Predicting odor perceptual similarity from odor structure

To understand the brain mechanisms of olfaction we must understand the rules that govern the link between odorant structure and odorant perception. Natural odors are in fact mixtures made of many molecules, and there is currently no method to look at the molecular structure of such odorant-mixtures and predict their smell. In three separate experiments, we asked 139 subjects to rate the pairwise perceptual similarity of 64 odorant-mixtures ranging in size from 4 to 43 mono-molecular components. We then tested alternative models to link odorant-mixture structure to odorant-mixture perceptual similarity. Whereas a model that considered each mono-molecular component of a mixture separately provided a poor prediction of mixture similarity, a model that represented the mixture as a single structural vector provided consistent correlations between predicted and actual perceptual similarity (r≥0.49, p<0.001). An optimized version of this model yielded a correlation of r = 0.85 (p<0.001) between predicted and actual mixture similarity. In other words, we developed an algorithm that can look at the molecular structure of two novel odorant-mixtures, and predict their ensuing perceptual similarity. That this goal was attained using a model that considers the mixtures as a single vector is consistent with a synthetic rather than analytical brain processing mechanism in olfaction

Relationship between odor intensity estimates and COVID-19 prevalence prediction in a Swedish population

International audienceIn response to the coronavirus disease 2019 (COVID-19) pandemic, countries have implemented various strategies to reduce and slow the spread of the disease in the general population. For countries that have implemented restrictions on its population in a stepwise manner, monitoring of COVID-19 prevalence is of importance to guide the decision on when to impose new, or when to abolish old, restrictions. We are here determining whether measures of odor intensity in a large sample can serve as one such measure. Online measures of how intense common household odors are perceived and symptoms of COVID-19 were collected from 2440 Swedes. Average odor intensity ratings were then compared to predicted COVID-19 population prevalence over time i

Masking experiment Data for "A cross modal performance-based measure of sensory stimuli intricacy"

<p>This archive contains matlab data files for the results of 12 test subjects. Each of the test subjects participated in the masking experiment described in our paper: "A cross modal performance-based measure of sensory stimuli intricacy".</p><p>In each test subject the data structure contains the following variables:</p><p>"FirstLetter" is the list of masked letters which the test subjects were asked to identify </p><p>"answer" is the letter the subject indicated that they saw.</p><p>"distractorindex" is the index of the Brodatz image used to mask the letter in the experiment.</p><p><br></p

A cross modal performance-based measure of sensory stimuli intricacy

<p>This matlab data file contains the raw data for our paper “A cross modal performance-based measure of sensory stimuli intricacy”. Its basic analysis is done by the accompanying script.</p><p>Analysis in the paper was carried out on this data.</p

OdorVarianceProperty

<p>This script analyzes the data we upload in the accompanying file. It loads, normalizes and plots a histogram of correlations as explained in the paper “A cross modal performance-based measure of sensory stimuli intricacy”</p><p>In order to run this script the linstats 2006b library needs to be installed.<br></p>https://www.mathworks.com/matlabcentral/fileexchange/13493-linstats-2006b/content/linstats/util/nanzscore.m<br><p><br></p

Data Set B Subjects Responses

This data file contained the raw data for data set B in our paper “A cross modal performance-based measure of sensory stimuli intricacy”.  These answers are averaged and then analyzed with the rest of the data sets in the script we supplied.<br><br>All subjects rated the stimuli twice, except for subjects m3,m21,f14,f21 which rated only once<br

A Cross Modal Performance-Based Measure of Sensory Stimuli Intricacy.

We define a new measure of sensory stimuli which has the following properties: It is cross modal, performance based, robust, and well defined. We interpret this measure as the intricacy or complexity of the stimuli, yet its validity is independent of its interpretation. We tested the validity and cross modality of our measure using three olfactory and one visual experiment. In order to test the link between our measure and cognitive performance we also conducted an additional visual experiment. We found that our measure is correlated with the results of the well-established Rapid Serial Visual Presentation masking experiment. Specifically, ranking stimuli according to our measure was correlated at r = 0.75 (p < 0.002) with masking effectiveness. Thus, our novel measure of sensory stimuli provides a new quantitative tool for the study of sensory processing