Dr. David Kellen

Research Interests:

Recognition memory, multinomial processing tree models, signal detection theory, and pretty much anything related to the development and implementation of measurement models for response accuracy data (unfortunately, I still haven't found the time to explore the world of response time data).

Publications:

Note: Due to racketeering copyright restrictions, these manuscripts are password protected. In any case, KELLEN might help you.

Singmann, H., & Kellen, D.  (submitted). MPTinR: Analysis of Multinomial Processing Tree models with R. Behavior Research Methods.

Kellen, D., Klauer, K. C., & Singmann, H. (in press). On the measurement of criterion noise in Signal Detection Theory: The case of recognition memory. Psychological Review.

Klauer, K. C., & Kellen, D. (2012). The Law of Categorical Judgment (Corrected) extended: A note on Rosner and Kochanski (2009). Psychological Review, 119, 216-220.

Klauer, K. C., & Kellen, D. (2011). The flexibility of models of recognition memory: An analysis by the minimum-description length principle. Journal of Mathematical Psychology, 55, 430-450.

Kellen, D., & Klauer, K. C. (2011). Evaluating models of recognition memory using first- and second-choice responses. Journal of Mathematical Psychology, 55, 251–266.

Klauer, K. C., & Kellen, D. (2011). Assessing the belief bias effect with ROCs: Reply to Dube, Rotello, and Heit (2010). Psychological Review, 118, 164-173.

Klauer, K. C., & Kellen, D. (2010). Toward a complete decision model of item and source recognition: A discrete-state approach. Psychonomic Bulletin & Review, 17, 465-478.

Invited Presentations:

Recognition memory modeling: From confidence ratings to item rankings, and back. June, 21st, 2012. Universität Basel (Prof. Hertwig / Prof. Rieskamp)

Recognition memory modeling: Beyond ROCs. September 29th, 2011. Mannheim University (Prof. Erdfelder)

Invited Workshops:

An introduction to R. August 6th and 13th, 2012. Universität Zürich (Prof. Oberauer)

An introduction to R: Data analysis and modeling methods. October 19th and 24th, 2011. Mannheim University (Prof. Erdfelder)

Conference Presentations:

Kellen, D., Klauer, K. C., & Singmann, H. (2011, July). Beyond ROCs: Fitting and extending recognition memory models with multiple-alternative, multiple-response tasks. Paper presented at the 44th Annual Meeting of the Society for Mathematical Psychology, Boston, Massachusetts, July 2011.

Kellen, D., & Klauer, K. C. (2012, April). The flexibility of models of recognition memory: An analysis by the minimum-description length principle. Paper presented at the Tagung experimentell arbeitender Psychologen 2012, Mannheim, Germany, April, 2012.

Conference Posters:

Singmann, H., Kellen, D., Hölzenbein, F., & Klauer, K. C. (2011, July). MPTinR: An (almost) complete R package for analyzing MPTs. Poster presented at the 44th Annual Meeting of the Society for Mathematical Psychology, Boston, Massachusetts, July 2011.

Ad-hoc reviews:

Experimental Psychology

Frontiers in Cognitive Science

Journal of Experimental Psychology: General

Office:

Institut für Psychologie der Albert-Ludwigs-Universität Freiburg

Engelbergerstr. 41  

D-79085 Freiburg im Breisgau

Raum 4035,  4. OG

Phone: 0761-203-2123

Email:  david.kellenpsychologieuni-freiburgde

If you're interested in my work, drop me a line or two, but please don't send me any Word attachments.

Seminar Classes 2011/2012:

Im giving a Social Psychology seminar in the 2011/2012 Winter Semester. There is a Sprechstunde (Mittwoch, 11.00-12.00 Uhr), but I am relatively tolerant with meeting outside of this schedule. If you need to come some other time, just send me an email (or show up) and I will see what I can do for you.

Fitting MPT models in R: the MPTinR package:

Henrik Singmann and myself developed a R  package to fit MPT models in a convenient manner. You can get it here. Future versions will have additional features (did somebody say hierarchical?) and will be faster as well (it already is). If you have any suggestions and/or problems, please contact us. 

 The good stuff:

R project

WinBUGS

LaTeX

Python

xkcd

A reading list on Bayesian methods

Because we do not understand the brain very well we are constantly tempted to use the latest technology as a model for trying to understand it. In my childhood we were always assured that the brain was a telephone switchboard. ('What else could it be?') I was amused to see that Sherrington, the great British neuroscientist, thought that the brain worked like a telegraph system. Freud often compared the brain to hydraulic and electro-magnetic systems. Leibniz compared it to a mill, and I am told some of the ancient Greeks thought the brain functions like a catapult. At present, obviously, the metaphor is the digital computer.  - John R. Searle, "Brains, Minds, and Science"

- "Suppose there are a series of little drawers in the brain..."
- "I have never seen any drawers in there"
- "They're very small"

- "But modeling devices that make sense for an unbiased decision-maker may not make sense for a biased one. For example, why would individuals have priors and posteriors if they are destined to apply Bayes’ law incorrectly?"
- "Because it works, bitches."  

- “No, I'm serious. If it wasn't for the bats, insects would take over the world.”
- “What, you mean like, replace world leaders and occupy positions of social and economic power?”