Prediction Models for the Pleasantness of Binary Mixtures in Olfaction

摘要

Whereas the rules underlying the perceived intensity of binary mixtures have been investigated,minimal efforts have been directed at elucidating the rules underlying the perceived pleasantness of such mixtures.To address this,84 subjects ranked the pleasantness and intensity of 5 distinct binary mixtures(15 pairs,inter-stimulus interval=4 s,inter-trial interval=30 s,flow=6 l/min,pulse=2 s)constructed from different ratios(0:100%,25:75%,50:50%,75:25%,and 100:0%,olfactometer-generated vapor phase).We found that in the majority of cases,the pleasantness of the mixture fell between the pleasantness values of its separated constituents and that it was strongly influenced by the relative intensities of the constituents.Based on these results,we proposed a prediction paradigm for the pleasantness of binary mixtures from the pleasantness of their separated constituents weighted by their respective perceived intensities.The uniqueness of the proposed paradigm is that it neither requires presetting an interaction constant between the mixture components nor require any factorization of the pleasantness weights.It does,nonetheless,require solid psychophysical data on the separated components at their different concentrations,and currently it can only explain the behavior of intermediate pleasantness of mixtures.