Strategies in probabilistic categorization: results from a new way of analyzing performance

The "Weather Prediction" task is a widely used task for investigating probabilistic category learning, in which various cues are probabilistically (but not perfectly) predictive of class membership. This means that a given combination of cues sometimes belongs to one class and sometimes to another. Prior studies showed that subjects can improve their performance with training, and that there is considerable individual variation in the strategies subjects use to approach this task. Here, we discuss a recently introduced analysis of probabilistic categorization, which attempts to identify the strategy followed by a participant. Monte Carlo simulations show that the analysis can, indeed, reliably identify such a strategy if it is used, and can identify switches from one strategy to another. Analysis of data from normal young adults shows that the fitted strategy can predict subsequent responses. Moreover, learning is shown to be highly nonlinear in probabilistic categorization. Analysis of performance of patients with dense memory impairments due to hippocampal damage shows that although these patients can change strategies, they are as likely to fall back to an inferior strategy as to move to more optimal ones.     

Does group efficacy increase group identification? Resolving their paradoxical relationship

Although group identification and group efficacy are both important predictors of collective action against collective disadvantage, there is mixed evidence for their (causal) relationship. Meta-analytic and correlational evidence suggests an overall positive relationship that has been interpreted as consistent with the idea that group identification leads to group efficacy. However, experimental evidence has not supported this causal relationship. To resolve this paradox, we show in an experiment that it is group efficacy that leads to increased group identification because group efficacy puts individuals' identity into action. We discuss the theoretical and practical implications of these findings.     

Sequential sampling designs for the two-parameter item response theory model

In optimal design research, designs are optimized with respect to some statistical criterion under a certain model for the data. The ideas from optimal design research have spread into various fields of research, and recently have been adopted in test theory and applied to item response theory (IRT) models. In this paper a generalized variance criterion is used for sequential sampling in the two-parameter IRT model. Some general principles are offered to enable a researcher to select the best sampling design for the efficient estimation of item parameters.