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Odor pleasantness shown to be partly hard-wired

Scientists have "trained" an electronic system to predict the pleasantness of novel odors, just like a human would perceive them, turning on its head the popular notion that smell is completely personal and culture-specific. The scientists, from the Weizmann Institute of Science and Edith Wolfson Medical Center, argue that the perception of an odor's pleasantness is innately hard-wired to its molecular structure, and it is only within specific contexts that personal or cultural differences are made apparent. Details are published April 15 in the open-access journal PLoS Computational Biology.

Over the last decade, electronic devices, commonly known as electronic noses or "eNoses," have been developed to detect and recognize odors. The main component of an eNose is an array of chemical sensors. As an odor passes through the eNose, its molecular features stimulate the sensors in such a way as to produce a unique electrical pattern an "odor fingerprint" that characterizes that specific odor. Like a sniffer dog, an eNose first needs to be trained with odor samples so as to build a database of reference. But unlike humans, if eNoses are presented with a novel odor whose fingerprint has not already been recorded in their database, they are unable to classify or recognize it.

The scientists decided to approach this issue from a different perspective, training the eNose to estimate the odor along an axis of odorant pleasantness In other words, they trained their eNose to predict whether an odor would be perceived as pleasant or unpleasant, or anywhere in between. The scientists stressed that "the uniquness of this approach was that rather than learning singular odorant objects such as "rose" or "skunk", their eNose learned an axis, and could then place novel objects anywhere along the axis it learned".

The scientists first asked a group of native Israelis to rate the pleasantness of a selection of odors according to a 30-point scale ranging from "very pleasant" to "very unpleasant." From this dataset, they developed an "odor pleasantness" algorithm, which they then programmed into the eNose. The scientists then asked the eNose to predict the pleasantness of a completely new set of odors. The scientists found that the eNose was able to generalize and rate the pleasantness of novel odors it never smelled before, and these ratings were about 80% similar to those of naive human raters who had not participated in the eNose training phase. Moreover, if the odors were simply categorized as either "pleasant" or "unpleasant," as opposed to being rated on a scale, it achieved an accuracy of 99%.

To test whether olfactory perception is culture-specific, the scientists tested eNose predictions against a group of recent immigrants to Israel from Ethiopia. The results showed that the eNose's ability to predict the pleasantness of novel odors against the native Ethiopian's ratings was just as good, even though it was "tuned" to the pleasantness of odors as perceived by native Israelis. This suggests a fundamental cross-cultural similarity in odorant pleasantness.

To account for observed cultural differences, Sobel says that "culture influences the perception of olfactory pleasantness mostly in particular contexts. To stress this point, many may wonder how the French can like the smell of their cheese, when most find the smell quite repulsive. We believe that it is not that the French think the smell is pleasant per se, they merely think it is a sign of good cheese. However, if the smell was presented out of context in a jar, then the French would probably rate the odor just as unpleasant as anyone else would; that is why the French don't make cheese-smelling perfume".

The findings of the study could provide new methods for odor screening and environmental monitoring. It may, in the future, contribute to the digital transmission of smell which would scent-enable movies, games and music to provide a more immersive and captivating experience.


Contact: Michell Dror
Public Library of Science

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