Conditional Associative Learning of Spatial and Object Information in Children with Attention Deficit/Hyperactivity Disorder

The present study assessed frontostriatal mediated memory functions in children with ADHD (N = 12) and healthy control participants (N = 12) using two tests of conditional associative learning (i.e., object and spatial) that shared similar stimulus-response association structures but that differed in terms of the demands placed upon strategic processes. Children with ADHD displayed normal performance on the object learning task but were impaired on the spatial learning task that placed greater demands on internally derived strategic processes. Secondary analyses further indicated that this strategic processing impairment cannot be attributed specifically to perseverative or working memory errors but rather appears to be related to a more general inability to maintain a high degree of consistency in responding across trials. Although the results of this study must be interpreted in light of the small sample sizes, they suggest that ADHD does not produce a basic deficit in acquiring stimulus-response associations previously shown to be associated with basal ganglia dysfunction. Rather, these findings suggest that the impaired conditional associative learning performance of children with ADHD is attributable to deficits in strategic processes previously been found to be dependent upon the integrity of the prefrontal cortex.     

Abstract: A Meta-Analysis of the Autocorrelation in Single Case Designs

Single case design (SCD) experiments in the behavioral sciences utilize just one participant from whom data is collected over time. This design permits causal inferences to be made regarding various intervention effects, often in clinical or educational settings, and is especially valuable when between-participant designs are not feasible or when interest lies in the effects of an individualized treatment. Regression techniques are the most common quantitative practice for analyzing time series data and provide parameter estimates for both treatment and trend effects. However, the presence of serially correlated residuals, known as autocorrelation, can severely bias inferences made regarding these parameter estimates. Despite the severity of the issue, few researchers test or correct for the autocorrelation in their analyses.

Shadish and Sullivan (in press) recently conducted a meta-analysis of over 100 studies in order to assess the prevalence of the autocorrelation in the SCD literature. Although they found that the meta-analytic weighted average of the autocorrelation was close to zero, the distribution of autocorrelations was found to be highly heterogeneous. Using the same set of SCDs, the current study investigates various factors that may be related to the variation in autocorrelation estimates (e.g., study and outcome characteristics). Multiple moderator variables were coded for each study and then used in a metaregression in order to estimate the impact these predictor variables have on the autocorrelation.

This current study investigates the autocorrelation using a multilevel meta-analytic framework. Although meta-analyses involve nested data structures (e.g., effect sizes nested within studies nested within journals), there are few instances of meta-analysts utilizing multilevel frameworks with more than two levels. This is likely attributable to the fact that very few software packages allow for meta-analyses to be conducted with more than two levels and those that do allow this provide sparse documentation on how to implement these models. The proposed presentation discusses methods for carrying out a multilevel meta-analysis. The presentation also discusses the findings from the metaregression on the autocorrelation and the implications these findings have on SCDs.     

Some Esoteric Aspects of SEM That Its Practitioners Should Want to Know

The topic of this article is the interpretation of structural equation modeling (SEM) solutions. Its purpose is to augment structural modeling's metatheoretic resources while enhancing awareness of how problematic is the causal significance of SEM-parameter solutions. Part I focuses on the nonuniqueness and consequent dubious interpretability of loop pathweights in nonrecursive SEMs: Solutions for a nonrecursive SEM's open parameters are strongly indeterminate absent constraints that in most applications would be arbitrary. The generic indeterminacy is well known, but how severely this thwarts identification of a linear dynamic system's structural coefficients from synchronic (one-panel) data still seems inadequately appreciated. Part II introduces an efficient formalism for recognizing an important facet of lawful regularity—locus displacements—that is generally suppressed by the equations in which mathematical models are standardly written. (Preoccupation with SEM's matrix algebra and fit appraisal has masked the cognitive poverty with which SEM applications are prevailingly conceived.) Introduced here are some unfamiliar concepts that enable us to think about system structure more deeply than matrix equations can express. The Appendix attempts to incite some debate on how we can best conceptualize and exploit different levels (molar vs. molecular) of subject conception while appreciating that not all becauses are productive causes. It provides an overview of explanatory determinations comprising more than just production causality.     

On Two Main Themes in Gutting's What Philosophers Know

This paper addresses each of two of Gutting's three main contentions: that like anyone else, philosophers are entitled to begin with what they find obvious and that philosophy has produced a distinctive body of knowledge. I emphatically agree with the first contention and expand on it, defending a stronger claim. The second contention I dispute, in spirit if not in letter, on each of several grounds.     

Pseudocontingencies derived from categorically organized memory representations

Pseudocontingencies (PCs) allow for inferences about the contingency between two variables X and Y when the conditions for genuine contingency assessment are not met. Even when joint observations X _i and Y _i about the same reference objects i are not available or are detached in time or space, the correlation r(X _i ,Y _i ) is readily inferred from base rates. Inferred correlations are positive (negative) if X and Y base rates are skewed in the same (different) directions. Such PC inferences afford useful proxies for actually existing contingencies. While previous studies have focused on PCs due to environmental base rates, the present research highlights memory organization as a natural source of PC effects. When information about two attributes X and Y is represented in a hierarchically organized categorical memory code, as category-wise base rates p(X) and p(Y), the reconstruction of item-level information from category base rates will naturally produce PC effects. Three experiments support this contention. When the yes base rates of two respondents in four questionnaire subscales (categories) were correlated, recalled and predicted item-level responses were correlated in the same direction, even when the original responses to specific items within categories were correlated in the opposite direction.