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The neurocognitive processing of environmental sounds and linguistic stimuli shares common semantic resources and can lead to the activation of motor programs for the generation of the passively heard sound or speech. We investigated the extent to which the cognition of environmental sounds, like that of language, relies on symbolic mental representations independent of the acoustic input. In a hierarchical sorting task, we found that evaluation of nonliving sounds is consistently biased toward a focus on acoustical information. However, the evaluation of living sounds focuses spontaneously on sound-independent semantic information, but can rely on acoustical information after exposure to a context consisting of nonliving sounds. We interpret these results as support for a robust iconic processing strategy for nonliving sounds and a flexible symbolic processing strategy for living sounds.

Online data collection has begun to revolutionize the behavioral sciences. However, conducting carefully controlled behavioral experiments online introduces a number of new of technical and scientific challenges. The project described in this paper, psiTurk, is an open-source platform which helps researchers develop experiment designs which can be conducted over the Internet. The tool primarily interfaces with Amazon's Mechanical Turk, a popular crowd-sourcing labor market. This paper describes the basic architecture of the system and introduces new users to the overall goals. psiTurk aims to reduce the technical hurdles for researchers developing online experiments while improving the transparency and collaborative nature of the behavioral sciences.

Summary Numerous proposals have been put forward concerning the nature of human category representations, ranging from rules to exemplars to prototypes. However, it is unlikely that a single, fixed form of representation is sufficient to account for the flexibility of human categories. In this chapter, we describe an alternative to these fixed-representation accounts based on the principle of adaptive clustering. The specific model we consider, SUSTAIN, represents categories in terms of feature bundles called clusters which are adaptively recruited in response to task demands. In some cases, SUSTAIN acts like an exemplar model, storing each category instance as a separate memory trace, while in others it appears more like a prototype model, extracting only the central tendency of a number of items. In addition, selective attention in the model allows it to mimic many of the behaviours associated with rule-based systems. We review a variety of evidence in support of the clustering principle, including studies of the relationship between categorization and recognition memory, changes in unsupervised category learning abilities across development, and the influence of category learning on perceptual discrimination. In each case, we show how the nature of human category representations is best accounted for using an adaptive clustering scheme. SUSTAIN is just one example of a system that casts category learning in terms of adaptive clustering, and future directions for the approach are discussed.

What makes a category seem natural or intuitive? In this paper, an unsupervised categorization task was employed to examine observer agreement concerning the categorization of nine different stimulus sets. The stimulus sets were designed to capture different intuitions about classification structure. The main empirical index of category intuitiveness was the frequency of the preferred classification, for different stimulus sets. With 169 participants, and a within participants design, with some stimulus sets the most frequent classification was produced over 50 times and with others not more than two or three times. The main empirical finding was that cluster tightness was more important in determining category intuitiveness, than cluster separation. The results were considered in relation to the following models of unsupervised categorization: DIVA, the rational model, the simplicity model, SUSTAIN, an Unsupervised version of the Generalized Context Model (UGCM), and a simple geometric model based on similarity. DIVA, the geometric approach, SUSTAIN, and the UGCM provided good, though not perfect, fits. Overall, the present work highlights several theoretical and practical issues regarding unsupervised categorization and reveals weaknesses in some of the corresponding formal models.

As accelerators are developed to extend the frontiers of high energy physics, the complexity of their nature presents technological problems that need to be solved by extensive programs of research and development. The following reports on the results of one such program, the design and construction of the radio frequency accelerating system of the Argonne Zero Gradient Synchrotron. It describes the system requirements, the equipment that has been developed to fulfill these requirements, and the present performance of the system.