The Goal Circuit Model: A Hierarchical Multi‐Route Model of the Acquisition and Control of Routine Sequential Action in Humans

Human control of action in routine situations involves a flexible interplay between (a) task‐dependent serial ordering constraints; (b) top‐down, or intentional, control processes; and (c) bottom‐up, or environmentally triggered, affordances. In addition, the interaction between these influences is modulated by learning mechanisms that, over time, appear to reduce the need for top‐down control processes while still allowing those processes to intervene at any point if necessary or if desired. We present a model of the acquisition and control of goal‐directed action that goes beyond existing models by operationalizing an interface between two putative systems—a routine and a non‐routine system—thereby demonstrating how explicitly represented goals can interact with the emergent task representations that develop through learning in the routine system. The gradual emergence of task representations offers an explanation for the transfer of control with experience from the non‐routine goal‐based system to the routine system. At the same time it allows action selection to be sensitive both to environmental triggers and to biasing from multiple levels within the goal system.     

Mothers speak differently to infants at‐risk for dyslexia

Dyslexia is a neurodevelopmental disorder manifested in deficits in reading and spelling skills that is consistently associated with difficulties in phonological processing. Dyslexia is genetically transmitted, but its manifestation in a particular individual is thought to depend on the interaction of epigenetic and environmental factors. We adopt a novel interactional perspective on early linguistic environment and dyslexia by simultaneously studying two pre‐existing factors, one maternal and one infant, that may contribute to these interactions; and two behaviours, one maternal and one infant, to index the effect of these factors. The maternal factor is whether mothers are themselves dyslexic or not (with/without dyslexia) and the infant factor is whether infants are at‐/not‐at family risk for dyslexia (due to their mother or father being dyslexic). The maternal behaviour is mothers’ infant‐directed speech (IDS), which typically involves vowel hyperarticulation, thought to benefit speech perception and language acquisition. The infant behaviour is auditory perception measured by infant sensitivity to amplitude envelope rise time, which has been found to be reduced in dyslexic children. Here, at‐risk infants showed significantly poorer acoustic sensitivity than not‐at‐risk infants and mothers only hyperarticulated vowels to infants who were not at‐risk for dyslexia. Mothers’ own dyslexia status had no effect on IDS quality. Parental speech input is thus affected by infant risk status, with likely consequences for later linguistic development.     

Cognitive,not physical,engagement in video gaming influences executive functioning

Physically active video games (i.e., exergames), which are a prevalent and popular childhood activity, may have benefits to executive-functioning (EF) skills, as they incorporate both cognitive engagement and physical activity. Acute EF change in 147 7- to 12-year-olds was assessed after participation in a 20-min activity. The between-subjects design had 4 conditions: exergame, sedentary video game, exercise, and nonplaying control. The varied level of physical and cognitive engagement examined whether the nature of the activity, such as physical movement or cognitive engagement in an enjoyable activity, differentially related to changes in different aspects of EF. Participants in the cognitively engaging conditions (i.e., the 2 video game conditions) improved their accuracy on the most complex EF test and their reaction time on the standard EF test more than participants in the other conditions. These findings suggest that the kind of cognitive engagement involved in video game play is the mechanism of acute effects on EF.     

TreeBUGS: An R package for hierarchical multinomial-processing-tree modeling

Multinomial processing tree (MPT) models are a class of measurement models that account for categorical data by assuming a finite number of underlying cognitive processes. Traditionally, data are aggregated across participants and analyzed under the assumption of independently and identically distributed observations. Hierarchical Bayesian extensions of MPT models explicitly account for participant heterogeneity by assuming that the individual parameters follow a continuous hierarchical distribution. We provide an accessible introduction to hierarchical MPT modeling and present the user-friendly and comprehensive R package TreeBUGS, which implements the two most important hierarchical MPT approaches for participant heterogeneity—the beta-MPT approach (Smith & Batchelder, Journal of Mathematical Psychology 54:167-183, 2010) and the latent-trait MPT approach (Klauer, Psychometrika 75:70-98, 2010). TreeBUGS reads standard MPT model files and obtains Markov-chain Monte Carlo samples that approximate the posterior distribution. The functionality and output are tailored to the specific needs of MPT modelers and provide tests for the homogeneity of items and participants, individual and group parameter estimates, fit statistics, and within- and between-subjects comparisons, as well as goodness-of-fit and summary plots. We also propose and implement novel statistical extensions to include continuous and discrete predictors (as either fixed or random effects) in the latent-trait MPT model.