By manipulating the scale in graphs, this study demonstrated a new evaluation bias caused by attribute salience in graphical representations. That is, (de)compressing the graph axis scale changed the relative distance with respect to the options of a given attribute and thus changed the salience of the information in graphical representations. Experiment 1 showed that the differences in the graphical representations had a significant impact on the evaluation. Experiment 2 repeated the scale manipulation effect in a different scenario and extended it to a multi-options context. Experiment 3 disentangled the effect of scale distance manipulation from the other variables (e.g., scale resolution and assignment of attributes to axes) and further supported the finding of Experiment 1. These results indicated that attribute salience in graphical representations clearly affects evaluations and that graphs can be manipulated to cause very different impressions of the same data. This finding is not consistent with the axioms of normative economic theory. Experiment 3 also tested the attribute importance hypothesis, but the evidence indicated that the participants did not regard the longer axis as the more important attribute. Finally, we related our findings to the impact of visual processing on decision making and discussed them from the perspective of two-system cognitive theory.

We introduce a modified Galton‒Watson process using the framework of an infinite system of particles labelled by (x,t), where x is the rank of the particle born at time t. The key assumption concerning the offspring numbers of different particles is that they are independent, but their distributions may depend on the particle label (x,t). For the associated system of coupled monotone Markov chains, we address the issue of pathwise duality elucidated by a remarkable graphical representation in which the trajectories of the primary Markov chains and their duals coalesce to form forest graphs on a two-dimensional grid.