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The Society for Mathematical Psychology promotes the advancement and communication of research in mathematical psychology and related disciplines. Mathematical psychology is broadly defined to include work of a theoretical character that uses mathematical methods, formal logic, or computer simulation. The official journals of the society are Journal of Mathematical Psychology and Computational Brain & Behavior.

Society News and Updates
Postdoc position in cognitive psychology / neuroscience at the University of Hamburg (Germany)

We are looking for an excellent and highly motivated post-doctoral scholar to join the newly funded General Psychology Lab (PI: Sebastian Gluth). This is a fixed-term contract. The initial fixed term is 3 years. The contract provides for a maximum extension of up to 3 years depending on the associate’s achievements during the first stage. Importantly, the postdoc may apply for temporary civil servant status which comes with many benefits (e.g., favorable tax, pension and health insurance schemes).

Responsibilities: Duties include research and teaching in the area of cognitive psychology and cognitive neuroscience (esp. neuroeconomics). Research associates may also pursue independent research and further academic qualifications as well as acquire teaching experience. These duties are intended to promote academic achievement.

The research foci of the General Psychology Lab in Hamburg comprise learning and decision making, cognitive modeling, eye tracking, and the use of neuroscientific methods (fMRI, EEG). The postdoc is expected to plan, develop and conduct research projects along their own research interests within the foci of the professorship. Application for funding to perform independent projects, possibly in collaboration with other universities or partners, will be supported.

Further research-related duties include documenting and publishing of scientific results, attending national and international conferences along with presenting own research output, as well as supporting the principal investigator with the training of PhD students. Besides independent research, the position involves 4 LVS (“semester hours”) of teaching. The postdoc is expected to hold courses (possibly virtual and in English) and to supervise Bachelor and Master theses. The teaching duties will be linked to the modules “General Psychology” (Bachelor of Science) and “Cognitive Neuroscience” (Master of Science).

Requirements: A university degree in a relevant subject plus doctorate. We expect the candidate to hold an excellent university degree in psychology (Master/Diploma) or related fields and an excellent and completed PhD, relevant research experience, and very good programming skills (e.g., in Matlab, R, Python). Experience in university teaching and thesis supervision is an asset. In addition, very good skills and knowledge in the following areas are desirable: in general psychology (esp. judgement and decision making, attention, learning and memory), in cognitive neuroscience (esp. neuroeconomics, reinforcement learning), in advanced statistical methods (esp. cognitive modeling), in neuroscientific methods (esp. EEG, fMRI, eye tracking). High proficiency in both verbal and written English is a requirement. Finally, candidates should be highly committed and willing to work independently.

Lab website:

For further information, please contact Prof. Dr. Sebastian Gluth. Applications should include a cover letter, a tabular curriculum vitae, and copies of degree certificate(s). Please send applications by October 31, 2020 to: This e-mail address is being protected from spambots. You need JavaScript enabled to view it

Postdoc position at Santa Fe Institute

SFI has a postdoc opening (2 to 3 years) for someone interested in the gain and loss of cognitive ability and expert levels of performance in model systems like Go and Chess, Rubik's Cube, Tetris, etc. The successful candidate will collaborate with David Krakauer (SFI), John Krakauer (Johns Hopkins, SFI), and Adrian Haith (Johns Hopkins). This position is funded by a James S. McDonnell Foundation grant on Complex Time: Adaptation, Aging, & the Arrow of Time.

Deadline for best consideration: November 1, 2020.


Detailed job ad:

Out now: Bayesian Statistics for Experimental Scientists

Bayesian Statistics for Experimental Scientists

A new book about Bayesian statistics is now available for purchase from MIT Press. The book is titled Bayesian Statistics for Experimental Scientists: A General Introduction Using Distribution-Free Methods. The book is a novel introduction to Bayesian statistics with a special emphasis on distribution-free or nonparametric procedures.

Although the book is designed for experimental scientists in general, there are a number of applications associated with either the estimation of parameters or the testing of models from mathematical psychology. There are nonstandard analyses and methods in the book that should be of interest to mathematical psychologists, yet the book is written with either an advanced undergraduate student or a graduate student in experimental science in mind. I have in fact used a draft version of the book to teach a required graduate course at Tufts University in advanced statistics for psychologists.

The book provides R programming instructions for implementing all the methods, and there are many exercises at the end of each chapter. The book is organized in two parts. Part I provides a general introduction to probability theory, the binomial model, multinomial models, and experimental comparisons with categorical variables. Part II deals with techniques for the analysis of rank-based data. Topics included in Part II are rank-based experimental tests, the Wilcoxon signed-rank procedure, the Mann-Whitney method, the Goodman-Kruskal statistic, the goodness-of-fit of a mathematical function to data, and the Kendall tau correlation. There are side-by-side comparisons made in all parts of the book between frequentist and Bayesian methods. The discussion in several chapters provides a critique of frequentist practice and demonstrates why the Bayesian approach is better suited to the goals of experimental scientists.


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