Unstructured numerical intensity scales: Models, protocols and errors.

A sample of 62 untrained subjects were assessed on their ability to use unstructured numerical 9-point and 30-point category scales along with an unstructured line scale, using both rank-rating and serial monadic protocols. Visual stimuli were used for convenience, the task being to rate the heights of 12 easily discriminable columns of mung beans held in transparent vertical cylinders. Such stimuli had no perceptual variance, which would otherwise have added uncontrolled variance to the subjects' performance. Two measures of performance were used for each of the 6 experimental conditions. First, mean number of 'scaling errors' made in each of the six experimental conditions was computed. In this experiment, a scaling error was defined as giving a taller column a score equal to or less than a shorter column. The lower the error count, the better the subjects' performance. The second measure was to match the subjects' rating scale pattern of scores to a 'true' pattern of scores, derived from the physical measurements of the 12 columns. For this, a 'dissimilarity score' was developed. This compared the sum of the Euclidean distances between standardized true scale ratings for each of the column's 12 true heights, with those obtained from each subject. This gave a measure of the mismatch between the subject's set of scores and the true set of scores. Both the scaling error counts and the dissimilarity measures, indicated that subjects performed significantly better using the rank-rating protocol than the serial monadic. This was because of the effects of forgetting the exact intensities of stimuli once they had been removed, removal of stimuli being a necessary part of the serial monadic protocol. Subjects were penalized when using the 9-point scales, because there were too few categories to represent the different heights of all 12 columns. This introduced the concept of 'sufficient space'. Using the rank-rating protocol, the 30-point and line scales, with no memory problems and sufficient space elicited the best performances; they were not significantly different.

The 9-point hedonic scale and hedonic ranking in food science: some reappraisals and alternatives.

The 9-point hedonic scale has been used routinely in food science, the same way for 60 years. Now, with advances in technology, data from the scale are being used for more and more complex programs for statistical analysis and modeling. Accordingly, it is worth reconsidering the presentation protocols and the analyses associated with the scale, as well as some alternatives. How the brain generates numbers and the types of numbers it generates has relevance for the choice of measurement protocols. There are alternatives to the generally used serial monadic protocol, which can be more suitable. Traditionally, the 'words' on the 9-point hedonic scale are reassigned as 'numbers', while other '9-point hedonic scales' are purely numerical; the two are not interchangeable. Parametric statistical analysis of scaling data is examined critically and alternatives discussed. The potential of a promising alternative to scaling itself, simple ranking with a hedonic R-Index signal detection analysis, is explored in comparison with the 9-point hedonic scale.

A transfer of technology from engineering: use of ROC curves from signal detection theory to investigate information processing in the brain during sensory difference testing.

This article reviews a beneficial effect of technology transfer from Electrical Engineering to Food Sensory Science. Specifically, it reviews the recent adoption in Food Sensory Science of the receiver operating characteristic (ROC) curve, a tool that is incorporated in the theory of signal detection. Its use allows the information processing that takes place in the brain during sensory difference testing to be studied and understood. The review deals with how Signal Detection Theory, also called Thurstonian modeling, led to the adoption of a more sophisticated way of analyzing the data from sensory difference tests, by introducing the signal-to-noise ratio, d', as a fundamental measure of perceived small sensory differences. Generally, the method of computation of d' is a simple matter for some of the better known difference tests like the triangle, duo-trio and 2-AFC. However, there are occasions when these tests are not appropriate and other tests like the same-different and the A Not-A test are more suitable. Yet, for these, it is necessary to understand how the brain processes information during the test before d' can be computed. It is for this task that the ROC curve has a particular use.