a b s t r a c t a r t i c l e i n f oWord processing studies increasingly make use of regression analyses based on large numbers of stimuli (the socalled megastudy approach) rather than experimental designs based on small factorial designs. This requires the availability of word features for many words. Following similar studies in English, we present and validate ratings of age of acquisition and concreteness for 30,000 Dutch words. These include nearly all lemmas language researchers are likely to be interested in. The ratings are freely available for research purposes.

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A robust finding in category-based induction tasks is for positive observations to raise the willingness to generalize to other categories while negative observations lower the willingness to generalize. This pattern is referred to as monotonic generalization. Across three experiments we find systematic non-monotonicity effects, in which negative observations raise the willingness to generalize. Experiments 1 and 2 show that this effect emerges in hierarchically structured domains when a negative observation from a different category is added to a positive observation. They also demonstrate that this is related to a specific kind of shift in the reasoner's hypothesis space. Experiment 3 shows that the effect depends on the assumptions that the reasoner makes about how inductive arguments are constructed. Non-monotonic reasoning occurs when people believe the facts were put together by a helpful communicator, but monotonicity is restored when they believe the observations were sampled randomly from the environment.

The gold standard among proximity data collection methods for multidimensional scaling is the (dis)similarity rating of pairwise presented stimuli. A drawback of the pairwise method is its lengthy duration, which may cause participants to change their strategy over time, become fatigued, or disengage altogether. Hout, Goldinger, and Ferguson (2013) recently made a case for the Spatial Arrangement Method (SpAM) as an alternative to the pairwise method, arguing that it is faster and more engaging. SpAM invites participants to directly arrange stimuli on a computer screen such that the interstimuli distances are proportional to psychological proximity. Based on a reanalysis of the Hout et al. (2013), data we identify three caveats for SpAM. An investigation of the distributional characteristics of the SpAM proximity data reveals that the spatial nature of SpAM imposes structure on the data, invoking a bias against featural representations. Individual-differences scaling of the SpAM proximity data reveals that the two-dimensional nature of SpAM allows individuals to only communicate two dimensions of variation among stimuli properly, invoking a bias against high-dimensional scaling representations. Monte Carlo simulations indicate that in order to obtain reliable estimates of the group average, SpAM requires more individuals to be tested. We conclude with an overview of considerations that can inform the choice between SpAM and the pairwise method and offer suggestions on how to overcome their respective limitations.

Are natural language categories represented by instances of the category or by a summary representation? We used an exemplar model and a prototype model, both derived within the framework of the generalized context model (Nosofsky, 1984, 1986), to predict typicality ratings for 12 superordinate natural language concepts. The models were fitted to typicality ratings averaged across participants and to the typicality judgments of individual participants. Both analyses yielded results in favor of the exemplar model. These results suggest that higher-level natural language concepts are represented by their subordinate members, rather than by a summary representation.