies (21 papers). Harald Lachnit collaborates with scholars based in Germany, United Kingdom and United States. Harald Lachnit's co-authors include Martín Giurfa, Klaus G. Melchers, Nina Deisig, H. D. Kimmel, Metin Uengoer, David R. Shanks, Jean‐Christophe Sandoz, Annette Kinder, Anna Thorwart and Stephan König and has published in prestigious journals such as PLoS ONE, Pain and Behaviour Research and Therapy.

In The Last Decade

Harald Lachnit

101 papers receiving 1.8k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Harald Lachnit Germany 26 1.1k 561 516 368 312 105 1.8k
Alan Silberberg United States 27 772 0.7× 625 1.1× 1.1k 2.2× 135 0.4× 89 0.3× 86 2.6k
James W. Kalat United States 12 1.0k 1.0× 406 0.7× 294 0.6× 149 0.4× 79 0.3× 33 2.2k
Donald M. Wilkie Canada 28 1.2k 1.1× 296 0.5× 845 1.6× 208 0.6× 100 0.3× 107 2.3k
Karli Watson United States 21 941 0.9× 326 0.6× 158 0.3× 145 0.4× 262 0.8× 45 2.1k
Kenneth W. Rusiniak United States 20 893 0.8× 667 1.2× 176 0.3× 150 0.4× 70 0.2× 26 2.3k
Aaron P. Blaisdell United States 24 961 0.9× 353 0.6× 566 1.1× 227 0.6× 55 0.2× 102 1.8k
Allan R. Wagner United States 35 2.4k 2.3× 1.3k 2.3× 1.1k 2.1× 208 0.6× 87 0.3× 81 3.9k
Donald S. Blough United States 29 1.1k 1.1× 339 0.6× 1.3k 2.6× 337 0.9× 62 0.2× 79 2.7k
Charles F. Flaherty United States 30 858 0.8× 1.1k 1.9× 409 0.8× 90 0.2× 124 0.4× 95 2.7k
Walter G. Hankins United States 14 769 0.7× 566 1.0× 145 0.3× 140 0.4× 62 0.2× 14 2.0k

Countries citing papers authored by Harald Lachnit

Since Specialization
Citations

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

Fields of papers citing papers by Harald Lachnit

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Harald Lachnit

This figure shows the co-authorship network connecting the top 25 collaborators of Harald Lachnit. A scholar is included among the top collaborators of Harald Lachnit 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 Harald Lachnit. Harald Lachnit 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.
Lachnit, Harald, et al.. (2025). Music as a contextual cue in human predictive learning. Learning and Motivation. 89. 102096–102096.
2.
Lachnit, Harald, et al.. (2021). An attentional perspective on differential fear conditioning in chronic pain: The informational value of safety cues.. Behaviour Research and Therapy. 144. 103917–103917. 3 indexed citations
3.
Livesey, Evan J., et al.. (2020). Does learning history shape the associability of outcomes? Further tests of the outcome predictability effect. PLoS ONE. 15(12). e0243434–e0243434. 1 indexed citations
4.
Uengoer, Metin, Harald Lachnit, & John M. Pearce. (2020). The role of common elements in the redundancy effect.. Journal of Experimental Psychology Animal Learning and Cognition. 46(3). 286–296. 2 indexed citations
5.
Uengoer, Metin, et al.. (2017). Reward Draws the Eye, Uncertainty Holds the Eye: Associative Learning Modulates Distractor Interference in Visual Search. Frontiers in Behavioral Neuroscience. 11. 128–128. 19 indexed citations
6.
Bustamante, Javier, Metin Uengoer, & Harald Lachnit. (2016). Reminder Cues Modulate the Renewal Effect in Human Predictive Learning. Frontiers in Psychology. 7. 1968–1968. 4 indexed citations
7.
Riecke, Jenny, et al.. (2016). Cross-cultural adaption of the German Quebec Back Pain Disability Scale: an exposure-specific measurement for back pain patients. Journal of Pain Research. 9. 9–9. 19 indexed citations
8.
Lachnit, Harald, et al.. (2013). The informational value of contexts affects context-dependent learning. Learning & Behavior. 41(3). 285–297. 34 indexed citations
9.
Thorwart, Anna, Steven Glautier, & Harald Lachnit. (2010). Convergent results in eyeblink conditioning and contingency learning in humans: Addition of a common cue does not affect feature-negative discriminations. Biological Psychology. 85(2). 207–212. 4 indexed citations
10.
Thorwart, Anna, Holger Schultheis, Stephan König, & Harald Lachnit. (2009). ALTSim: A MATLAB simulator for current associative learning theories. Behavior Research Methods. 41(1). 29–34. 27 indexed citations
11.
Schultheis, Holger, Anna Thorwart, & Harald Lachnit. (2008). Rapid-REM: A MATLAB simulator of the replaced-elements model. Behavior Research Methods. 40(2). 435–441. 12 indexed citations
12.
Melchers, Klaus G., et al.. (2006). Extinction of conditioned inhibition through nonreinforced presentation of the inhibitor. Psychonomic Bulletin & Review. 13(4). 662–667. 16 indexed citations
13.
Melchers, Klaus G., Harald Lachnit, & David R. Shanks. (2004). Within-compound associations in retrospective revaluation and in direct learning: A challenge for comparator theory. The Quarterly Journal of Experimental Psychology Section B. 57(1). 25–53. 61 indexed citations
14.
Sandoz, Jean‐Christophe, et al.. (2003). Non-elemental processing in olfactory discrimination tasks needs bilateral input in honeybees. Behavioural Brain Research. 145(1-2). 135–143. 29 indexed citations
15.
Lachnit, Harald, et al.. (2002). Evidence for the utilization of distinctive features in nonlinear discrimination problems. Biological Psychology. 61(3). 277–292. 4 indexed citations
16.
Lachnit, Harald, et al.. (2002). Configural learning in human Pavlovian conditioning: acquisition of a biconditional discrimination. Biological Psychology. 59(2). 163–168. 16 indexed citations
17.
18.
Lachnit, Harald, et al.. (2000). Further investigations of stimulus coding in nonlinear discrimination problems. Biological Psychology. 55(1). 57–73. 13 indexed citations
19.
Lachnit, Harald & Annette Kinder. (2000). Stimulus representations in human Pavlovian conditioning: Implications of missing negative transfer across response systems. The Quarterly Journal of Experimental Psychology Section B. 53(3). 209–224. 13 indexed citations
20.
Lachnit, Harald & H. D. Kimmel. (2000). Experimental manipulation of a unique cue in Pavlovian SCR conditioning with humans. Biological Psychology. 53(2-3). 105–129. 16 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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