more often than the second most frequently chosen taste.
Next, we analyzed any cross-cultural differences in terms of the tastes that were chosen. We performed log-linear analyses for each stimulus, using taste (bitter, salty, sour, sweet, and umami) × country (China, India, Malaysia, and the USA) as the variables.
After, ‘taste-patterns’ (i.e., the overall pattern of responses across all of the stimuli) were computed for each group of participants, and were weighted by their confidence ratings by being multiplied by the respective coefficient (0 for very unconfident, 0.25 for not confident, 0.5 for uncertain, 0.75 for confident, and 1 for very confident). representational similarity analysis (RSA) was performed using pyRSA (http://github.com/ilogue/pyrsa). The representational dissimilarity matrices (RDMs, see Kriegeskorte et al., 2008) were calculated for each cultural group, in order to compare the crossmodal correspondence between one stimulus and the five tastes (i.e., the taste-pattern) to the taste-pattern of another stimulus. The emerging structure seen in the RDM displays the structure in the taste representations of the stimuli, such as categories. For instance, within one stimulus category, the taste-patterns may be more similar than outside of that category. This would show as a blue (low dissimilarity) block in the RDM. Here, the dissimilarity between two taste-patterns was calculated as 1 minus the correlation coefficient, Pearson’s r, between these two patterns. Therefore, each RDM has n rows and n columns, where n represents the number of stimuli that participants were asked to associate with the tastes. After that, we calculated a cross-cultural RDM using the same method in order to compare the crossmodal associations between cultures.
Multidimensional scaling (MDS) was also used in order to display the similarity distances between the stimuli by compressing all of the dimensions into two. In the MDS figure, those stimuli having taste-patterns that were more similar are closer to each other.
Taste-Patterns Across the Four Groups
First, we visualized the associations between each stimulus and the five basic tastes in Figure 3, and performed Chi-square tests in order to see whether certain taste(s) were more often associated with one taste than the others. As can be seen in Table 2, all the colors except blue were associated with certain taste(s). The results of post hoc Chi-square tests revealed that some colors were certainly more strongly associated with one taste than with other tastes, including black with bitter, green with sour, pink with sweet, and white with salty.
FIGURE 3. The taste-patterns for each color (upper panel), each shape (lower panel), and each texture patch (on the right of the upper panel). Note that the percentage of the bitter, salty, sour, sweet, and umami taste terms chosen for the visual stimuli are represented by the fill-in colors black, white, green, pink, and blue, respectively.
TABLE 2. Results of Chi-square tests for each stimulus by all four groups of participants (N = 428).
As can be seen in Table 1 where the results of this study and two previous studies are summarized, some of our results are consistent with those reported by previous research, such as the white-salty associations (O’Mahony, 1983; Koch and Koch, 2003), and green-sour (Koch and Koch, 2003; Tomasik-Krótki and Strojny, 2008). By contrast, the crossmodal associations consistently observed in these three previous studies, i.e., the red-sweet and yellow-sour associations, failed to reach significance in the present study.
What is more, all of the shapes except the Arrow and Drop were associated with certain taste(s). The results of post hoc Chi-square tests revealed that some shapes were more strongly associated with one taste than the others, including cross/rectangle/square with bitter, and cloud/heart/star with sweet. The results also revealed that both the smooth and rough texture patches were strongly associated with the salty taste.
After weighting all the data by the participants’ confidence ratings 