Memory updating practice across 100 days in the COGITO study

We investigated working memory updating performance in younger and older adults before, during, and after 100-day practice. Performance to presentation time (PT) relation was fitted to a negatively accelerated logistic function. Relative to younger adults, older adults showed lower asymptotes at pretest and posttest, and shallower slopes at pretest. Older adults practicing the task with fast PT gained less than older adults practicing the task with slow PT, probably reflecting the persistent use of a selective strategy throughout the 100-day practice period in the fast PT group. These results have implications for designing and evaluating age-comparative working memory training programs.     

Age Differences in Day-To-Day Speed-Accuracy Tradeoffs: Results from the COGITO Study

We examined adult age differences in day-to-day adjustments in speed-accuracy tradeoffs (SAT) on a figural comparison task. Data came from the COGITO study, with over 100 younger and 100 older adults, assessed for over 100 days. Participants were given explicit feedback about their completion time and accuracy each day after task completion. We applied a multivariate vector auto-regressive model of order 1 to the daily mean reaction time (RT) and daily accuracy scores together, within each age group. We expected that participants adjusted their SAT if the two cross-regressive parameters from RT (or accuracy) on day t-1 of accuracy (or RT) on day t were sizable and negative. We found that: (a) the temporal dependencies of both accuracy and RT were quite strong in both age groups; (b) younger adults showed an effect of their accuracy on day t-1 on their RT on day t, a pattern that was in accordance with adjustments of their SAT; (c) older adults did not appear to adjust their SAT; (d) these effects were partly associated with reliable individual differences within each age group. We discuss possible explanations for older adults’ reluctance to recalibrate speed and accuracy on a day-to-day basis.     

Opportunities and Issues in Modeling Intensive Longitudinal Data: Learning from the COGITO Project

Abstract

Technological developments increasingly permit the collection of longitudinal data sets in which the data structure contains a large number of participants N and a large number of measurement occasions T. Promising new dynamical systems approaches to the analysis of large N, large T data sets have been proposed that utilize both between-subjects and within-subjects information. The COGITO project, begun over a decade ago, is an early large N?=?204, large T?=?100 study that collected high quality cognitive and psychosocial data. In this introduction, I describe the COGITO project and conceptual and statistical issues that arise in the analysis of large N, large T data sets. I provide a brief overview of the five papers in the special section which include conceptual pieces, a didactic presentation of a dynamic structural equation approach, and papers reporting new statistical analyses of the COGITO data set to answer substantive questions. Although many challenges remain, these new approaches offer the promise of improving scientific inquiry in the behavioral sciences.     

At the Frontiers of Modeling Intensive Longitudinal Data: Dynamic Structural Equation Models for the Affective Measurements from the COGITO Study

With the growing popularity of intensive longitudinal research, the modeling techniques and software options for such data are also expanding rapidly. Here we use dynamic multilevel modeling, as it is incorporated in the new dynamic structural equation modeling (DSEM) toolbox in Mplus, to analyze the affective data from the COGITO study. These data consist of two samples of over 100 individuals each who were measured for about 100 days. We use composite scores of positive and negative affect and apply a multilevel vector autoregressive model to allow for individual differences in means, autoregressions, and cross-lagged effects. Then we extend the model to include random residual variances and covariance, and finally we investigate whether prior depression affects later depression scores through the random effects of the daily diary measures. We end with discussing several urgent—but mostly unresolved—issues in the area of dynamic multilevel modeling.     

REDUCTIONISM AND HOLISM,CHANCE AND SELECTION,MECHANISM AND MIND

Abstract. Despite its rich and deepening panoply of empirical support, evolutionary theory continues to generate widespread concern. Some of this concern can be attributed to misunderstandings of the original concept, some to unfamiliarity with its current trajectories, and some to strongly held fears that it strips the human of cherished attributes. In this essay I seek to deconstruct such misunderstandings, lift up current concepts of what evolution entails, and address some of the existential issues it generates.     

PSYCHOANALYSIS AND INFERTILITY MYTHS AND REALITIES

Psychoanalysis, long interested in infertility, and a valuable treatment for men and women suffering with this affliction, has also helped to create and support a myth of psychogenic infertility. Multiple causes of infertility exist across the physiological - psychological spectrum. There is no simple psychodynamic causality. Advances in assisted reproductive technologies provide treatments that create emotional stress and outpace psychological preparedness of patients and analysts. This paper is based on the experience of a unique study group in Boston. An analytic case illustrates some of the ways analysis can be a treatment of choice for people using assisted reproduction. In fact, analysis offers a unique opportunity to elaborate fully the complex realities and dilemmas faced by people and their therapists throughout the infertility experience. More generally, this study of the concept of psychogenic infertility explores a valuable role for psychoanalysis in the treatment of medical conditions.