Literatur

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Einführung in die Methoden & Versuchsplanung

  • Bortz, J., Döring, N. (2006) Forschungsmethoden und Evaluation. Berlin: Springer
  • Goodwin, C. J. (2009) Research in Psychology. Hoboken NJ: Wiley
  • Hecht, H., Desnizza, W. (2012) Psychologie als empirische Wissenschaft. Berlin: Springer
  • Herzog, W. (2012) Wissenschafts-theoretische Grundlagen der Psychologie. Berlin: Springer
  • Hussy, W., & Jain, A. (2002). Experimentelle Hypothesenprüfung in der Psychologie. Göttingen: Hogrefe
  • Hussy, W., Schreier, M., Echterhoff, G. (2010) Forschungsmethoden in Psychologie und Sozialwissenschaften für Bachelor. Berlin: Springer
  • Kukla, A., & Walmsley, J. (2006). Mind: A Historical and Philosophical Introduction to the Major Theories. Indianapolis: Hackett Publishing
  • Sarris, V. (1992) Methodologische Grundlagen der Experimentalpsychologie, Bd.2, Versuchsplanung und Stadien des psychologischen Experiments. München: UTB
  • Schnell, R., Hill, P.B., Esser, E. (2011) Methoden der empirischen Sozialforschung. München: Oldenbourg
  • Whitley, B.E., Kite, M.E. (2013) Principles of Research in Behavioral Science. New York: Routledge
  • Walach, H. (2013) Psychologie: Wissenschaftstheorie, philosophische Grundlagen und Geschichte. Stuttgart: Kohlhammer


Kognitive Modellierung

  • Anders, R., Alario, F., & Van Maanen, L. (2016). The shifted Wald distribution for response time data analysis. Psychological methods, 21(3), 309.
  • Anderson, B. (2014). Computational neuroscience and cognitive modelling: a student's introduction to methods and procedures. Sage.
  • Anderson, J. R., Bothell, D., Byrne, M. D., Douglass, S., Lebiere, C., & Qin, Y. (2004). An integrated theory of the mind. Psychological review, 111(4), 1036.
  • Anderson, J. R. (1996). ACT: A simple theory of complex cognition. American Psychologist, 51(4), 355.
  • Andres, J.: Das allgemeine lineare Modell. In Edgar Erdfelder, Rainer Mausfeld, Thorsten Meiser & Georg Rudinger (Hrsg.), Handbuch quantitative Methoden, 1996 (S.185-200); Weinheim: Belz.
  • Balota, D. A., & Yap, M. J. (2011). Moving beyond the mean in studies of mental chronometry: The power of response time distributional analyses. Current Directions in Psychological Science, 20(3), 160-166.
  • Bundesen, C., & Habekost, T. (2007). Models of attention.
  • Busemeyer, J. R., & Diederich, A. (2010). Cognitive modeling. Sage.
  • Domjan, M. (2014). The principles of learning and behavior. Nelson Education.
  • Forstmann, B. U., Ratcliff, R., & Wagenmakers, E. J. (2016). Sequential sampling models in cognitive neuroscience: Advantages, applications, and extensions. Annual review of psychology, 67.
  • Gaschler, R. (2014). Kognitive Architektur. In M. A. Wirtz (Hrsg.), Dorsch – Lexikon der Psychologie (18. Aufl., S. 839). Bern: Verlag Hogrefe Verlag.
  • Goldstone, R. L., & Janssen, M. A. (2005). Computational models of collective behavior. Trends in cognitive sciences, 9(9), 424-430.
  • Grimm, V., Revilla, E., Berger, U., Jeltsch, F., Mooij, W. M., Railsback, S. F., ... & DeAngelis, D. L. (2005). Pattern-oriented modeling of agent-based complex systems: lessons from ecology. Science, 310(5750), 987-991.
  • Heathcote, A., Brown, S. D., & Wagenmakers, E. J. (2015). An introduction to good practices in cognitive modeling. In An introduction to model-based cognitive neuroscience (pp. 25-48). Springer, New York, NY.
  • Honing, H. (2006). The role of surprise in theory testing: Some preliminary observations. In Proceedings of the international conference on music perception and cognition (pp. 38-42).
  • Izhikevich, E. M. (2007). Solving the distal reward problem through linkage of STDP and dopamine signaling. Cerebral cortex, 17(10), 2443-2452.
  • Kelso, J. S. (1997). Dynamic patterns: The self-organization of brain and behavior. MIT press.
  • Laird, J. E., Newell, A., & Rosenbloom, P. S. (1987). Soar: An architecture for general intelligence. Artificial intelligence, 33(1), 1-64.
  • Lins, J., & Schöner, G. (2014). A neural approach to cognition based on dynamic field theory. In Neural Fields (pp. 319-339). Springer, Berlin, Heidelberg.
  • Macho, S. (2002). Cognitive modeling with spreadsheets. Behavior Research Methods, Instruments, & Computers, 34(1), 19-36.
  • Mathys, C. D., Lomakina, E. I., Daunizeau, J., Iglesias, S., Brodersen, K. H., Friston, K. J., & Stephan, K. E. (2014). Uncertainty in perception and the Hierarchical Gaussian Filter. Frontiers in human neuroscience, 8, 825.
  • Matzke, D., & Wagenmakers, E. J. (2009). Psychological interpretation of the ex-Gaussian and shifted Wald parameters: A diffusion model analysis. Psychonomic bulletin & review, 16(5), 798-817.
  • McClelland, J. L. (2009). The place of modeling in cognitive science. Topics in Cognitive Science, 1(1), 11-38.
  • McKerchar, T. L., Green, L., Myerson, J., Pickford, T. S., Hill, J. C., & Stout, S. C. (2009). A comparison of four models of delay discounting in humans. Behavioural processes, 81(2), 256-259.
  • Miller, R., Scherbaum, S., Heck, D. W., Goschke, T., & Enge, S. (2018). On the relation between the (censored) shifted Wald and the Wiener distribution as measurement models for choice response times. Applied psychological measurement, 42(2), 116-135.
  • Portenier, C., & Gromes, W. (2005). Mathematik für Humanbiologen und Biologen. Fachbereich Mathematik und Informatik Philipps-Universität Marburg.
  • Ratcliff, R., Smith, P. L., Brown, S. D., & McKoon, G. (2016). Diffusion decision model: current issues and history. Trends in cognitive sciences, 20(4), 260-281.
  • Ratcliff, R., Spieler, D., & Mckoon, G. (2000). Explicitly modeling the effects of aging on response time. Psychonomic Bulletin & Review, 7(1), 1-25.
  • Rey, G. D., & Wender, K. F. (2008). Neuronale Netze. Eine Einführung in die Grundlagen, Anwendungen und Datenauswertung. Huber, Bern.
  • Rescorla, R. A. (2008) Rescorla-Wagner model. Scholarpedia, 3(3):2237.
  • Rescorla, R. A., & Wagner, A. R. (1972). A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. Classical conditioning II: Current research and theory, 2, 64-99.
  • Rudolf, M., & Müller, J. (2012). Multivariate Verfahren: eine praxisorientierte Einführung mit Anwendungsbeispielen in SPSS. Hogrefe Verlag.
  • Sandamirskaya, Y., Zibner, S. K., Schneegans, S., & Schöner, G. (2013). Using dynamic field theory to extend the embodiment stance toward higher cognition. New Ideas in Psychology, 31(3), 322-339.
  • Siegfried, R. (2009). Agent-based modeling and simulation. In Modeling and Simulation of Complex Systems (pp. 86–98).
  • Schoner, G., & Kelso, J. A. (1988). Dynamic pattern generation in behavioral and neural systems. Science, 239(4847), 1513-1520.
  • Sun, R. (2008). Introduction to computational cognitive modeling. Cambridge handbook of computational psychology, 3-19.
  • Tuller, B., Case, P., Ding, M., & Kelso, J. A. (1994). The nonlinear dynamics of speech categorization. Journal of Experimental Psychology: Human perception and performance, 20(1), 3.
  • Stephan, A., & Walter, S. (Eds.). (2013). Handbuch Kognitionswissenschaft. Springer-Verlag.
  • Voss, A., Rothermund, K., Gast, A., & Wentura, D. (2013). Cognitive processes in associative and categorical priming: A diffusion model analysis. Journal of Experimental Psychology: General, 142(2), 536.
  • Wilensky, U., & Resnick, M. (1999). Thinking in levels: A dynamic systems approach to making sense of the world. Journal of Science Education and technology, 8(1), 3-19.
  • Woergoetter, F. & Porr, B. (2008) Reinforcement Learning. Scholarpedia, 3(3):1448.


Statistische Grundbegriffe und Grundlagen multivariater Verfahren

  • Bock, J. (1998). Bestimmung des Stichprobenumfangs: Für biologische Experimente und kontrollierte klinische Studien. München: Oldenbourg.
  • Bortz, J., & Schuster, C. (2016). Statistik für Human- und Sozialwissenschaftler. Berlin: Springer.
  • Bühner, M. (2010). Einführung in die Test- und Fragebogenkonstruktion (3. Aufl.; Kapitel 6). München: Pearson Studium.
  • Clauß, G., Finze, F. R., & Partzsch, L. (2011). Grundlagen der Statistik für Soziologen, Pädagogen, Psychologen und Mediziner. Frankfurt: Europa-Lehrmittel
  • Cohen, J. (2013). Statistical power analysis for the behavioral sciences. Routledge.
  • Collingridge, D. S. (2013). A primer on quantitized data analysis and permutation testing. Journal of Mixed Methods Research, 7(1), 81-97.
  • Eid, M., Gollwitzer, M., & Schmitt, M. (2017). Statistik und Forschungsmethoden. Weinheim: Beltz.
  • Hayes, A. F., & Cai, L. (2007). Using heteroskedasticity-consistent standard error estimators in OLS regression: An introduction and software implementation. Behavior research methods, 39(4), 709-722.
  • Heiler, S., & Weichselberger, K. (1969). Über den Permutationstest und ein daraus ableitbares Konfidenzintervall. Metrika, 14(1), 232-248.
  • Holling, H. & Gediga, G. (2010). Statistik - Deskriptive Verfahren. Göttingen: Hogrefe.
  • Manly, B.F. (2018). Randomization, bootstrap and Monte Carlo Methods in biology. Chapman and Hall/CRC
  • Rudolf, M. & Buse, J. (2020). Multivariate Verfahren. Eine praxisorientierte Einführung mit Anwendungsbeispielen. Göttingen: Hogrefe.
  • Rudolf, M., & Kuhlisch, W. (2008). Biostatistik: Eine Einführung für Biowissenschaftler. München: Pearson Studium.