ted with it. Specifically, the participants were instructed to (1) place the cursor on the target and press the left mouse button, (2) drag the word by moving the mouse (with the left button still being pressed) until the target overlapped with one of the taste words, and (3) to release the target by releasing the left mouse button. Upon making their response, the participants were asked how confident they were that other people would respond in the same fashion as they had, by indicating on a 5-point horizontal scale with the following options, arranged from left to right: Very unconfident, unconfident, uncertain, confident, and very confident. After they had rated their confidence, a “continue” button appeared on the webpage for them to press in order to initiate the next trial. It took ∼8 min to complete the entire study. After completing the experiment, all of the participants were debriefed as to its purpose.
FIGURE 2. An illustrations of the screens on which participants were asked to match the central target with one of the taste terms (upper panel) and to indicate how confident they were about their answer (lower panel).
Data Recording and Analyses
The key dependent variable in this study was the taste word that the participants chose for each target, from the five possible options. That being said, their confidence rating regarding the consensuality of their answer (Koriat, 2008), i.e., the degree to which the participants felt that others would make the same stimulus pairing, was also included in some of the data analyses.
We first analyzed the chosen tastes across the four groups. We combined the data from the four groups of participants, and performed Chi-square tests with Bonferroni’s correction for multiple testing5 to examine whether certain taste(s) were more often associated with a stimulus than other tastes. When the results of these Chi-square tests reached significance, we then performed post hoc Chi-square tests to examine whether the taste that was chosen most often was chosen significantly 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.
Tast