Category label and response location shifts in category learning

The category shift literature suggests that rule-based classification, an important form of explicit learning, is mediated by two separate learned associations: a stimulus-to-label association that associates stimuli and category labels, and a label-to-response association that associates category labels and responses. Three experiments investigate whether information–integration classification, an important form of implicit learning, is also mediated by two separate learned associations. Participants were trained on a rule-based or an information–integration categorization task and then the association between stimulus and category label, or between category label and response location was altered. For rule-based categories, and in line with previous research, breaking the association between stimulus and category label caused more interference than breaking the association between category label and response location. However, no differences in recovery rate emerged. For information–integration categories, breaking the association between stimulus and category label caused more interference and led to greater recovery than breaking the association between category label and response location. These results provide evidence that information–integration category learning is mediated by separate stimulus-to-label and label-to-response associations. Implications for the neurobiological basis of these two learned associations are discussed

Catalog of Radio Galaxies with z>0.3. I:Construction of the Sample

The procedure of the construction of a sample of distant ($z>0.3$) radio galaxies using NED, SDSS, and CATS databases for further application in statistical tests is described. The sample is assumed to be cleaned from objects with quasar properties. Primary statistical analysis of the list is performed and the regression dependence of the spectral index on redshift is found.Comment: 9 pages, 6 figures, 2 table

The implications of advances in research on motivation for cognitive models

There has been an upsurge of research in psychology on the interface between motivation and cognition. Much of this work has focused on elucidating the structure of the motivational system, although this work has also begun to examine the influence of motivation on preference, choice and learning. The growing body of data provides an opportunity for computationally minded researchers to extend existing cognitive models to incorporate insights about the nature of the motivational system. This paper reviews some recent research and draws out the implications of this work for computational cognitive science

Classification of exemplars with single and multiple feature manifestations: The effects of relevant dimension variation and category structure

Most classification research focuses on cases in which each abstract feature has the same surface manifestation whenever it is presented. Previous research finds that people have difficulty learning to classify when each abstract feature has multiple surface manifestations. These studies created multiple manifestations by varying aspects of the stimuli irrelevant to the abstract feature dimension. In this feature dimension. People given categories with the family resemblance category structure often used in psychology experiments had difficulty learning to classify when multiple manifestations were present, even though the variation was relevant. This effect was reversed when a family resemblance structure with nondiagnostic values was used. Category members differ along many dimensions. For example, cars differ in their size, engine power, styling, and numerous other attributes. Despite this variation, we have little trouble recognizing these items as cars and classifying them appropriately. Although we are sensitive to variation among members of a category, it has proven difficult to characterize the systematic effects of variabilit