Recent publications in the domains of interactive evolution- ary computation and data visualisation consider an emerg- ing topic coined Evolutionary Visual Exploration (EVE). EVE systems combine visual analytics with stochastic opti- misation to aid the exploration of complex, multidimensional datasets. In this work we present an experimental analysis of the behaviour of an EVE system that is dedicated to the vi- sualisation of multidimensional datasets, which are generally characterised by a large number of possible views or projec- tions. EvoGraphDice is an interactive evolutionary system that progressively evolves a small set of new dimensions, to provide new viewpoints on the dataset, in the form of lin- ear and non-linear combinations of the original dimensions. The criteria for evolving new dimensions are not known a priori and are partially specified by the user via an interac- tive interface: (i) The user selects views with meaningful or interesting visual patterns and provides a satisfaction score. (ii) The system calibrates a fitness function to take into ac- count the user input, and then calculates new views, with the help of an evolutionary engine. In previous work (an ob- servational study), we showed that EvoGraphDice was able to facilitate "exploration" tasks, helping users to discover new interesting views and relationships in their data. Here, we focus on the system's "convergence" behaviour, conduct- ing an experiment with users who have a precise task to perform. The experimental task is set up as a geometrical game, and collected data show that EvoGraphDice is able to "learn" user preferences in a way that helps users fulfill their task (i.e. converge to desired solutions).