Two ways of learning associations

How people learn chunks or associations between adjacent items in sequences was modelled. Two previously successful models of how people learn artificial grammars were contrasted: the CCN, a network version of the competitive chunker of Servan‐Schreiber and Anderson [J. Exp. Psychol.: Learn. Mem. Cogn. 16 (1990) 592], which produces local and compositionally‐structured chunk representations acquired incrementally; and the simple recurrent network (SRN) of Elman [Cogn. Sci. 14 (1990) 179], which acquires distributed representations through error correction. The models' susceptibility to two types of interference was determined: prediction conflicts, in which a given letter can predict two other letters that appear next with an unequal frequency; and retroactive interference, in which the prediction made by a letter changes in the second half of training. The predictions of the models were determined by exploring parameter space and seeing howdensely different regions of the space of possible experimental outcomes were populated by model outcomes. For both types of interference, human data fell squarely in regions characteristic of CCN performance but not characteristic of SRN performance.     

Factor structure of the Treatment Evaluation Inventory: Implications for measurement of treatment acceptability

The Treatment Evaluation Inventory (TEI), a frequently used measure of treatment acceptability, was used by 164 undergraduates to rate the acceptability of each of the following treatments: differential reinforcement of other behavior, exclusionary time-out, overcorrection, medical restraint, contingent electric shock, and physical restraint. TEI ratings of each treatment type were grouped separately, variance-covariance matrices were formed and compared, and data were subjected to factor analysis. The results indicated that the factor structure of the TEI varied with the treatment it was used to evaluate. Item analysis of the TEI indicated a high degree of internal consistency, although item-total correlations varied between rated treatments. The findings suggest that although the TEI is a reliable instrument, sensitive assessment of the treatment acceptability construct probably requires multidimensional measurement.     

Charting the process of change: A primer on survival analysis

Survival analysis is a powerful and useful technique for understanding qualitative change. This article provides a practical, nontechnical introduction to the use of survival analysis for social scientists. Important issues in using survival analysis are discussed, including research design, data preparation and management, and data analysis. Attendance data from a self-helf organization are used to illustrate common survival analysis tasks such as describing the overall survival and hazard functions, examining covariate effects, and modeling the form of the hazard function over time. An appendix that discusses the strengths and weaknesses of current survival analysis computer programs is included. Editor's note: Edward Seidman served as action editor for this article while serving as Associate Editor for Methodology.I thank William Davidson, Susan Englund, Bruce Rapkin, Kurt Ribisl, and three anonymous reviewers for their helpful comments on earlier drafts. The example data presented here were collected with the support of an NIMH grant (MH37390) awarded to Julian Rappaport and Ed Seidman.     

Setting phenotypes in a mutual help organization: Expanding behavior setting theory

Expands Barker's theory of behavior settings by proposing an additional method of classifying settings based on their functional/behavioral aspects--the setting phenotype. Although behavior setting theory has been widely hailed as a revolutionary contribution to behavioral science, it has had limited impact on general psychology. This may be due in part to a reliance on a purely structural method of classifying behavior settings--the setting genotype. Behavioral data were collected from 510 meetings of 13 self-help groups from a mutual help organization for persons with problems in living. A cluster analysis was performed to uncover meaningful behavioral patterns among the groups. Four phenotypes were identified: personal, impersonal, small talk, and advising. Mutual help group phenotype was found to be related to a set of setting characteristics as well as to overall rated change of group members. The results are discussed in light of the significance of the phenotype construct for making behavior setting theory more relevant for social scientists.     

The priming of face recognition after metric transformations

Four experiments were performed to test whether the perceptual priming of face recognition would show invariance to changes in size, position, reflectional orientation (mirror reversal), and picture-plane rotation. In all experiments, subjects recognized faces in two blocks of trials; in the second block, some of the faces were identical to those in the first, and others had undergone metric transformations. The results show that subjects were equally fast to recognize faces whether or not the faces had changed in size, position, or reflectional orientation between the first and second presentations of the faces. In contrast, subjects were slower to recognize both faces and objects when they were planar-rotated between the first and second presentations. The results suggest that the same metric invariances are shown by both face recognition and basic-level object recognition.     

Getting the Big Picture in Community Science: Methods That Capture Context

Community science has a rich tradition of using theories and research designs that are consistent with its core value of contextualism. However, a survey of empirical articles published in the American Journal of Community Psychology shows that community scientists utilize a narrow range of statistical tools that are not well suited to assess contextual data. Multilevel modeling, geographic information systems (GIS), social network analysis, and cluster analysis are recommended as useful tools to address contextual questions in community science. An argument for increased methodological consilience is presented, where community scientists are encouraged to adopt statistical methodology that is capable of modeling a greater proportion of the data than is typical with traditional methods.