Evelina Dineva

Cue salience and infant perseverative reaching: tests of the dynamic field theory

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 perceptual-motor processes that produce reaching in infants and skilled flexible behavior in general.

Comment on “Infants’ Perseverative Search Errors Are Induced by Pragmatic Misinterpretation”

Topál et al. (Reports, 26 September 2008, p. 1831) proposed that infants’ perseverative search errors can be explained by ostensive cues from the experimenter. We use the dynamic field theory to test the proposal that infants encode locations more weakly when social cues are present. Quantitative simulations show that this account explains infants’ performance without recourse to the theory of natural pedagogy.

New methodology to explore the role of visualisation in aircraft design tasks

A toolbox for the assessment of engineering performance in a realistic aircraft design task is presented. Participants solve a multidisciplinary optimisation (MDO) task by means of a graphical user interface (GUI). The quantity and quality of visualisation may be systematically varied across experimental conditions. The GUI also allows tracking behavioural responses like cursor trajectories and clicks. Behavioural data together with evaluation of the generated aircraft design can help uncover details about the underlying decision making process. The design and the evaluation of the experimental toolbox are presented. Pilot studies with two novice and two advanced participants confirm and help improve the GUI functionality. The selection of the aircraft design task is based on a numerical analysis that helped to identify a ‘sweet spot’ where the task is not too easy nor too difficult.

Lessons learned in participative multidisciplinary design optimization

Research into future air vehicles incorporating novel technologies is characterized by a high number of interacting disciplines which need to be considered. Despite advances in numeric interfacing techniques for participative Multidisciplinary Design and Optimisation (pMDO), it is not well understood how to build a team of specialists who jointly operate shared tools and gain system level insight. This contribution shifts focus to the human MDO participants and their working environment. Three aspects of collaboration are considered: (a) design of cognitive experiments to measure engineering performance in different settings; (b) integration of prior experience through a Lessons Learned process; and (c) the application of the above into the enhancement of Integrated Design Laboratory (IDL). The pronunciation of competence and working environment, rather than software tools or data, opens opportunities for attractive use cases.}