Judging compound social categories: Compound familiarity and compatibility as determinants of processing mode

Three experiments tested the hypothesis that judgments about a group formed by two paired categories would rely on stored instances of individual category members (i.e., exemplars) in some cases, but not in others. Specifically, judgments of a relatively unfamiliar compound category (e.g., male elementary schoolteachers) were expected to rely on exemplars, whereas alternative sources of information, particularly abstract stereotypes, would be available for making judgments of a more familiar category (e.g., female elementary schoolteachers). Experiments 1 and 2 demonstrated support for these hypotheses. Experiment 3 ruled out the possibility that the differences in judgment strategy between the familiar and unfamiliar compound categories arose from the relative incompatibility of the two constituent categories (e.g., males and elementary schoolteachers), rather than familiarity. Implications for stereotype development and change are discussed

Nested Sequential Monte Carlo Methods

We propose nested sequential Monte Carlo (NSMC), a methodology to sample from sequences of probability distributions, even where the random variables are high-dimensional. NSMC generalises the SMC framework by requiring only approximate, properly weighted, samples from the SMC proposal distribution, while still resulting in a correct SMC algorithm. Furthermore, NSMC can in itself be used to produce such properly weighted samples. Consequently, one NSMC sampler can be used to construct an efficient high-dimensional proposal distribution for another NSMC sampler, and this nesting of the algorithm can be done to an arbitrary degree. This allows us to consider complex and high-dimensional models using SMC. We show results that motivate the efficacy of our approach on several filtering problems with dimensions in the order of 100 to 1 000