Orientation-specificity in kinesthetic spatial learning: The role of multiple orientations

In the current study we tested whether multiple orientations in kinesthetic learning affected how flexibly spatial information is stored and later used in making location judgments. Three groups learned simple routes by walking them while blindfolded, with (1) multiple orientations achieved through normal walking, (2) multiple orientations achieved through backward walking, or (3) a single orientation achieved through walking without turning (which required forward, backward, and sideways walking). When subjects had experienced multiple orientations while learning the routes, later directional judgments were equally accurate (and equally rapid) regardless of whether the judgments were aligned or were contra-aligned with the orientation of the routes as originally learned. In contrast, when routes were learned in a single orientation (without turning), subsequent judgments on contra-aligned trials were both less accurate and slower than judgments on aligned trials. Thus, multiple orientations are important to establish orientation-free, flexible use of spatial information in a kinesthetic learning environment. This contrasts with the pattern of results typically found in visual spatial learning and suggests that the factors that affect orientation specificity of spatial use may differ across spatial modality.     

Causal modeling with the TETRAD program

Drawing substantive conclusions from linear causal models that perform acceptably on statistical tests is unreasonable if it is not known how alternatives fare on these same tests. We describe a computer program, TETRAD, that helps to search rapidly for plausible alternatives to a given causal structure. The program is based on principles from statistics, graph theory, philosophy of science, and artificial intelligence. We describe these principles, discuss how TETRAD employs them, and argue that these principles make TETRAD an effective tool. Finally, we illustrate TETRAD's effectiveness by applying it to a multiple indicator model of Political and Industrial development. A pilot version of the TETRAD program is described in this paper. The current version is described in our forthcoming Discovering Causal Structure: Artificial Intelligence for Statistical Modeling.     

Clinical tests of memory in dementia, depression, and healthy aging

In Study 1, carefully screened elderly adults with primary degenerative dementia or major depression were compared to healthy aged control subjects on three tests of learning and memory: the Benton Visual Retention Test, Inglis Paired-Associate Learning Test, and the Fuld Object-Memory Evaluation (OME). The sharpest distinction in performance among the groups was observed on the OME, and discriminant equations based on this test correctly classified a high percentage (greater than or equal to 90%) of participants. Study 2 applied the classification rules derived in the first investigation to an unselected series of geropsychiatry inpatients referred for neuropsychological evaluation. There was agreement between memory test classification and general categories of clinical discharge diagnosis (organic vs. functional) for 21 of 25 patients, and with status at follow-up approximately 18 months later. Predictive value computations suggested that the OME is more accurate in confirming true dementia than in detecting dementia syndromes associated with functional disorders.     

Synthesized clustering: A method for amalgamating alternative clustering bases with differential weighting of variables

In the application of clustering methods to real world data sets, two problems frequently arise: (a) how can the various contributory variables in a specific battery be weighted so as to enhance some cluster structure that may be present, and (b) how can various alternative batteries be combined to produce a single clustering that best incorporates each contributory set. A new method is proposed (SYNCLUS, SYNthesizedCLUStering) for dealing with these two problems.We wish to thank Anne Freeny and Deborah Art for their computer assistance, and Ed Fowlkes for his helpful technical discussion. We would also like to acknowledge the insightful and helpful comments from the editor and reviewers.     

Mood and the mundane: relations between daily life events and self-reported mood

Daily mood ratings and corresponding diary entries were studied to determine relations between common events and two independent mood factors--Positive Affect (PA) and Negative Affect (NA)--in a sample of 18 young adults over a 3-month period. In an extension of findings from earlier interindividual studies, PA (enthusiastic, delighted vs. sluggish, drowsy) was found to be associated with a wide range of daily events, whereas fewer correlations were found between these events and NA (distressed, nervous, angry vs. calm, relaxed). The relation between high PA and reported social interactions (particularly physically active social events) was especially robust, and its effects were noted repeatedly; NA was unrelated to social activity. As hypothesized, high NA was associated with physical problems; contrary to expectations, low PA also tended to be correlated with health complaints. Overall, the results reaffirm the importance of assessing NA and PA independently and suggest that PA is an interesting and important dimension that deserves more research attention. Theoretical considerations and clinical implications are discussed.