Published 3 October 2005
The major focus of the program is on cognitive development at both ends of the lifespan-cognitive changes during childhood and senescence. Although the primary focus is on cognitive development, secondary questions relate to the age-related changes in the interaction between cognition and affect and to cognitive deficits that may occur when development goes astray or to deficits that are associated with cognitive decline. In studying cognitive development, multiple methods are used ranging from the experimental analysis of performance (mostly speeded responses and accuracy measures), non-invasive measurements of the central and autonomic nervous system (respectively, EEG, MEG, brain potentials, and heart rate, respiration, skin conductance, eye-movements, electromyographic activity), formal modeling and dedicated statistical analysis (respectively, neural networks, catastrophe modeling, Markov modeling, and latent class analysis, source analysis of brain potentials).
The program consists of two research domains: Developmental Processes and Brain and Development. Within the context of Developmental Processes the emphasis is on (i) nonlinear dynamical modeling of cognitive development and (ii) the construction and application of adaptive signal analysis techniques to EEG/MEG measurements. The research into Brain and Development centers on (i) the development of cognitive control during childhood and senescence and (ii) the construction and application of experimental tasks for the analysis of individual differences during development and clinical groups, or variations in organismic state. The growing interaction between both themes is facilitated by a shared interest in mental chronometry and electrocortial activity. This interaction converged on the common theme Cognitive Development. Thus, the common theme carries the same label as our program as a whole. This is to signify that we consider this theme the very heart of our program. In this section, we will present the two themes of the program, Developmental Processes and Brain and Development. The common theme, Cognitive Development, will be presented in the section on ‘Strategy and policy’ for reasons outlined there.
The focus within this theme of the program is on the modeling (computational, mathematical and statistical) of developmental processes, primarily in cognitive and perceptual development. The driving force behind this program is the pressing need to formalize theories of development into concrete and testable models. Our work is guided by a dynamic systems theoretical account of development and, in particular, developmental transitions. Mathematical models have been developed with the use of catastrophe theory. Based on mathematical models of phase transitions, experimental designs and advanced statistical techniques have been developed and applied to central issues in cognitive development (e.g., proportional reasoning, analogical reasoning, discrimination learning and perceptual classification) but also in various other areas of psychology (e.g., adult cognition, perception, movement, emotions, attitudes, addiction). Dynamical models with transitions imply psychological typologies in behavior (e.g., styles, types, strategies, developmental phases). Thus, during the past few years, there is an increasing focus on statistical techniques and psychometric theories suitable for the analysis of typologies (i.e., categorical latent structure models, stochastic catastrophe theory, time series models). These techniques and theories have been applied successfully in our work on transitions in cognitive development (e.g., rule use in reasoning on the well known Balance task).
Cognitive development does not proceed in a vacuum but is limited by brain maturational processes that extend well into late adolescence. Thus, it is of vital importance to know which brain areas are activated during cognitive task processing, how these areas interact, and how these interactions change dynamically during childhood and senescence. We therefore developed new statistical methods to analyze the sources of EEG/MEG brain avctivity, and the interactions between these sources. We recently extended this statistical approach to fMRI as well. Due to these developments, we are now in the unique position to make important contributions to the analysis of both EEG, MEG and fMRI measures that provide a non-invasive window on the brain regions underlying cognitive processing.
The central focus of this theme is on age-related changes in the neurocognitive control of behavior at both ends of the lifespan. In broad outline, neurocognitive control refers to the ability to dynamically adjust to the changing demands of the environment and the brain mechanisms implicated in goal-oriented behaviors. There is mounting evidence from various sources (e.g., animal work, clinical neuropsychology, and cognitive neuroscience) indicating that prefrontal cortex is implicated in neurocognitive control. Moreover, several indicators of brain structure (e.g., myelination and synaptogenesis in development and de-myelination and cortical thinning during senescence) indicate that this brain region is the last to fully mature and the first to decline during senescence. The ‘last-in/first-out’ hypothesis guided our work during the past period and was contrasted with the prominent ‘global-change’ hypothesis suggesting that all processing components mature in concert and decline at a similar rate. Most of our results provide support for the ‘last-in/first’ out hypothesis. The focus of current work is on the decomposition of neurocognitive control into a limited set of constituent mechanisms. Based on our reading of the pertinent literature and the results that emerged from our own work, we identified inhibition, flexibility, and working memory as candidate mechanisms. Preliminary results show that the three candidate mechanisms are separable and follow differential developmental trends during late childhood and adolescence.
A focus that gained in importance during the past few years is concerned with decision-making (i.e., reward processing and feedback-based association learning) and performance monitoring (i.e., observing one’s actions and action tendencies as well as the external environment for signs of error or conflict). Using heart rate recordings, we demonstrated that developmental change in feedback-based association learning is critically dependent on the ability to detect errors and processing performance feedback. Using EEG recordings, combined with neurocomputational modeling, we established that deficiencies in feedback-based association learning in older age are primarily related to the mesofrontal dopamine system. Combined studies using heart rate and EEG measures suggest separable but collaborative brain regions underlying performance monitoring.
Source: Developmental Psychology
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