Technical clarification to Silbert and Thomas (2013): “Decisional separability,model identification,and statistical inference in the general recognition theory framework”

We offer a minor technical correction to the published proof of part (ii) of the main theorem in Silbert and Thomas (Psychonomic Bulletin & Review, 20, 1–20, 2013) that somewhat limits the scope of the equivalence observed in that work. Specifically, in order for a mean shift integrality with decisional separability to be mimicked by a perceptually separable but nondecisionally separable configuration, one needs to assume stimulus invariance. This holds when all of the covariance matrices in the stimulus configuration are equal to each other. We note that part (i) of the theorem is unaffected by this modification; an empirical finding of perceptual separability and the failure of decisional separability can be mimicked by a perceptually nonseparable, decisionally separable configuration without restricting the covariance matrices to be equal. We also note that stimulus invariance is often assumed in simple designs (e.g., Macmillan & Ornstein in Journal of the Acoustical Society of America, 97, 1261–1285, 1998), due to the implausibility of different perceptual correlations being present within stimuli perched very closely in perceptual space.     

Does Symptom Linkage Density Predict Outcomes in Cognitive Therapy for Recurrent Depression?

Journal of Psychopathology and Behavioral Assessment - Acute-phase cognitive therapy (CT) is an efficacious treatment for major depressive disorder (MDD), but how CT helps patients is incompletely...     

African American Children’s Racial Discrimination Experiences and Mental Health

Journal of Child and Family Studies - The current study examined how African American children’s experiences of perceived personal racial discrimination and perceived vicarious racial...     

Individual differences in cognitive plasticity: an investigation of training curves in younger and older adults

To date, cognitive intervention research has provided mixed but nevertheless promising evidence with respect to the effects of cognitive training on untrained tasks (transfer). However, the mechanisms behind learning, training effects and their predictors are not fully understood. Moreover, individual differences, which may constitute an important factor impacting training outcome, are usually neglected. We suggest investigating individual training performance across training sessions in order to gain finer-grained knowledge of training gains, on the one hand, and assessing the potential impact of predictors such as age and fluid intelligence on learning rate, on the other hand. To this aim, we propose to model individual learning curves to examine the intra-individual change in training as well as inter-individual differences in intra-individual change. We recommend introducing a latent growth curve model (LGCM) analysis, a method frequently applied to learning data but rarely used in cognitive training research. Such advanced analyses of the training phase allow identifying factors to be respected when designing effective tailor-made training interventions. To illustrate the proposed approach, a LGCM analysis using data of a 10-day working memory training study in younger and older adults is reported.