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Analysis of RT distributions with R

Analysis of RT distributions with R. Emil Ratko-Dehnert WS 2010/ 2011. About me. Studied Mathematics (LMU) „Kalman Filter, State-space models and EM-algorithm“ Dr. candidate under Prof. Müller, Dr. Zehetleitner Research Interest: Visual attention and memory

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Analysis of RT distributions with R

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  1. Analysis of RT distributionswith R Emil Ratko-Dehnert WS 2010/ 2011

  2. About me • Studied Mathematics (LMU) • „Kalman Filter, State-space models and EM-algorithm“ • Dr. candidate under Prof. Müller, Dr. Zehetleitner • Research Interest: • Visual attention and memory • Formal modelling and systems theory • Philosophy of mind

  3. About the students • Your name and origin? • Your educational background? • Your research interests/ experience? • Any statistical/ programming skills? • What are your expectations about the course?

  4. Concept of the course • Where: CIP-Pool 001, Martiusstr. 4 • When: Tuesdays, 0800 – 1000 • Introduction to probability theory, statistics with focus on instruments for RT distribution analysis • Part theory, part programming (in R) • Tailored to the students state of knowledge and speed • Follow-up course next semester is planned

  5. Literature • This course is loosely based on... • Trisha Van Zandt: Analysis of RT distributions • John Verzani: simpleR – Using R for introductory statistics

  6. RT Motivation for the course

  7. RT Why use response times (RT)? • measured easily and (in principle) with high precision • are ratio-scaled, thus a large amount of statistical/ mathematical tools can be applied

  8. RT Response times in research • RTs are of paramount importance for empirical investigations in biological, social and clinical psychology with over 29.000 abstracts in PsychInfo database

  9. RT But... • Although RTs have been used for over a century, still basic issues arise • NP H0 testing are routinely applied to RTs even though normality and independence are violated • analysis at the level of means most often too conservative, uninformative, concealing ...

  10. RT Recently ... • Publications with in-depth investigation of RT distributions were issued • Ulrich 2007, Ratcliff 2006, Maris 2003, Colonius 2001, ... • Why not earlier? • Mathematical theories are not very accessible for non-mathematicians • Implementation with current statistical software is generally not easy to use

  11. GNU R Project • R was created by Ross Ihaka and Robert Gentleman at the University of Auckland (NZ) • R has become a de facto standard among statisticians for the development of statistical software and is widely used for statistical software development and data analysis.

  12. Advantages of R • R is free - R is open-source and runs on UNIX, Windows and Mac • R has an excellent built-in help system • R has excellent graphing capabilities • R has a powerful, easy to learn syntax with many built-in statistical functions • R is highly extensible with user-written functions

  13. „Downsides“ of R • R is a computer programming language, so users must learn to appreciate syntax issues etc. • It has a limited graphical interface • There is no commercial support

  14. Useful links for R • Book of the course: • http://wiener.math.csi.cuny.edu/UsingR/index.html/ • http://mirrors.devlib.org/cran/doc/contrib/Verzani-SimpleR.pdf • Manuals: • http://cran.r-project.org/doc/manuals/R-intro.html • http://www.statmethods.net/index.html • http://www.cyclismo.org/tutorial/R/ • http://math.illinoisstate.edu/dhkim/Rstuff/Rtutor.html

  15. Links for packages • http://cran.r-project.org/web/views/ • http://cran.r-project.org/web/packages/index.html • http://crantastic.org/

  16. Course roadmap I Introduction to probability theory Random variables and their characterization Estimation Theory Model testing II III IV

  17. I Introduction toProbability theory

  18. I Interpretations of probability • Laplacian Notion • „events of interest“ / „all events“ • Frequentistic Notion • Throwing a dice 1000 times  „real“ probability • Subjective probabilities/ Bayesian approach • How likely would you estimate the occurence of e.g. being struck by a lightning? • Updating estimation after observing evidence

  19. I Randomness in mathematics • Probability theory • Axiomatic system of Kolmogorov; measure theory • Stochastic processes (e.g. Wiener process) • Mathematical statistics • Test and estimation theory; modelling

  20. I Randomness in the brain? • Neural level • Neurons are non-linear system and have intrinsic noise • Stimulus level • BU: Ambiguous sensory evidence may lead to conflict/ deliberation • Subject level • TD: expectations, intertrial and learing effects alter the per se deterministic decision loop • Measurement device • May have subpar precision or sampling rate

  21. I Mathematical Modelling „Reality“ Modelspace

  22. And now to

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