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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.

This workshop will be held on the Medford campus of Tufts University, which is in the greater Boston area. The workshop will be a full-day workshop on Multinomial Processing Tree models conducted by William Batchelder, Richard Chechile and Xiangen Hu.  The workshop follows the annual meeting of the Society for Mathematical Psychology and proceeds the annual meeting of the Cognitive Science Society, which is also held in the Boston area. The purpose of this workshop is to provide students/researchers with a detailed, hands-on access to recent mathematical, empirical, computational, and statistical developments concerning Multinomial Processing Tree (MPT) models.  MPT models are an increasingly popular class of parametric probability models for categorical data generated in cognitive experiments. Models within this class have been applied in such areas as perception, categorization, decision making, social psychology, reasoning, and especially in many experimental paradigms in human memory, including recognition memory, free recall, source monitoring, process dissociation, and prospective memory. In fact, in a 2009 special issue of Zeitschrift für Psychologie, there is lead article by Erdfelder et al.that reviews over 100 papers developing versions of MPT models in 20 different research areas. MPT models are relatively simple and paradigm specific, and as a consequence many of the mathematical and statistical properties for this class of models have been developed.  Models in the MPT class are specified as latent decision trees, where branches correspond to hypothetical processing sequences that eventuate in a manifest categorical response. The main purpose of using a MPT model is to provide a way to indirectly measure latent cognitive processes, such as memory storage, memory retrieval, logical inference, attentional focus, metacognition, guessing biases, and perceptual integration, which otherwise are not directly accessible with any single dependent variable measure.

This workshop involves a full day of instruction by three researchers with wide experience in the history and development of MPT models in psychology.  The course is designed to be informative and practical for researchers from diverse backgrounds.  Minimal prerequisites would include a basic course in calculus and some background in probability and statistics.  Both graduate students in experimental psychology as well as experienced modelers who are inexperienced with MPT models should learn valuable information from this workshop.  The instruction includes discussion of task and model design, model specification, model identifiability, parameter validation, both Bayesian and classical methods for statistical estimation, hierarchical modeling, and model selection. Among the statistical estimation methods covered will be maximum likelihood estimation, random effects models for item and subjects, and population parameter mapping. Importantly, software for the different approaches will be provided and explained

One can register for this workshop through the website for the annual meeting of the Society for Mathematical Psychology (SMP), although one can register for just the workshop and not the conference. The website for SMP conference and the workshop is