In Search of an Optimizer Matrix for Affordance Design 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 triggertrain 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 LeCunu0027s common filter to improve search strategies and identification of the region of interest across sample welldefined and illdefined contexts. Function (goal), structure (associative schema) and behaviour (actionable possibilities) stand out as the metaheuristics, above shape and colour. A hierarchical optimizer matrix linking affordance design with schematics is then proposed.

Research on Generating an Indoor Landmark Salience Model for SelfLocation and Spatial Orientation from EyeTracking Data Landmarks play an essential role in wayfinding and are closely related to cognitive processes. Eyetracking data contain massive amounts of information that can be applied to discover the cognitive behaviors during wayfinding; however, little attention has been paid to applying such data to calculating landmark salience models. This study proposes a method for constructing an indoor landmark salience model based on eyetracking data. First, eyetracking data are taken to calculate landmark salience for selflocation and spatial orientation tasks through partial least squares regression (PLSR). Then, indoor landmark salience attractiveness (visual, semantic and structural) is selected and trained by landmark salience based on the eyetracking data. Lastly, the indoor landmark salience model is generated by landmark salience attractiveness. Recruiting 32 participants, we designed a laboratory eyetracking experiment to construct and test the model. Finding 1 proves that our eyetracking databased modelling method is more accurate than current weighting methods. Finding 2 shows that significant differences in landmark salience occur between two tasks; thus, it is necessary to generate a landmark salience model for different tasks. Our results can contribute to providing indoor maps for different tasks.