In Search of an Optimizer Matrix for Affordance Design

Abstract

Affordance in product, packaging and service design has attracted much attention. Aiming at increasing perceived usefulness and usability, foci has increasingly extended to user experience and interaction. Neuroscience research can also benefit from affordance research, i.e., by designing external task demands to trigger/train functional networks and possibly, to compensate dysfunctional networks. Visual processing influences diverse cognitive processings. Hence, aspects investigated are types of contexts and properties which contribute to visual salience (colour, shape). The aim is to investigate which among these factors can serve as LeCun's common filter to improve search strategies and identification of the region of interest across sample well-defined and ill-defined contexts. Function (goal), structure (associative schema) and behaviour (actionable possibilities) stand out as the meta-heuristics, above shape and colour. A hierarchical optimizer matrix linking affordance design with schematics is then proposed.