Autism spectrum disorder (ASD) is characterized by impaired social communication, often attributed to misreading of emotional cues. Why individuals with ASD misread emotions remains unclear. Given that terrestrial mammals rely on their sense of smell to read conspecific emotions, we hypothesized that misreading of emotional cues in ASD partially reflects altered social chemosignaling. We found no difference between typically developed (TD) and cognitively able adults with ASD at explicit detection and perception of social chemosignals. Nevertheless, TD and ASD participants dissociated in their responses to subliminal presentation of these same compounds: the undetected 'smell of fear' (skydiver sweat) increased physiological arousal and reduced explicit and implicit measures of trust in TD but acted opposite in ASD participants. Moreover, two different undetected synthetic putative social chemosignals increased or decreased arousal in TD but acted opposite in ASD participants. These results implicate social chemosignaling as a sensory substrate of social impairment in ASD.
A common goal in olfaction research is modeling the link between odorant structure and odor perception. Such modeling efforts require large data sets on olfactory perception, yet only a few of these are publicly and freely available. Given that individual odor perception may be informative on personal makeup and interpersonal relationships, we hypothesized that people would gladly provide olfactory perceptual estimates in the context of an odor-based social network. We developed a web-based infrastructure for such a network we called SmellSpace and distributed 10 000 scratch-and-sniff registration booklets each containing a subset of 12 out of 35 microencapsulated odorants. Within ~100 days, we obtained data from ~1000 participants who rated the odorants along 13 verbal descriptors. To verify that these estimates are comparable to lab-collected estimates we tested 26 participants in a controlled lab setting using the same odorants and descriptors. We observed remarkably high overall group correlations between lab and SmellSpace data, implying that this method provides for credible group-representations of odorants. We further estimated the usability of the data by applying to it two previously published models that used odorant structure alone to predict either odorant pleasantness or pairwise odorant perceptual similarity. We observed statistically significant predictions in both cases, thus further implying that the current data may be helpful toward future efforts of modeling olfactory perception from structure. We conclude that an odor-based social network is a potentially useful instrument for collecting extensive data on olfactory perception and here post the complete raw data set from the first ~1000 participants.
In response to the 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 step-wise manner, monitoring of COVID-19 prevalence is of importance to guide 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 in the Swedish population and were found to closely track each other (r=-0.83). Moreover, we found that there was a large difference in rated intensity between individuals with and
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