Skilled behavior requires a balance between previously successful behaviors and new behaviors appropriate to the present context. We describe a dynamic field model for understanding this balance in infant perseverative reaching. The model predictions are tested with regard to the interaction of two aspects of the typical perseverative reaching task: the visual cue indicating the target and the memory demand created by the delay imposed between cueing and reaching. The memory demand was manipulated by imposing either a 0-or a 3-second delay, and the salience of the cue to reach was systematically varied. Infants demonstrated fewer perseverative errors at 0-delay versus 3-second delay based on the cue salience, such that a more salient visual cue was necessary to overcome a longer delay. These results have important implications for understanding both the basic perceptualmotor processes that produce reaching in infants and skilled flexible behavior in general.

That competences may emerge given appropriate environmental and behavioral context is a long-standing theme in developmental research. Work in the motor domain, but also in cognitive development, has made it possible to transform this idea into a mechanistic account closely linked to empirical evidence. In dynamic systems thinking, such capacities as keeping a motor goal in mind, remembering a location, or resisting a motor habit, are all understood in terms of the generation of stable patterns of neuronal activation. These may be input-driven, but also be stabilized by interactions within neuronal representations. A key theoretical insight is that whether a particular pattern of activation is stable or not is not determined by any single factor, learning process, or structural parameter. Instead, ongoing activity, recent activation history, current input, all may affect when a particular dynamic regime is reachable. In spite of such broad interdependence, sharp transitions may characterize the onset of a skill in any given context. Dynamic instabilities are the mechanistic basis for this phenomenon and thus form the basis for understanding development in terms of emergence. We exemplify the concepts of instability and emergence around the phenomenon of infant perseverative reaching and discuss implications for identifying key markers of development and their link to neuronal processes.

This chapter seeks to articulate and clarify cases of perceived differences between dynamical systems theory (DST) and the connectionist (CN) approaches that are not real, as well as cases of perceived differences that are real. It discusses the implications of efforts to integrate the two approaches for developmental science more generally. Clarifying similarities/differences between approaches offers far more that just technical clarity for co-called modeler types; it offers a vision of a new, integrative, developmental theory.

In Piaget's classical A-not-B-task, infants repeatedly make a sensorimotor decision to reach to one of two cued targets. Perseverative errors are induced by switching the cue from A to B, while spontaneous errors are unsolicited reaches to B when only A is cued. We argue that theoretical accounts of sensorimotor decision-making fail to address how motor decisions leave a memory trace that may impact future sensorimotor decisions. Instead, in extant neural models, perseveration is caused solely by the history of stimulation. We present a neural dynamic model of sensorimotor decision-making within the framework of Dynamic Field Theory, in which a dynamic instability amplifies fluctuations in neural activation into macroscopic, stable neural activation states that leave memory traces. The model predicts perseveration, but also a tendency to repeat spontaneous errors. To test the account, we pool data from several A-not-B experiments. A conditional probabilities analysis accounts quantitatively how motor decisions depend on the history of reaching. The results provide evidence for the interdependence among subsequent reaching decisions that is explained by the model, showing that by amplifying small differences in activation and affecting learning, decisions have consequences beyond the individual behavioural act.

The article describes observations from the online teaching of a robotics class during the COVID-19 pandemic caused by SARS-CoV-2, also known as the coronavirus. The changes in the course structure and in the provided material lead to an unexpected increase in the grade performance of the students. The article provides a description and an analysis of the effects and their possible causes. In addition to a grade-performance analysis, further data from a university-wide and from a course-specific survey are used. The analysis of the effects and their possible causes is furthermore discussed in relation to the educational research literature. Some evidence for the general findings is provided, which are of interest for online teaching or blended learning in general, respectively, for teaching in robotics and related areas. These include some evidence for the benefits of asynchronous online teaching and for the role of social interaction, which may happen in self-organized, smaller peer groups, even without the intervention of the instructor. The findings and the extensive pointers to the literature can also provide useful guidelines for instructors of robotics courses when considering the use of online or blended teaching in the future beyond the COVID-19 pandemic.