Risks of drawing inferences about cognitive processes from model fits to individual versus average performance.

@article{EstesMaddox, abstract = {With the goal of drawing inferences about underlying processes from fits of theoretical models to cognitive data, we examined the tradeoff of risks of depending on model fits to individual performance versus risks of depending on fits to averaged data with respect to estimation of values of a model's parameters. Comparisons based on several models applied to experiments on recognition and categorization and to artificial, computer-generated data showed that results of using the two types of model fitting are strongly determined by two factors: model complexity and number of subjects. Reasonably accurate information about true parameter values was found only for model fits to individual performance and then only for some of the parameters of a complex model. Suggested guidelines are given for circumventing a variety of obstacles to successful recovery of useful estimates of a model's parameters from applications to cognitive data.}, address = {Department of Psychology, Indiana University, Bloomington, Indiana 47405, USA. wkestes@indiana.edu}, author = {Estes, W. K. and Maddox, W. T. }, citeulike-article-id = {882849}, issn = {1069-9384}, journal = {Psychonomic Bulleting and Review}, keywords = {data\_fitting, group\_response\_time}, month = {June}, number = {3}, pages = {403--408}, posted-at = {2006-10-03 21:31:09}, priority = {5}, title = {Risks of drawing inferences about cognitive processes from model fits to individual versus average performance.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/16235625}, volume = {12}, year = {2005} }

TY - JOUR ID - EstesMaddox L3 - citeulike-article-id:882849 TI - Risks of drawing inferences about cognitive processes from model fits to individual versus average performance. JF - Psychonomic Bulleting and Review VL - 12 IS - 3 SP - 403 EP - 408 AD - Department of Psychology, Indiana University, Bloomington, Indiana 47405, USA. wkestes@indiana.edu SN - 1069-9384 N2 - With the goal of drawing inferences about underlying processes from fits of theoretical models to cognitive data, we examined the tradeoff of risks of depending on model fits to individual performance versus risks of depending on fits to averaged data with respect to estimation of values of a model's parameters. Comparisons based on several models applied to experiments on recognition and categorization and to artificial, computer-generated data showed that results of using the two types of model fitting are strongly determined by two factors: model complexity and number of subjects. Reasonably accurate information about true parameter values was found only for model fits to individual performance and then only for some of the parameters of a complex model. Suggested guidelines are given for circumventing a variety of obstacles to successful recovery of useful estimates of a model's parameters from applications to cognitive data. KW - data_fitting KW - group_response_time AU - Estes, WK AU - Maddox, WT PY - 2005/06// UR - http://view.ncbi.nlm.nih.gov/pubmed/16235625 ER -