Edited Books:

Schulte-Mecklenbeck, M., Kühberger, A. & Johnson, J. (Eds.). (2018). A Handbook of Process Tracing Methods. New York: Taylor & Francis.

Schulte-Mecklenbeck, M., Kühberger, A. & Ranyard, R. (Eds.). (2011). A Handbook of Process Tracing Methods for Decision Research: A Critical Review and User’s Guide. New York: Taylor & Francis.

Published chapters and journal articles:

Michael O’Donnell et al. (2018). Registered Replication Report: Dijksterhuis and van Knippenberg (1998). Current Directions in Psychological Science.

Pachur, T., Schulte-Mecklenbeck, M., Murphy, R.O., & Hertwig, R. (2018). Prospect theory reflects selective allocation of attention. Journal of Experimental Psychology: General, 147(2), 147–169.

de Bellis, E., Schulte-Mecklenbeck, M., Brucks, W., Herrmann, A., & Hertwig, R. (2018). Blind haste: As light decreases, speeding increases. PLoS ONE 13(1): e0188951.

Stöckli, S., Schulte-Mecklenbeck, M., Borer, S., & Samson, A. C. (2017). Facial expression analysis with AFFDEX and FACET: A validation study. Behavior Research Methods, 1-15.

Schulte-Mecklenbeck, M., Kühberger, A., Gagl, S., & Hutzler, F. (2017). Inducing thought processes: Bringing process measures and cognitive processes closer together. Journal of Behavioral Decision Making, 30(5), 1001-1013.

Schulte-Mecklenbeck, M., Johnson, J.G., Böckenholt, U., Goldstein, D., Russo, J., Sullivan, N., &  Willemsen, M. (2017). Process tracing methods in decision making: On growing up in the 70ties. Current Directions in Psychological Science, 26(5), 442-450.

Kühberger, A. & Schulte-Mecklenbeck, M. (2017). Economic decision making: risk, value and affect. In R. Ranyard (Ed.). Economic Psychology, 20-34. John Wiley & Sons, Ltd: Chichester, UK.

Schulte-Mecklenbeck, M., Spaanjaars, N.L., & Witteman, C.L.M. (2017). The (in)visibility of psychodiagnosticians’ expertise. Journal of Behavioral Decision Making, 30, 89-94.

Lejarraga, T., Schulte-Mecklenbeck, M., & Smedema, D. (2017). The pyeTribe: Simultaneous eyetracking for economic games. Behavior Research Methods, 49(5), 1769-1779.

Kieslich, P. J., Wulff, D. U., Henninger, F., Haslbeck, J. M. B., & Schulte-Mecklenbeck, M. (2016). Mousetrap: An R package for processing and analyzing mouse-tracking data. CRAN.

Skvortsova, A., Schulte-Mecklenbeck, M., Jellema, S., Sanfey, A., & Witteman, C. (2016). Deliberative versus intuitive psychodiagnostic decision. Psychology7, 1438–1450.

Schulte-Mecklenbeck, M., & Kühberger, A. (2014). Out of sight – out of mind?  Information acquisition patterns in risky choice framing. Polish Psychological Bulletin, 45, 21–28.
Schulte-Mecklenbeck, M., Sohn, M., De Bellis, E., Martin, N., & Hertwig, R. (2013). A lack of appetite for information and computation. Simple heuristics in food choice. Appetite, 71, 242–251.
 Schulte-Mecklenbeck, M. & Murphy, R.O. (2012). Flashlight as an online process tracing method. In Z. Yan (Ed.). Encyclopedia of Cyber Behavior. (p. 88–95). IGI Global: Hershey, PA.
 Schulte-Mecklenbeck, M., Kühberger, A., & Ranyard, R. (2011). The Role of Process Data in the Development and Testing of Process Models of Judgment and Decision Making. Judgement and Decision Making, 6(8), 733–739.
 Schulte-Mecklenbeck, M., Murphy, R. O., & Hutzler, F. (2011). Flashlight: Recording Information Acquisition Online. Computers in human behavior, 27, 1771–1782.
Huber, O., Huber, O.W., & Schulte-Mecklenbeck, M. (2011). Determining the information participants need – Methods of Active Information Search. In M. Schulte-Mecklenbeck, A., Kühberger, A. & R. Ranyard (Eds.). A Handbook of Process Tracing Methods for Decision Research: A Critical Review and User’s Guide. New York: Taylor & Francis.
Kühberger, A., Schulte-Mecklenbeck, M., & Ranyard, R. (2011). Windows for understanding the mind: Introduction to A Handbook of Process Tracing Methods for Decision Research. In M. Schulte-Mecklenbeck, A., Kühberger, A. & R. Ranyard (Eds.). A Handbook of Process Tracing Methods for Decision Research: A Critical Review and User’s Guide. New York: Taylor & Francis.
 Norman, E. & Schulte-Mecklenbeck, M. (2009). Take a careful click at that! Mouselab and eye-tracking as tools to measure intuition. In: A. Glöckner & C. Witteman (Eds). Foundations for Tracing Intuition: Challenges and methods. London: Psychology Press.
 Schulte-Mecklenbeck, M., & Murphy, R.O. (2009). Prozessdaten online erheben: Verschiedene Methoden im Uberblick. In: N. Jackob, H. Schoen, & T. Zerback. Sozialforschung im Internet (p. 197–209). Wiesbaden, Verlag fur Sozialwissenschaften.
 Schulte-Mecklenbeck, M. (2008). Brave New World … Wide Web: Blending Old Teaching Methods With a Cutting-edge Virtual Learning Environment. In: B. Perlman,L.I. McCann, & S.H. McFadden. Lessons Learned (Vol. 3): Practical advice for the teaching of psychology (p. 109-118). Washington, Association for Psychological Science.
 Johnson, E.J., Schulte-Mecklenbeck, M., & Willemsen, M. (2008). Process Models deserve Process Data: Comment on Brandstätter, Gigerenzer, & Hertwig (2006). Psychological Review, 115(1), 263-272.
 Johnson, E.J., Schulte-Mecklenbeck, M., & Willemsen, M.C. (2008). Postscript: Rejoinder to Brandstätter, Gigerenzer, and Hertwig (2008). Psychological Review, 115, 1, 272-273.
Schulte-Mecklenbeck, M. (2007). Information processing as one key for a unification? Behavioral and Brain Sciences, 30(1), 40 – 40.
Schulte-Mecklenbeck, M. (2006). Assessment durch Feedback. In: F. Gertsch (Ed.), Das Moodle Praxisbuch (S. 29 – 39). Munchen: Addison Wesley.
 Schulte-Mecklenbeck, M. (2006). Virtual Learning Environment. Planung und Durchführung einer webbasierten Übung in der Psychologie . In: S. Wehr (Ed.), Hochschullehre – adressatengerecht und wirkungsvoll (S. 57 – 92). Bern: Haupt Verlag.
 Schulte-Mecklenbeck, M., & Neun, M. (2005). WebDiP – a tool for information search experiments on the World-Wide-Web. Behavior Research Methods, 37(2), 293 – 300.
 Schulte-Mecklenbeck, M. (2005). Tracing the decision maker. Unpublished PhD Thesis. University of Fribourg, Switzerland.
 Schulte-Mecklenbeck, M. (2004). Brave New World … Wide Web. Blending Old Teaching Methods With a Cutting-edge Virtual Learning Environment. APS Observer, 17 (10), 48 – 53.
 Schulte-Mecklenbeck, M. & Huber, O. (2003). Information Search in the Laboratory and on the Web: With or Without an Experimenter. Behavior Research Methods, Instruments & Computers, 35(2), 227 – 235.
 Kühberger, A., Schulte-Mecklenbeck, M., & Perner, J. (2002). Framing decisions: hypothetical and real. Organizational Behavior and Human Decision Processes, 89, 1162 – 1175.
 Kühberger, A., Schulte-Mecklenbeck, M., & Perner, J. (1999). The effect of probabilities and payoff on framing: A meta-analysis and an empirical test. Organizational Behavior and Human Decision Processes, 78(3), 204 – 231.
 Schulte, M. (1998). Framing in real and hypothetical decision situations. Unpublished Master Thesis. University of Salzburg, Austria.
 Kühberger, A., Perner, J., Schulte, M., & Leingruber, R. (1995). Choice or No Choice. Is the Langer Effect Evidence against Simulation? Mind and Language, 10(4), 423 – 436.


Recent Posts

Blind Haste (aka im Blindflug)

Chance encounters sometimes lead to interesting and new projects. This is one of those cases … I got to know Emanuel de Bellis during my time at Nestle and we never stopped collaborating ever since (he actually lead my 2017 ‘skype to’ statistics with my wife as a close second …)

We got our hands on a rich dataset of speed measures the police in Zürich (Switzerland) does unbeknownst to the drivers during they year for planning purposes. The radar is put into a small black box hardly anybody realises driving by:

(it’s the black box above the bin – not the bin!)

So – we got these data from over a million cars and got to work with them trying to find an answer to a question in perception research: Do people perceive their environment differently when light conditions deteriorate? and (even more important) Do drivers change their driving speed accordingly.

Well – they don’t … and here is correlational proof 🙂

Stay tuned for a causal demonstration – oh yes!

Blind haste: As light decreases, speeding increases

Worldwide, more than one million people die on the roads each year. A third of these fatal accidents are attributed to speeding, with properties of the individual driver and the environment regarded as key contributing factors. We examine real-world speeding behavior and its interaction with illuminance, an environmental property defined as the luminous flux incident on a surface. Drawing on an analysis of 1.2 million vehicle movements, we show that reduced illuminance levels are associated with increased speeding. This relationship persists when we control for factors known to influence speeding (e.g., fluctuations in traffic volume) and consider proxies of illuminance (e.g., sight distance). Our findings add to a long-standing debate about how the quality of visual conditions affects drivers’ speed perception and driving speed. Policy makers can intervene by educating drivers about the inverse illuminance‒speeding relationship and by testing how improved vehicle headlights and smart road lighting can attenuate speeding.

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