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Dr. Anna Thorwart

Auslandsbeauftragte, ECTS-Beauftragte, Wiss. Mitarbeiterin

Philipps-Universität Marburg » Psychologie » AG Allgemeine und Biologische Psychologie » Assoziatives Lernen

Telefon* 28-23670
Fax* 28-26621
E-Mail* anna.thorwart@staff
Raum 01041
Gebäude Alte Jägerkaserne
Adresse Gutenbergstraße 18
35032 Marburg (Paketpost: 35037 Marburg)

*Die allgemeine Vorwahl für die Telefonnummer lautet "+49 (0)6421". E-Mail-Adressen sind nur im Intranet klickbar. Um eine richtige E-Mail-Adresse zu erhalten, fügen Sie bitte ".uni-marburg.de" bzw. "uni-marburg.de" an.




2000 - 2006 Degree in psychology (Diplom), Philipps-Universitaet Marburg
2007 - 2010 PhD-student in graduate program "NeuroAct"
April-July 2007 Visiting PhD student with Steven Glautier, University of Southampton
2010 - 2012 DAAD Postdoctoral research fellow with Justin Harris, University of Sydney
2012 - 2013 Research Associate, University of Sydney
2013 -2015 Acting Professor for Experimental Psychology (Vertretungsprofessur), Philipps-Universitaet Marburg
since March 2015
Research Fellow & Lecturer, Philipps-Universitaet Marburg
since December 2016  Visiting Fellow, University of New South Wales  

Coordination of International Exchange 

To contact me concerning a question about an international study exchange or to make an appointment me, please see Kontakt


In allen Projekten gibt es Möglichkeiten für Bachelor-, Master- und Diplomarbeiten. Außerdem bin ich auch immer offen für Themenvorschläge aus allen Bereichen der Lernpsychologie. Kontaktieren Sie mich direkt.


I'm interested in how we learn about apparently simple relationships between events based on our experience with these events, for example how we learn the relationship between "raining" and cues that precede "raining", e.g. a certain kind of clouds, people caring an umbrella and the cloud symbol on today's front page of the local newspaper. To investigate the processes and mechanisms responsible for such learning, I'm using mainly associative learning models as a theoretical basis and human causal learning and  eye tracking during computer games as empirical counterparts. 

AMAN - the space rocket version
AN ATTENTION-MODULATED ASSOCIATIVE NETWORK (AMAN) - exemplary network structure for three elements

Flexible representations in associative learning - a part/whole or a capacity question?
Postdoctoral Fellowship by the German Academic Exchange Service (DAAD) and the University of Sydney, 2010-2012.

This project is linked to the debate over the way predictive stimuli and signals are represented and processed during learning. Across several experiments and papers, we established that humans differ in the way they represent and process stimuli. Furthermore and in contrast to the standard framing of the debate, we investigate whether the crucial question is not whether the stimulus representation is configural or elemental but rather whether the representation of complex stimuli is affected by normalisation processes.

In order to model the flexibility and complexity of stimulus processing during learning, complex mathematical theories are necessary. In addition to the experimental verification of these theories, one contributions of this projects to the field of associative learning consists in providing tools and examples for computational modelling of such associative learning theories


Learned Predictiveness and Outcome Predictability - how previous experience influence subsequent learning
DFG Project, 2014-2017.

As we learn faster about stimuli that previously were highly predicitive, do we also learn faster about stimuli that were highly predictable? This project investigates whether the successful prediction of an event in the past influences learning about this event in new situations. It focusses on questions concerning the rate at which humans learn about a stimulus and its associations with other events, its associability. First, how is associability influenced by the extent to which that stimulus has been predictable in the past, that is the predictability of the stimulus? Second, how does the influence of stimulus predictability interact with the already known effects of the utility of the stimulus in predicting other events, that is the learned predictiveness of the stimulus? In addition to the causal learning paradigm, which is the standard procedure for the learned predictiveness effect, the current project will take advantage of an original paradigm for classical conditioning of the gaze that was developed in cooperation with J. Harris and E. Livesey from the University of Sydney and allows the simultaneous measurement of learning and attentional effects.

This project is done in close collaboration with Evan Livesey, University of Sydney, and my PhD student Wei Liu.

Connecting associative learning theories with other research fields
RTG 2271, 2017-2021 

When I started, my research focused on eye blink conditioning and skin conductance conditioning. During my PhD, I switched to more cognitive paradigms, like causal and predictive learning. This familiarity with historically rather distinct methods and concepts allows me to approach my projects from a unique position and the question which learning processes control and interact in which paradigm remains a constant thread in my research. In collaborations with colleagues, I'm also interested where and which of these processes govern behaviour outside the conditioning laboratory, in particular the clinical relevance of conditioning and learning processes.  


example of causal learning trial
example of causal learning trial
Selected Publications

Thorwart, A., Livesey, E., Wilhelm, F., Liu, W. & Lachnit, H. (2017). Learned Predictiveness and Outcome Predictability effects are not simply two sides of the same coin. Journal of Experimental Psychology: Animal Learning and Cognition, in press. doi:10.1037/xan0000150. 

Griffiths, O. & Thorwart, A. (2017). Effects of Outcome Predictability on Human Learning. Frontiers in Psychology, 8, 511. doi:10.3389/fpsyg.2017.00511.

Thorwart, A., Uengoer, M, Livesey, E. & Harris, J. (2017). Summation effects in human learning: evidence from patterning discriminations in goaltracking. Quarterly Journal of Experimental Psychology70, 1366-1379. doi:10.1080/17470218.2016.1184290

Thorwart, A & Livesey, E.J. (2016). Three ways that non-associative knowledge may affect associative learning processes. Front. Psychol. 7:2024. doi: 10.3389/fpsyg.2016.02024

Hechler, T., Endres, D.  & Thorwart, A. (2016). Why harmless sensations might hurt in individuals with chronic pain: About heightened prediction and perception of pain in the mind. Front. Psychol. 7:1638doi: 10.3389/fpsyg.2016.01638

Bustamante, J., Uengoer, M., Thorwart, A. & Lachnit, H. (2016). Extinction in multiple contexts: Effects on the rate of extinction and the strength of response recovery. Learning & Behavior, 44, 283–294. doi:10.3758/s13420-016-0212-7.

Rief, W., Glombiewski, J. A., Gollwitzer, M., Schubö, A., Schwarting, R., Thorwart, A. (2015). Expectancies as core features of mental disorders. Current Opinion in Psychiatry, 28, 378-385. doi:10.1097/yco.0000000000000184

Lachnit H, Thorwart A, Schultheis H, Lotz A, Koenig S, et al. (2013) Indicators of Early and Late Processing Reveal the Importance of Within-Trial-Time for Theories of Associative Learning. PLoS ONE 8(6):e66291. doi:10.1371/journal.pone.0066291

Thorwart, A., Livesey, E. J., & Harris, J. A. (2012). Normalisation between stimulus elements in a model of Pavlovian conditioning: Showjumping on an elemental horse. Learning & Behavior, 40, 334-346. doi:10.3758/s13420-012-0073-7. AMAN MODEL SIMULATOR

Livesey, E. J., Thorwart, A., & Harris, J. A. (2011). Comparing positive and negative patterning in human learning. Quarterly Journal of Experimental Psychology, doi:10.1080/17470218.2011.605153.

Livesey, E. J., Thorwart, A., De Fina, N. L., & Harris, J. A. (2011). Comparing learned predictiveness effects within and across compound discriminations. Journal of Experimental Psychology: Animal Behavior Processes, 37, 446-65, doi: 10.1037/a0023391.

Glautier, S., Redhead, E., Thorwart, A., & Lachnit, H. (2010). Reduced Summation with Common Features in Causal Judgements. Experimental Psychology, 57, 252-259, doi: 10.1027/1618-3169/a000030

Thorwart, A., Glautier, S. & Lachnit, H. (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, 207 – 212, doi: 10.1016/j.biopsycho.2010.07.002

Thorwart, A. & Lachnit, H. (2010). Generalization decrements: Further support for flexibility in stimulus processing. Learning & Behavior, 38, 367-373, doi: 10.3758/LB.38.4.367

Thorwart, A., Schultheis, H., König, S. & Lachnit, H. (2009). ALTSim: A MATLAB simulator for current associative learning theories. Behavior Research Methods, 41(1), 29-34. doi:10.3758/BRM.41.1.29, simulator [new version 3.9]

Thorwart, A. & Lachnit, H. (2009). Symmetrical generalization decrements: configural stimulus processing in human contingency learning. Learning & Behavior, 37, 107-115. doi:10.3758/LB.37.1.107

Schultheis, H., Thorwart, A., & Lachnit., H. (2008a). HMS: A MATLAB simulator of the Harris model of associative learning.Behavior Research Methods, 40, 442-449.doi:10.3758/BRM.40.2.442, simulator

Schultheis, H., Thorwart, A., & Lachnit, H. (2008b). Rapid-REM: A MATLAB simulator of the replaced elements model. Behavior Research Methods, 40, 435-441. doi:10.3758/BRM.40.2.442, Simulator

Zuletzt aktualisiert: 07.09.2017 · Anna Thorwart

Fb. 04 - Psychologie

Arbeitsgruppe Assoziatives Lernen, Gutenbergstraße 18, 35032 Marburg
Tel. +49 6421/28-23689, Fax +49 6421/28-26621, E-Mail: ag-lachnit@staff.uni-marburg.de

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