Analysis of Sociocultural and Optical Influences on Olfactory Memory

Blog, Journal for High Schoolers, Journal for High Schoolers 2021

Authors

Madison Abbassi, Jonathan Mulenga, Subah Mahbub, Esther Chung, Teyon Herring, Eric Guo, Dante Aguilar, Arindam Saha

Abstract

The field of olfaction is a rising topic of interest, with researchers globally making new discoveries about olfaction and its connections with our other basic senses. Studies over the past few decades have uncovered the effects of age, disease, color, and other sensory stimuli on people’s “smell memories” and their differences in smell perception. Research into olfaction is particularly relevant today given the COVID-19 pandemic: many who experienced hyposmia or anosmia during the pandemic have reported diminished happiness, along with disruptions to their normal cooking and eating patterns. For this study, our research group decided to use surveys due to our virtual setting and designed two surveys to examine potential areas impacting the perception of olfaction: the first survey tested image quality and alternative coloration of images; the second survey investigated relationships between participants’ sociocultural backgrounds and their preferences for certain smells. We deployed our surveys across different online forums to get a random, diverse group of participants.

Based on the findings from our prototype surveys, we expected our results to show an association between higher quality or brightly colored images and stronger olfactory perception. We also hypothesized that our sociocultural survey respondents will have stronger and more favorable perceptions of odors that they are most familiar with – whether that is due to frequent exposure or common use of the ingredient in a cultural dish that matches the participant’s cultural background.

Background/Literature Review

Olfaction, the sense of smell, plays a complex role in our daily lives, subtly influencing everything from our eating patterns to our social habits. During the current COVID-19 pandemic, some patients have reported experiencing anosmia – total loss of smell – or hyposmia – reduced ability to smell – as a direct result of the virus. Gaining a better understanding of how we can stimulate smell memories even when a respondent is unable to physically smell the odor will potentially help with future technologies or programs for the smell-impaired. A respondent to our survey who had lost her sense of smell post-COVID described the experience as losing “half the essence of life”, furthering the importance of olfaction-related research: a deeper understanding of arguably the most disregarded sense can help others to continue experiencing this “essence of life,” whether it is through novel technologies or by restoring smell for those who have been impaired by illness. As previous olfactory studies have done, this is best achieved by interacting with study participants, and more specifically to our study, by investigating the conditions that optimize olfactory memory/smell recall.

Prior studies in the area of olfaction research have established the effect of color on strengthening olfactory memory [1], linking changes in the color of a food item’s appearance with differences in reported odor perception. Professor Mariana Obrist’s lab collected in-depth responses from survey participants describing how they experience smell. The reported adjectives used by participants to describe scents (including heavy, penetrating, sweet, reassuring, and erotic, to name a few) suggested (1) a strong connection between smell perception and perception in other senses, and (2) a close relationship between intensity of smell memories and emotions associated with that smell.

However, there has not been much prior research on correlations between sociocultural background and ability to recall the odors of culturally linked ingredients such as kimchi, turmeric, cardamom, chilli pepper, and coffee. Furthermore, research linking smell to other senses is rather new, and there is a lot more to be explored in that space. To that end, we designed and deployed surveys aiming to further investigate the smell experience and its relationship with both cultural and multisensory factors.

Research Methodologies

Survey Design:

For our study, we designed and deployed surveys to measure participants’ ability to recall odors with a particular stimulus. After a few preliminary test surveys, we finalized two Google Form surveys: a multisensory survey (testing the impact of visual stimuli on odor recollection) and a multicultural survey (testing for correlations between cultural backgrounds and ability to recall the smell of certain culture-specific ingredients).

Both of the surveys were designed to maximize the efficiency of survey responses from a psychological understanding and approach. The beginning of the survey started with easy questions, including general information about one’s demographics or sexual orientation. Working off the momentum from the easier questions, the survey questions gradually progressed to harder questions, which required more reflection and activation of smell memories [3]. Pictures were added as part of survey questions to draw in more attention from the survey participants and randomization of question order was implemented in the survey to limit response biases (more specifically, participants responding in ways they believe researchers want them to rather than giving honest answers) [2]. The questions were in the form of multiple-choice questions, short responses, and linear scale questions. Two question varieties were utilized to maximize the accuracy of our conclusions. The question varieties are: same questions with different objects which were repetitive but reinforced the conclusions of an idea), and multiple variables accounted into one question format (for easier analysis of the data). Both of the surveys are explained in detail below:

Multisensory Survey:

To evaluate smell relationship from a multisensory aspect, we incorporated questions related to exposure, taste, and visual senses. To test how exposure affects smell recall, survey respondents were asked to rate how much they were exposed to and how often they consumed a particular food item on a scale of 1-5. The highlight of the multisensory survey was to find a connection between sight and ability to recall a certain smell. The normally colored image of a food item was set as the control and as a basis to compare the other variables’ results. Versions of each control image were made into a quality/pixelation, black and white, and multiple alternative colors: neon green, pink, and purple were chosen to be applied to the control as most food items on the survey do not naturally have such colors. The changes in visuals were obtained using Picsart app (for alternative color of the control), Pinetools website (or changing image quality through changing the pixelation of the control), and Adobe Spark (for black and white image of the control). We asked the survey respondents to rate their ability to recall the smell of the food item on a scale of 1-5 based on the different changes of the control image displayed in the survey.

Fig. 1: Some examples of visual stimuli that were used in the surveys.

Multicultural Survey:

In order to analyze the impact of cultural grouping on recalling certain smells, we chose universal along with culture-specific ingredients dominant in 7 different cultures to be incorporated in the survey. There are a total of 26 ingredients present in the survey from Asian, African, American, Latin American, European, Middle Eastern and South Asian cultures. The cultural groupings were made based on the previous responses of our initial surveys. We predict that respondents of the survey will potentially have stronger and more favorable recalling of odors that they are most familiar with from their respective cultures. For example, since a dominant ingredient from South Asia is cardamom, we predict that the smell of cardamom could potentially be more strongly recalled from that cultural group. To test this potential hypothesis, we asked our participants how intensely they can recall the smell of a particular ingredient on a scale of 1-7. Secondly, we asked the participants how strongly they liked a particular ingredient on a scale of 1 to 7. General demographic questions were added as well to evaluate the different cultures and racial associations of the survey respondents.

Survey Deployment

In order to reach a diverse sample of responses, we applied a three-pronged approach to deploying our surveys to multiple parties: Facebook survey exchange groups, Reddit forums such as r/samplesize and r/takemysurvey, and circulating it among our own communities. Facebook survey exchange groups are a community of random people from across the globe who are willing to take a survey in exchange for you completing a survey yourself. Reddit forums follow a similar concept as having random people from across the globe without the exchange system. Having members of the team located in different parts of the United States surrounded by a multitude of diverse people presented us with another method of survey data collection.

Website (UI)

Google Forms is an editable survey document with many bugs in participant survey filling. Users reported that question order was incorrect in multiple different ways informing us that each time the survey was taken, questions were moved without our command. Another inefficiency we discovered was the constant repetition of questions that could be condensed into a much neater, easier to read format. To eliminate these disadvantages from our survey we created our own website.

Creating our own website provided us the ability to have total control over survey creation allowing us to make our desired survey design. In addition to this upgrade, we were able to create a user-friendly interface giving each survey participant easy access to either of the two surveys and a nice display of both questions and answer choices. Also with total control of the survey, we were able to add more functionality to the survey. The website has a randomization feature that mixes all survey questions excluding the demographic questions to eliminate participants from figuring out the survey’s objective and adding bias into their responses. Another feature is a timer which allows us to see which questions take the most time and analyze the flow of the survey. A final bonus to having our own website is cross-platform compatibility giving participants the opportunity to fill the survey on any device of their choosing as long as they possessed a wifi-connection.

Fig. 2: Multisensory survey questions taken from the website.

Keywords – Vue.js, Firebase, Multisensory (MS), Multicultural (MC),

– Firebase Image

Fig. 3: Firebase database containing all survey responses.

Vue.js is a coding tool that builds interactive user interfaces and allows for easy integration and collaboration with other services and functionalities. Vue.js draws heavily from HTML, Javascript, and CSS, which are the basic tools of any web designer, thus providing an easy learning curve to those with prior experience. The benefits of using Vue.js include an easier front-end designing process, as seen through our website’s drop-down menu along with formatting of questions. Compared to old-school HTML, establishing the layout of the website is simplified with pre-built features such as v-row and v-col. Furthermore, Vue.js simplifies many aspects of survey design, which can be seen in the data storage of survey responses along with the creation of question templates and types. Vue.js eliminates the need to establish traditional formatting tools, such as Float-based or Grid-based layout on HTML.

Firebase is a service developed by Google for data storage and data CRUD (Create, Read, Update, Delete). Firebase ensures data security because of its client-side coding properties, while also creating numerous future add-ons through Google’s offerings of extensions that can apply onto the web.app.

The process of connecting Firebase and Vue.js together involves creating a Firebase project on Firebase console, then connecting the web API keys into the Vue.js code, which then opens up all firebase commands to be used in Vue.js, such as creating data collections and using code to CRUD data as needed in each survey.

After the connecting of Firebase and Vue.js, Firebase serves as the database and thus is the source that we draw from when analyzing data results. By activating Firebase’s blaze plan, we set up gcloud for our survey and can export all documents on the console of Google Cloud platform’s import/export tab, thus giving us our final .csv file, which could be used for further analysis.

Fig. 4: Google Cloud Platform to export/import data for .csv files.
Fig. 5: Code for Survey1, which as seen is a .vue file.

Data Analysis

Data analysis of the survey responses were conducted through Google Colab and Google Sheets. Each provided advantages in making our conclusions in an efficient and organized manner. Google Collab was able to support statistically complicated analyses, such as correlation tests, and some visuals while Google Sheets produced graphs, charts, and relative R-values much faster.

In order to make better use of Google Colab for data analysis, a series of packages were used, mainly NumPy, Matplotlib, and Pandas. The Pandas package was especially useful in transferring our csv files as datasets into Google Colab and in providing easy access to manipulate the data. NumPy was utilized to faster perform trigonometric, statistical and algebraic functions while Matplotlib was used for data visualization with its ability to transfer data as plots.

Several correlation tests (Pearson’s R, Spearman’s Rho, Kendall’s Tau) were used in order to enhance our understanding of our data outcomes and the relationships between certain responses. However, Pearson’s R test was preferred because it gave extra information not only about the strength of the relationship between two variables, but also the type of relationship (in this case, linear). Pearson Product Moment Correlation aims to draw a line that best fits the data of the two variables called a regression line. Pearson’s r value measures how far away each data point is from the regression line. When Pearson’s r value is 1 or -1, this signifies a perfectly linear regression line with no data points showing any variation to the line. This would show an association between our two survey questions. When Pearson’s r value is close to 0, this would show great variation between the data points and the linear regression line therefore showing no association between the two survey questions. Xi represents the x-values of a variable or in our case, responses to one survey question. Yi represents the y-values of a variable or in our case, responses to another survey question. \bar{X} represents the mean of the x-values and \bar{Y} represents the mean of the y-values. Figure 6 shows the equation for Pearson’s correlation coefficient given these variables. A R-value higher than 0.50 is accepted to indicate a strong correlation between two variables.

Pearson:

Figure 6: Pearson’s equation, which is used to solve for the correlation coefficient.

The correlation tests mentioned above were limited to only comparing two questions at a time, a limitation which was addressed by the “analysis of variance” test (ANOVA) [4] that finds if there is a statistically significant difference among the means of multiple groups and checks if one or more factors have an impact on these differing means. Questions the ANOVA one-way test can help answer include:

  • Among several sample groups, were differences in performance due to different situations and not by chance?
  • How statistically significant is the difference between several samples?
  • What is the probability that one group performs significantly different from the other two groups? [5]
Fig. 7: Comparison of means.

ANOVA uses the F-value (defined in Figure 7) to investigate the ratio of two variances, measuring dispersion from the mean, as shown in the equation above. Larger values represent greater dispersion while lower values mean low variability. A higher F-value relative to a larger context means that group means are relatively close together. However, ANOVA cannot affirmatively describe which groups are significantly different, it rather gives us a direction on which groups to look at.

We also made use of p-values calculated for each ANOVA test in the Scipy package. P-values not only show variance among groups but also calculate how likely our data would have occurred by random chance rather than because of underlying reasons. Lower p-values, less than 0.05, mean that our results are significant and that groups did respond differently. [6]

Altogether, with correlation, ANOVA, and p-value tests, our group was able to isolate several significant statistics that demonstrated strong relationships between different variables of data.

Results/Analysis

Multicultural Responses

With an intent to find a correlation between racial groups, who may be exposed to different cultural dishes, and cultural foods, the question “How intensely can you recall the smell of (cultural food) on a scale of 1 to 7?” was asked and analyzed based on race by the ANOVA tests. Among the results, kimchi, sauerkraut, and dumplings had a p-value less than 0.05, indicating that variance was very high among racial groups and that it was statistically significant.

Fig. 8: Averages of responses to “How intensely can you recall the smell of (cultural food) on a scale of 1 to 7?” organized based on race. The highest-varying cultural foods (kimchi, sauerkraut, dumplings) among racial groups based on the ANOVA test.

In the figure above, kimchi was the most prominent in Asian/Pacific Islander races while sauerkraut was in white races. However, as shown by the data from dumplings, comparing races to how vividly they can recall the smell of cultural foods is not always straightforward and not the best indicator of culture’s relationship with smell memory. Therefore, comparing cultural groups to each cultural food through the ANOVA test may show more relevant and accurate correlations.

Fig. 9: Averages of responses to “How intensely can you recall the smell of (cultural food) on a scale of 1 to 7?” organized based on culture. The highest-varying cultural foods (kimchi, sauerkraut, dumplings) among cultural groups based on the ANOVA test.

Again, when comparing cultural groups with cultural foods, Asians were better able to recall the smell of kimchi, which further affirms our prediction that kimchi would be better recalled by aAians possibly due to higher exposure to it. Interestingly, we can now make the more nuanced observation that the smell of sauerkraut is better recalled by people who identify with the Middle East, where sauerkraut is more commonly consumed. Unfortunately, dumplings again fell short in having a strong correlation for one set cultural group as we had predicted.

Therefore, there are sporadic correlations between cultural groups, how much they are exposed to a certain food based on their culture, and their cultural foods: some cultural foods are more strongly tied to a culture than others, leaving no set conclusion.

Multisensory responses

In our multisensory survey, we focused on how the amount of exposure of a certain food and how the color and image quality of a food picture can impact how vividly a respondent can recall the smell of that food.

Impact of Exposure

In Fig. 10, Pearson’s r-value was 0.603, showing high correlation between exposure of oranges to smell recall from a regular-colored image of an orange. This trend of higher-exposure-higher-smell-recall was apparent across multiple items in the multisensory survey, implying that there is a relationship between the two factors.

Image Quality

As shown in the figure above and as demonstrated by the high Pearson’s r-value, the pixelated and regular images of peppermint show to have a high correlation, indicating that responses to the two related questions were similar. This trend was similar across other pixelated and regular objects with little variation. Therefore, image quality, as demonstrated by pixelated images, was not a significant factor in smell recall evoked by images.

Color: No color/black and white images

Figure 12: Averages of responses to “How strongly does this picture remind you of the smell of (objects)?” organized based on foods. This data shows that among different foods, colored images had better smell recall and black and white images had the greatest impact on smell recall.

With a side-by-side comparison in Figure V, we can now observe the differences of how vividly participants were able to recall a certain smell based on the type of image they were presented with. Overall, it can be observed that regular-colored images, in red, had the highest averages in smell recall, shortly followed by pixelated images. Interestingly, alternative colors results, in grey titled “alternative” always ranked the lowest. Black and White was also lower than the regular-colored images and the pixelated images. This trend being consistent no matter the object strongly implies that photos with a change in color, are likely to evoke lower levels of smell recall, ultimately showing that color does have an impact on smell recall.

Limitations

Due to limited time and resources, we were unable to get a large and diverse sample size to effectively get the most accurate results. For example, most of the surveys were deployed to predominantly U.S.-based groups so we were unable to get respondents who fully identified with a different culture without the secondary influence of mainstream American culture. We were also unable to explore more different cultural sub-categories such as East Asian, Central Asian, Eastern European, Southern European, etc due to the limited data set. Therefore, we had to generalize the categories to 7 different broader cultural categories, limiting the insights that we could draw from our research on how cultural association influences smell recall. Furthermore, the simplified and general format of Google Forms in both surveys may have influenced slight response biases as it is easier for participants to potentially figure out the results we were trying to extract through the visuals and question types – even if the questions were randomized.

Conclusions

From our work, we have concluded that in order to create an ideal visual stimulus for smell recall, image quality and coloration must be prioritized. Using suboptimal image quality or altered color, such as black-and-white images or unconventional coloration, will negatively impact a subject’s ability to recall the target odor. We conclude that food-related odor memories are triggered optimally when the visual stimulus depicts a food item with natural coloration (for example, a purple steak would be suboptimal for stimulating odor memories). We also noted a positive correlation between the subjects’ reported sociocultural group and an increased ability to recall the odors of ingredients specific to that group, as compared with the broader sample’s scores. For one example, the Middle Eastern and South Asian cultural groups performed much higher on their ability to recall the smell of cardamom, which is more widely used in many Middle Eastern and South Asian cultures. These findings are in line with our earlier hypotheses.

Among the tested factors, changes in a visual stimulus’s coloration had the greatest impact on our subjects’ ability to recall odors, followed closely by image quality. We believe that these findings may be applicable to future treatments developed for the smell-impaired that aim to trigger olfactory memories even when the physical sense of smell has been damaged.

Implications/Future Directions

There are several steps we could take to further improve our survey. One main priority is making it accessible in more languages, which would allow us to avoid bias. Releasing non-English surveys will allow us to reach study participants who might be more immersed in their respective cultures, rather than implicitly requiring knowledge of English to participate in our survey. Requiring a knowledge of English could limit our results, because most English speakers have some degree of exposure to a mainstream culture or belong predominantly to English-speaking sociocultural groups, which may introduce bias into the groups that we are able to examine. We would try and implement an international reach by adding a Google Translate extension available through Firebase to have our surveys available in various languages on the website. We would also like to perform all future deployments of our surveys on the completed website, which will allow for more efficient survey deployment and accessibility on mobile devices. Furthermore, having a wider reach can allow access to more secluded cultural groups and give us a deeper understanding of how culture plays an impact on olfactory senses.

References

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  4. Editor, M. B. (2016, May 18). Understanding Analysis of Variance (ANOVA) and the F-test. Retrieved August 12, 2021,from Minitab Blog website: https://blog.minitab.com/en/adventures-in-statistics-2/understanding-analysis-of-variance-anova-and-the-f-test
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