Qualitatively Coherent Representation Makes Decision-Making Easier with Binary-Colored Multi-Attribute Tables: An Eye-Tracking Study

We aimed to identify the ways in which coloring cells affected decision-making in the context of binary-colored multi-attribute tables, using eye movement data. In our black-white attribute tables, the value of attributes was limited to two (with a certain threshold for each attribute) and each cell of the table was colored either black or white on the white background. We compared the two natural ways of systematic color assignment: "quantitatively coherent" ways and "qualitatively coherent" ways (namely, the ways in which the black-white distinction represented the quantitative amount distinction, and the ways in which the black-white distinction represented the quality distinction). The former consists of the following two types: (Type 1) "larger is black," where the larger value-level was represented by black, and "smaller is white," and (Type 2) "smaller is black." The latter consisted of the following two types: (Type 3) "better is black," and (Type 4) "worse is black." We obtained the following two findings. [Result 1] The qualitatively coherent black-white tables (Types 3 and 4) made decision-making easier than the quantitatively coherent ones (Types 1 and 2). [Result 2] Among the two qualitatively coherent types, the "black is better" tables (Type 3) made decision making easier; in fact, the participants focused on the more important (black) cells in the case of "black is better" tables (Type 3) while they did not focus enough on the more important (white) ones in the case of the "white is better" tables (Type 4). We also examined some measures of eye movement patterns and showed that these measures supported our hypotheses. The data showed differences in the eye movement patterns between the first and second halves of each trial, which indicated the phased or combined decision strategies taken by the participants.