Dr. Anna Thorwart
Auslandsbeauftragte, ECTS-Beauftragte, Wiss. Mitarbeiterin
Contact information+49 6421 28-23670 +49 6421 28-26621 anna.thorwart@staff 1 erasmus4@ 1 Gutenbergstraße 18
G|01 Institutsgebäude (Room: 01041 resp. +1041)
To contact me concerning a question about an international study exchange or to make an appointment, please see International Exchange
|2000 - 2006||Degree in psychology (Diplom), Philipps-Universitaet Marburg|
|2007 - 2010||PhD-student in graduate program "NeuroAct"|
|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|
|2016-2018||Visiting Fellow, University of New South Wales|
- Tools to simulate various associative learning theories(ALTSim, Rescorla-Wagner, Pearce, Harris, Replaced Elements, Inhibited Elements, AMAN)
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.
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.
Inhalt ausklappen Inhalt einklappen Flexible representations in associative learning - a part/whole or a capacity question?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.
Inhalt ausklappen Inhalt einklappen Outcome Predictability - how previous experience influence subsequent learningOutcome Predictability - how previous experience influence subsequent learning
DFG Projects, 2014-2021.
As we learn faster about stimuli that previously were highly predictive, 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 my PhD student Gen Hartanto, Evan Livesey, University of Sydney, Oren Griffiths, Flinders University, and my former PhD student Wei Liu.
Inhalt ausklappen Inhalt einklappen Connecting associative learning theories with other research fieldsConnecting 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. The familiarity with historically rather distinct methods and concepts ensures that the question which learning processes control and interact in which paradigm remains a constant thread in my research, currently for example what happens if humans learn in more realistic and enriched environments created in an VR. I'm also interested where and which of these processes govern behaviour outside the learning laboratory, for example the clinical relevance of conditioning and learning processes, and which ideas and solutions other models, for example Bayesian inference models, offer. Projects are done in collaboration with for example Dominik Endres, Marburg, Tanja Hechler, Trier, and my PhD student Midhula Chandran.
Thorwart, A. & Lachnit, H.(2020). Inhibited Elements Model — Implementation of an associative learning theory. Journal of Mathematical Psychology, 102310, https://doi.org/10.1016/j.jmp.2019.102310. Open acces.
Livesey, E., Greeneway, J. K., Schubert, S. & Thorwart, A. (2019). Testing the deductive inferential account of blocking in causal learning. Memory & Cognition. doi:10.3758/s13421-019-00920-w. Accepted version
Griffiths, O., Livesey, E., & Thorwart, A. (2019). Learned biases in the processing of outcomes: A brief review of the outcome predictability effect. Journal of Experimental Psychology: Animal Learning and Cognition, 45(1), 1-16. doi:10.1037/xan0000195. Accepted version
Livesey, E. J., Don, H. J., Uengoer, M., & Thorwart, A. (2019). Transfer of associability and relational structure in human associative learning. Journal of Experimental Psychology: Animal Learning and Cognition, 45(2), 125-142. doi:10.1037/xan0000197. Accepted version
Gollwitzer, M., Thorwart, A., & Meissner, K., (2018). Editorial: Psychological Responses to Violations of Expectations. Frontiers in Psychology, 8, 2357. doi: 10.3389/fpsyg.2017.02357.
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, 43(4), 341-365. 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 Psychology, 70, 1366-1379. doi:10.1080/17470218.2016.1184290, simulation files
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. 7:1638. doi: 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, simulation files
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.
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 4.0]
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
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