The current study compared 3 models of recognition memory in their ability to generalize across yes/no and 2-alternative forced-choice (2AFC) testing. The unequal-variance signal-detection model assumes a continuous memory strength process. The dual-process signal-detection model adds a thresholdlike recollection process to a continuous familiarity process. The mixture signal-detection model assumes a continuous memory strength process, but the old item distribution consists of a mixture of 2 distributions with different means. Prior efforts comparing the ability of the models to characterize data from both test formats did not consider the role of parameter reliability, which can be critical when comparing models that differ in flexibility. Parametric bootstrap simulations revealed that parameter regressions based on separate fits of each test type only served to identify the least flexible model. However, simultaneous fits of receiver-operating characteristic data from both test types with goodness-of-fit adjusted with Akaike’s information criterion (AIC) successfully recovered the true model that generated the data. With AIC and simultaneous fits to real data, the unequal-variance signal-detection model was found to provide the best account across yes/no and 2AFC testing.

Provenance of correlations in psychological data There are few truisms in the field of psychology, but one of them is surely that measurement error is found in all experiments. Data are inevitably produced that do not perfectly reflect the logic imposed by the experimental design. To the extent that a psychological experiment succeeds in measuring something or in making some sort of distinction, the data will partially reflect the design, and this leads to a way of thinking about data that is found throughout all the experimental sciences: data � signal � noise. This innocent equation almost always contains an implicit but critical assumption: that the noise may be regarded as independent samples from some distribution— typically taken to be the Gaussian distribution. In this way, the residual error is conceived of as a featureless background of white noise in which the interesting part, the treatment means, are more or less buried. Often this conception of data is justified. Whenever there is random assignment to cells and each participant contributes a single datum, errors may be expected to be uncorrelated. However, in all of sensory psychophysics and most of cognitive psychology, single individuals respond to entire blocks of trials in a given experimental session. Here, the residual error will develop correlations by virtue of the circumstance that the response history was laid down by a nervous system that has memory. In many situations, these correlations are little more than a Preparation of this article was supported by NIMH Grants R01-

Abstract. Rituals shape many aspects of our lives, and they are no less common in scientific research than elsewhere. One that figures prominently in hypothesis testing is the null ritual, the pitting of hypotheses against chance. Although known to be problematic, this practice is still widely used. One way to resist the lure of the null ritual is to increase the precision of theories by casting them as formal models. These can be tested against each other, instead of against chance, which in turn enables a researcher to decide between competing theories based on quantitative measures. This article gives an overview of the advantages of modeling, describes research that is based on it, outlines the difficulties associated with model testing, and summarizes some of the solutions for dealing with these difficulties. Pointers to resources for teaching modeling in university classes are provided.

In cognitive science, empirical data collected from participants are the arbiters in model selection. Model discrimination thus depends on designing maximally informative experiments. It has been shown that adaptive design optimization (ADO) allows one to discriminate models as efficiently as possible in simulation experiments. In this paper we use ADO in a series of experiments with people to discriminate the Power, Exponential, and Hyperbolic models of memory retention, which has been a long-standing problem in cognitive science, providing an ideal setting in which to test the application of ADO for addressing questions about human cognition. Using an optimality criterion based on mutual information, ADO is able to find designs that are maximally likely to increase our certainty about the true model upon observation of the experiment outcomes. Results demonstrate the usefulness of ADO and also reveal some challenges in its implementation. 1

Models of a psychological process can be difficult to discriminate experimentally because it is not easy to determine the values of the critical design variables (e.g., presentation schedule, stimulus structure) that will be most informative in differentiating them. Recent developments in sampling-based search methods in statistics make it possible to determine these values, and thereby identify an optimal experimental design. After describing the method, it is demonstrated in two content areas in cognitive psychology in which models are highly competitive: retention (i.e., forgetting) and categorization. The optimal design is compared with the quality of designs used in the literature. The findings demonstrate that use of the method has the potential to increase the informativeness of the experimental method. 2 1