Abstract
This paper describes continuing research on the building of neurocognitive models of the internal mental and brain processes of children using a novel adapted combination of existing computational approaches and tools, and using electro-encephalographic (EEG) data to validate the models. The guiding working model which was pragmatically selected for investigation was the established and widely used Adaptive Control of Thought-Rational (ACT-R) modeling architecture from cognitive science. The anatomo-functional circuitry covered by ACT-R is validated by MRI-based neuroscience research. The present experimental data was obtained from a cognitive neuropsychology study involving preschool children (aged 4–6), which measured their visual selective attention and word comprehension behaviors. The collection and analysis of Event-Related Potentials (ERPs) from the EEG data allowed for the identification of sources of electrical activity known as dipoles within the cortex, using a combination of computational tools (Independent Component Analysis, FASTICA; EEG-Lab DIPFIT). The results were then used to build neurocognitive models based on Python ACT-R such that the patterns and the timings of the measured EEG could be reproduced as simplified symbolic representations of spikes, built through simplified electric-field simulations. The models simulated ultimately accounted for more than three-quarters of variations spatially and temporally in all electrical potential measurements (fit of model to dipole data expressed as R2 ranged between 0.75 and 0.98; P < 0.0001). Implications for practical uses of the present work are discussed for learning and educational applications in non-clinical and special needs children's populations, and for the possible use of non-experts (teachers and parents).
Highlights
The primary goal of this paper is to report on continuing research on the building of cognitive models of the internal mental or brain processes of children by using the measurements of electroencephalographic (EEG) data in order to validate the models constructed
A background question permeating this work concerns how much reduction is tolerable in order to achieve models that could one day be relatively implemented for real-world, practical applications for learning
In the following “Experiment” subsection, we describe two behavioral tasks with a sample of young children: one measured the activation of the sensorimotor and perceptual systems engaged in a visual selective attention task, the other task measured linguistic-conceptual and semantic memory systems engaged in a word-verification task
Summary
The primary goal of this paper is to report on continuing research on the building of cognitive models of the internal mental or brain processes of children by using the measurements of electroencephalographic (EEG) data in order to validate the models constructed. Analysis of verbal protocols (Ericsson and Simon, 1993), and other forms of verbal reports (see Runge et al, 2017) could be used to build converging validity for neurocognitive models using the “phenomenology-neuralbehavior triangulation” (see Flanagan and Dryden, 1998) Determining all these elements in young (i.e., infants and preschoolers) children escalates complexity further. A background question permeating this work concerns how much reduction is tolerable in order to achieve models that could one day be relatively implemented for real-world, practical applications for learning (ideally by users such as, for example, educators, teachers, and parents or the learners themselves, the children) For these reasons, the present work assumes the very pragmatic tactic of combining already existing and validated computational tools in a novel way
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