Constructing a group distribution from individual distributions.

A group distribution is a synthesis of a set of individual distributions. To be adequate, a method for creating group distributions should not introduce characteristics that are not present in the individual distributions and preserve those that are present. A method occasionally used is quantile averaging (sometimes called vincentizations), applied generally to response time distributions. However, it is shown here using quantile-quantile plots on empirical response times that this method is inadequate. As shown by Thomas and Ross (1980, Journal of Mathematical Psychology), to solve this problem, quantile averaging can be generalised using an appropriate nonlinear transformation of the data. Here we argue that the correct transformation is the log transform of response times to which the base response time has been removed. Equivalently, the geometric mean of the quantiles can be used. We first propose 4 estimates of the base response times. We next examine empirical data in a same-different task, in a redundant-attribute target detection task and in a visual search task. The results show that this approach is appropriate to construct group distributions. It can be used to aggregate distributions over multiple participants, over multiple sessions of training for a given participant, or both. (PsycINFO Database Record

Decision-tree models of categorization response times, choice proportions, and typicality judgments.

The authors present 3 decision-tree models of categorization adapted from T. Trabasso, H. Rollins, and E. Shaughnessy (1971) and use them to provide a quantitative account of categorization response times, choice proportions, and typicality judgments at the individual-participant level. In Experiment 1, the decision-tree models were fit to reaction time and choice proportion data from a study reported by A. L. Cohen and R. M. Nosofsky (2003). In Experiment 2, participants were also asked to provide typicality ratings for each stimulus. A process-tracing method called the "4-questions game" (Y. Sayeki, 1969) was used in a posttest phase to identify a decision tree for each participant. In both experiments, the decision-tree models explained a very high proportion of variance in the data and compared favorably with 2 leading exemplar models.

Is absolute identification always relative? Comment on Stewart, Brown, and Chater (2005).

N. Stewart, G. D. A. Brown, and N. Chater's relative judgment model includes three core assumptions that enable it to predict accurately the vast majority of "classical" phenomena in absolute identification choices, but not the time taken to make them, including sequential effects, such as assimilation and contrast. These core assumptions, coupled with the parameter values used in the above-mentioned article, lead to the prediction that identification accuracy is low when a large stimulus on 1 trial is followed by a small stimulus on the next trial and vice versa. Data do not support this prediction. The authors identify a set of parameters that allow the model to better fit the data, but problems remain when the data are analyzed with a version of the discrimination measure (d') from signal detection theory. The fundamental problem is that the model fits data on average but at the expense of making incorrect predictions in detail.

Choice and response time processes in the identification and categorization of unidimensional stimuli.

Lacouture and Marley (1991, 1995, 2001) have successfully modeled the probabilities of correct responses and the mean correct response times (RTs) in unidimensional absolute identification tasks for various stimulus ranges and stimulus/response set sizes, for individual and group data. These fits include those to a set of phenomena often referred to as end-anchor effects. A revised model, with the independent accumulator decision process replaced by a leaky competing accumulator decision process, fits the probabilities of correct responses and the full distributions of RTs in unidimensional absolute identification. The revised model is also applied successfully to a particular class of unidimensional categorization tasks. We discuss possible extensions for handling sequential effects in unidimensional absolute identification, and other extensions of the given class of categorization tasks that are of potential empirical and theoretical importance as a supplement to the study of multidimensional absolute identification tasks.