Empirical Bayes Derivative Estimates

Abstract

A dynamic system is a set of interacting elements characterized by changes occurring over time. The estimation of derivatives is a mainstay for exploring dynamics of constructs, particularly when the dynamics are complicated or unknown. The presence of measurement error in many social science constructs frequently results in poor estimates of derivatives, as even modest proportions of measurement error can compound when estimating derivatives. Given the overlap in the specification of latent differential equation models and latent growth curve models, and the equivalence of latent growth curve models and mixed models under some conditions, derivatives could be estimated from estimates of random effects. This article proposes a new method for estimating derivatives based on calculating the Empirical Bayes estimates of derivatives from a mixed model. Two simulations compare four derivative estimation methods: Generalized Local Linear Approximation, Generalized Orthogonal Derivative Estimates, Functional Data Analysis, and the proposed Empirical Bayes Derivative Estimates. The simulations consider two data collection scenarios: short time series (≤10 observations) from many individuals or occasions, and long individual time series (25–500 observations). A substantive example visualizing the dynamics of intraindividual positive affect time series is also presented.     

Towards modern post-coma care based on neuroscientific evidence

BackgroundUnderstanding the mechanisms underlying human consciousness is pivotal to improve the prognostication and treatment of severely brain-injured patients. Consciousness remains an elusive concept and the identification of its neural correlates is an active subject of research, however recent neuroscientific advances have allowed scientists to better characterize disorders of consciousness. These breakthroughs question the historical nomenclature and our current management of post-comatose patients.MethodThis review examines the contribution of consciousness neurosciences to the current clinical management of severe brain injury. It investigates the major impact of consciousness disorders on healthcare systems, the scientific frameworks employed to identify their neural correlates and how evidence-based data from neuroimaging research have reshaped the landscape of post-coma care in recent years.ResultsOur increased ability to detect behavioral and neurophysiological signatures of consciousness has led to significant changes in taxonomy and clinical practice. We advocate for a multimodal framework for the management of severely brain-injured patients based on precision medicine and evidence-based decisions, integrating epidemiology, health economics and neuroethics.ConclusionsMajor progress in brain imaging and clinical assessment have opened the door to a new era of post-coma care based on standardized neuroscientific evidence. We highlight its implications in clinical applications and call for improved collaborations between researchers and clinicians to better translate findings to the bedside.     

Durability and generalization of attribution-based feedback following failure: Effects on expectations and behavioral persistence

ObjectiveThis experiment investigated, following perceived failure, the immediate, long-term (i.e., durability), and cross-situational (i.e., generalization) effects of attribution-based feedback on expectations and behavioral persistence.DesignWe used a 3 × 2 (Group × Time) experimental design over seven weeks with attributions, expectations of success, and persistence as dependent measures.Method49 novice participants were randomly assigned to one of three treatment (attributional feedback) groups: (a) functional (i.e., controllable and unstable); (b) dysfunctional (i.e., uncontrollable and stable); or (c) no feedback. Testing involved three sessions, in which participants completed a total of five trials across two performance tasks (golf-putting and dart-throwing). In order to track whether the attributional manipulation conducted within the context of the golf-putting task in Session 2 would generalize to a new situation, participants performed a dart-throwing task in Session 3, and their scores were compared with those recorded at baseline (in Session 1).ResultsAnalysis of pre- and post-intervention measures of attributions, expectations, and persistence revealed that the functional attributional feedback led to more personally controllable attributions following failure in a golf-putting task, together with increases in success expectations and persistence. In contrast, dysfunctional attributional feedback led to more personally uncontrollable and stable attributions following failure, together with lower success expectations and reduced persistence. These effects extended beyond the intervention period, were present up to four weeks post intervention, and were maintained even when participants performed a different (i.e., dart-throwing) task.ConclusionsThe findings demonstrate that attributional feedback effects are durable over time and generalize across situations.     

Unrealistic representations of “the self”: A cognitive neuroscience assessment of anosognosia for memory deficit

Three cognitive components may play a crucial role in both memory awareness and in anosognosia for memory deficit (AMD): (1) a personal data base (PDB), i.e., a memory store that contains “semantic” representations about the self, (2) monitoring processes (MPs) and (3) an explicit evaluation system (EES), or comparator, that assesses and binds the representations stored in the PDB with information obtained from the environment.We compared both the behavior and the functional connectivity (as assessed by resting-state fMRI) of AMD patients with aware patients and healthy controls. We found that AMD is associated with an impoverished PDB, while MPs are necessary to successfully update the PDB.AMD was associated with reduced functional connectivity within both the default-mode network and in a network that includes the left lateral temporal cortex, the hippocampus and the insula. The reduced connectivity between the hippocampus and the insular cortex was correlated with AMD severity.     

Artificial syntactic violations activate Broca’s region

In the present study, using event-related functional magnetic resonance imaging, we investigated a group of participants on a grammaticality classification task after they had been exposed to well-formed consonant strings generated from an artificial regular grammar. We used an implicit acquisition paradigm in which the participants were exposed to positive examples. The objective of this study was to investigate whether brain regions related to language processing overlap with the brain regions activated by the grammaticality classification task used in the present study. Recent meta-analyses of functional neuroimaging studies indicate that syntactic processing is related to the left inferior frontal gyrus (Brodmann’s areas 44 and 45) or Broca’s region. In the present study, we observed that artificial grammaticality violations activated Broca’s region in all participants. This observation lends some support to the suggestions that artificial grammar learning represents a model for investigating aspects of language learning in infants [TICS 4 (2000) 178] and adults [Proceedings of the National Academy of Sciences of United States of America 99 (2002) 529].