Search

Parallel interactive processing (PIP) is a parallel distributed processing approach in which a representation of an object involves interaction of its components in a way that influences information processing of the components. Prior research is summarized in the areas of categorization and probability judgment. It was found that an explanation of the halo effect and its generalization as well as phenomena in other information processing situations could be explained by PIP. The explanation of the halo effect is largely in terms of reduction of variance in judgments of components of an object produced by a holistic impression of the object. There was a focus on processes common to various subject areas. [ABSTRACT FROM AUTHOR]

Parallel interactive processing (PIP) represents an approach in which specific context generates interactive relationships between general attributes. This article summarizes previous research that demonstrates how such relationships influence inference making [...]

We investigated the effect of learning one category structure on the learning of a related category structure. Photograph--name combinations, called identifiers, were associated with values of four demographic attributes. Two problems were related by analogous demographic attributes, common identifiers, or both to examine the impact of common identifier, related general characteristics, and the interaction of the two variables in mediating learning transfer from one category structure to another. Problems sharing the same identifier information prompted greater positive transfer than those not sharing the same identifier information. In contrast, analogous defining characteristics in the two problems did not facilitate transfer. We computed correlations between responses to first-problem stimuli and responses to analogous second-problem stimuli for each participant. The analogous characteristics produced a tendency to respond in the same way to corresponding stimuli in the two problems. The results support an alignment between category structures related by analogous defining characteristics, which is facilitated by specific identifier information shared by two category structures.

An experiment was performed to examine the role of category instances and category structure in the transfer of learning between categories. Two problems in learning of categories were presented using exemplars of categories that were individuals with specified demographic characteristics. One group received two problems in which the same individuals were used as exemplars in both problems. The categories in both problems partitioned the exemplars in the same way, such that learning of the first problem could be transferred to the second problem by learning the correspondence. A second group received two problems which did not overlap on specific exemplars, but the second problem categorization rule preserved the attribute-value structure of the first problem. A third group received two problems which overlapped completely on specific exemplars, but in which there was no correspondence between problems in the way the rule partitioned the set. Performance on the second problem showed different patterns for the three groups. Results suggest that intercategory relationships are mediated in part by some exemplar-based representation and in part by structural similarity.

In 1991 Nahinsky demonstrated that victories were more likely to follow losses than to follow victories in World Series competition, consistent with a 'bouncing back' effect. Zentall stated the result was a statistical artifact of the analysis and presented an analysis he claimed supported that assertion. This article shows that his conclusions are based upon a misconception about the assumptions underlying Nahinsky's analysis as well as faulty assumptions underlying his own. Other aspects of analyzing processes of this sort were also considered.

Examines issues concerning the assumption that a victory is more likely to follow a defeat than a victory for a given team. Analysis of the results of a study on World Series baseball play between 1903 and 1989.

The article presents an analysis of variance model specifically designed to analyse intraindividual Q sort patterns. The dimensions presented are used for illustrative purpose and certainly do not preclude the adaptation of the approach to other types of patterning. This set of analyses is but one example of how Q sort data may lead to analyses yielding information about intra-individual personality patterns. The interested personality researcher undoubtedly will think of many sets of assumptions built around different Q sort structures. To the extent that patterns derived in this manner are replicable within specified groups, this approach provides a worthwhile extension of normative measurement.

This study attempts to investigate the relationship between self-ideal discrepancies and adjustment in a specific environment. The Q-technique developed by W. Stephenson was utilized in this study. Two groups of Navy officers, one composed of officers choosing a Navy career, and the other of officers deciding against a Navy career sorted 100 statements, using the Q-sort. One sorting was to describe themselves, another to describe a typical career Navy officer, and another to describe an ideal career Navy officer. For each officer, correlations were computed between item values for each of the three pairings of descriptions. An analysis of variance of item values was also done to compare the groups.