| Literature DB >> 25506332 |
Carlos Velasco1, Diana Balboa1, Fernando Marmolejo-Ramos2, Charles Spence1.
Abstract
Previous research has demonstrated that ratings of the perceived pleasantness and quality of odors can be modulated by auditory stimuli presented at around the same time. Here, we extend these results by assessing whether the hedonic congruence between odor and sound stimuli can modulate the perception of odor intensity, pleasantness, and quality in untrained participants. Unexpectedly, our results reveal that broadband white noise, which was rated as unpleasant in a follow-up experiment, actually had a more pronounced effect on participants' odor ratings than either the consonant or dissonant musical selections. In particular, participants rated the six smells used as being less pleasant and less sweet when they happened to be listening to white noise, as compared to any one of the other music conditions. What is more, these results also add evidence to support the existence of a close relationship between an odor's hedonic character and the perception of odor quality. So, for example, independent of the sound condition, pleasant odors were rated as sweeter, less dry, and brighter than the unpleasant odors. These results are discussed in terms of their implications for the understanding of crossmodal correspondences between olfactory and auditory stimuli.Entities:
Keywords: audition; crossmodal correspondences; olfaction; pleasantness; white noise
Year: 2014 PMID: 25506332 PMCID: PMC4246650 DOI: 10.3389/fpsyg.2014.01352
Source DB: PubMed Journal: Front Psychol ISSN: 1664-1078
FIGURE 1Participants’ average (Harrell–Davis estimator of the median; The ratings were performed on a scale ranging from 0 (not at all) to 100 (very). The error bars represent 95% bias-corrected-and-accelerated bootstrap confidence intervals.
FIGURE 2Average ratings (Harrell–Davis estimator of the median) for each attribute in each odor and musical condition. The error bars represent 95% bias-corrected-and-accelerated bootstrap confidence intervals (Efron, 1987).
Results of the ATS performed on each of the attributes assessed in the experiment.
| Pleasantness | M: | M × O: | M: WN (50.23) vs. PM (54.04): |
| O: | O: WN (50.23) vs. UM (53.42): | ||
| PM (54.04) vs. UM (53.42): | |||
| NO (52.69) vs. PO (76.74): | |||
| NO (52.69) vs. UO (25.50): | |||
| PO (76.74) vs. UO (25.50): | |||
| Intensity | M: | M × O: | O: NO (13.96) vs. PO (57.61): |
| O: | NO (13.96) vs. UO (67.54): | ||
| PO (57.61) vs. UO (67.54): | |||
| Sweetness | M: | M × O: | M: WN (44.21) vs. PM (47.61): |
| O: | O: WN (44.21) vs. UM (47.90): | ||
| PM (47.61) vs. UM (47.90): | |||
| NO (50.14) vs. PO (69.65): | |||
| NO (50.14) vs. UO (20.55): | |||
| PO (69.65) vs. UO (20.55): | |||
| Dryness | M: | M × O: | M: WN (42.85) vs. PM (41.06): |
| O: | O: WN (42.85) vs. UM (38.46): | ||
| PM (41.06) vs. UM (38.46): | |||
| NO (31.34) vs. PO (25.71): | |||
| NO (31.34) vs. UO (73.14): | |||
| PO (25.71) vs. UO (73.14): | |||
| Acidity | M: | M × O: | O: NO (19.61) vs. PO (53.80): |
| O: | NO (19.61) vs. UO (36.85): | ||
| PO (53.80) vs. UO (36.85): | |||
| Brightness | M: | M × O: | O: NO (40.13) vs. PO (63.54): |
| O: | NO (40.13) vs. UO (25.59): | ||
| PO (63.54) vs. UO (25.59): | |||
M, music; O, odor; WN, white noise; PM, pleasant music; UN, unpleasant music; NO, no-odor; PO, pleasant odor; UO, unpleasant odor; FDR, false discovery rate (Bejamini and Hochberg, 1995); MHD, Harrell–Davis estimator of the median. The p-value of the FDR should be taken as the adjusted final p-value obtained.
FIGURE 3Correlations between the ratings for the attributes of the pleasant music and pleasant odors (A), the white noise and pleasant odors (B), the unpleasant music and unpleasant odors (C), and the white noise and unpleasant odors (D) conditions. The value within each box represents the Kendall tau coefficient and the p-values should be interpreted as follows: •p < 0.5, *p < 0.05, **p < 0.01, ***p < 0.001. In order to be coherent with the non-parametric tests used, locally weighted scatterplot smoothing (LOESS) lines (a non-parametric regression method) are shown instead of the traditional parametric straight lines (parametric regression method). Note, however, that interpretation of these lines is different from traditional ones. For example, the fitting lines in the scatterplot of intense-sweet (D) seem to indicate that there is a negative quadratic relationship, but this curvature is caused by only a few data points. The same occurs with intense-pleasant negative quadratic relationship.