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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.

Amazon Mechanical Turk (AMT) is an online crowdsourcing service where anonymous online workers complete web-based tasks for small sums of money. The service has attracted attention from experimental psychologists interested in gathering human subject data more efficiently. However, relative to traditional laboratory studies, many aspects of the testing environment are not under the experimenter's control. In this paper, we attempt to empirically evaluate the fidelity of the AMT system for use in cognitive behavioral experiments. These types of experiment differ from simple surveys in that they require multiple trials, sustained attention from participants, comprehension of complex instructions, and millisecond accuracy for response recording and stimulus presentation. We replicate a diverse body of tasks from experimental psychology including the Stroop, Switching, Flanker, Simon, Posner Cuing, attentional blink, subliminal priming, and category learning tasks using participants recruited using AMT. While most of replications were qualitatively successful and validated the approach of collecting data anonymously online using a web-browser, others revealed disparity between laboratory results and online results. A number of important lessons were encountered in the process of conducting these replications that should be of value to other researchers.

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.

1. Introduction Emmanuel M. Pothos and Andy J. Wills 2. The generalized context model: an exemplar model of classification Robert M. Nosofsky 3. Prototype models of categorization: basic formulation, predictions, and limitations John Paul Minda and J. David Smith 4. COVIS F. Gregory Ashby, Erick J. Paul and W. Todd Maddox 5. Semantics without categorization Timothy T. Rogers and James L. McClelland 6. Models of attentional learning John K. Kruschke 7. An elemental model of associative learning and memory Evan Livesey and Ian McLaren 8. Nonparametric Bayesian models of categorization Thomas L. Griffiths, Adam N. Sanborn, Kevin R. Canini, Daniel J. Navarro and Joshua B. Tenenbaum 9. The simplicity model of unsupervised categorization Emmanuel M. Pothos, Nick Chater and Peter Hines 10. Adaptive clustering models of categorization John V. McDonnell and Todd M. Gureckis 11. COBWEB models of categorization and probabilistic concept formation Wayne Iba and Pat Langley 12. The knowledge and resonance (KRES) model of category learning Harlan D. Harris and Bob Rehder 13. The contribution (and drawbacks) of models to the study of concepts Gregory L. Murphy 14. Formal models of categorization: insights from cognitive neuroscience Lukas Strnad, Stefano Anzellotti and Alfonso Caramazza 15. Comments on models and categorization theories: the razor's edge Douglas Medin.