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| 2. |
- Buchanan, E. M., et al.
(author)
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The Psychological Science Accelerator's COVID-19 rapid-response dataset
- 2023
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In: Scientific Data. - : Springer Science and Business Media LLC. - 2052-4463. ; 10:1
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Journal article (peer-reviewed)abstract
- In response to the COVID-19 pandemic, the Psychological Science Accelerator coordinated three large-scale psychological studies to examine the effects of loss-gain framing, cognitive reappraisals, and autonomy framing manipulations on behavioral intentions and affective measures. The data collected (April to October 2020) included specific measures for each experimental study, a general questionnaire examining health prevention behaviors and COVID-19 experience, geographical and cultural context characterization, and demographic information for each participant. Each participant started the study with the same general questions and then was randomized to complete either one longer experiment or two shorter experiments. Data were provided by 73,223 participants with varying completion rates. Participants completed the survey from 111 geopolitical regions in 44 unique languages/dialects. The anonymized dataset described here is provided in both raw and processed formats to facilitate re-use and further analyses. The dataset offers secondary analytic opportunities to explore coping, framing, and self-determination across a diverse, global sample obtained at the onset of the COVID-19 pandemic, which can be merged with other time-sampled or geographic data.
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| 3. |
- Jones, Benedict C, et al.
(author)
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To which world regions does the valence-dominance model of social perception apply?
- 2021
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In: Nature Human Behaviour. - : Springer Science and Business Media LLC. - 2397-3374. ; 5:1, s. 159-169
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Journal article (peer-reviewed)abstract
- Over the past 10 years, Oosterhof and Todorov's valence-dominance model has emerged as the most prominent account of how people evaluate faces on social dimensions. In this model, two dimensions (valence and dominance) underpin social judgements of faces. Because this model has primarily been developed and tested in Western regions, it is unclear whether these findings apply to other regions. We addressed this question by replicating Oosterhof and Todorov's methodology across 11 world regions, 41 countries and 11,570 participants. When we used Oosterhof and Todorov's original analysis strategy, the valence-dominance model generalized across regions. When we used an alternative methodology to allow for correlated dimensions, we observed much less generalization. Collectively, these results suggest that, while the valence-dominance model generalizes very well across regions when dimensions are forced to be orthogonal, regional differences are revealed when we use different extraction methods and correlate and rotate the dimension reduction solution. PROTOCOL REGISTRATION: The stage 1 protocol for this Registered Report was accepted in principle on 5 November 2018. The protocol, as accepted by the journal, can be found at https://doi.org/10.6084/m9.figshare.7611443.v1 .
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| 4. |
- Cedres, Nira, et al.
(author)
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Subjective Impairments in Olfaction and Cognition Predict Dissociated Behavioral Outcomes
- 2022
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In: The journals of gerontology. Series B, Psychological sciences and social sciences. - : Oxford University Press (OUP). - 1079-5014 .- 1758-5368. ; 78:1, s. 1-9
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Journal article (peer-reviewed)abstract
- Background: Self-rated subjective cognitive decline (SCD) and subjective olfactory impairment (SOI) are associated with objective cognitive decline and dementia. However, their relationship and co-occurrence is unknown. We aimed to (a) describe the occurrence of SOI, SCD and their overlap in the general population; (b) compare SOI and SCD in terms of longitudinal associations with corresponding objective olfactory and cognitive measures; and (c) describe how SOI and SCD may lead to distinct sensory and cognitive outcomes.Methods: Cognitively unimpaired individuals from the third wave of the Swedish population-based Betula study (n = 784, aged 35–90 years; 51% females) were split into self-rated SOI, SCD, overlapping SCD + SOI, and controls. Between-subject and within-subject repeated-measures MANCOVA were used to compare the groups regarding odor identification, cognition, age, sex, and education. Spearman correlation was used to assess the different patterns of association between olfaction and cognition across groups.Results: SOI was present in 21.1%, whereas SCD was present in 9.9% of participants. According to a chi-square analysis, the SCD + SOI overlap (2.7%) is on a level that could be expected if the phenomena were independent. Odor identification in SOI showed decline at the 10-year follow-up (n = 284) and was positively associated with cognition. The SOI and SCD groups showed distinct cognitive-olfactory profiles at follow-up.Conclusions: SOI occur independently of SCD in the population, and these risk factors are associated with different cognitive and olfactory outcomes. The biological causes underlying SOI and SCD, as well as the risk for future cognitive impairment, need further investigation.
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| 5. |
- Gerkin, Richard C., et al.
(author)
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Recent Smell Loss Is the Best Predictor of COVID-19 Among Individuals With Recent Respiratory Symptoms
- 2021
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In: Chemical Senses. - : Oxford University Press (OUP). - 0379-864X .- 1464-3553. ; 46
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Journal article (peer-reviewed)abstract
- In a preregistered, cross-sectional study, we investigated whether olfactory loss is a reliable predictor of COVID-19 using a crowdsourced questionnaire in 23 languages to assess symptoms in individuals self-reporting recent respiratory illness. We quantified changes in chemosensory abilities during the course of the respiratory illness using 0–100 visual analog scales (VAS) for participants reporting a positive (C19+; n = 4148) or negative (C19−; n = 546) COVID-19 laboratory test outcome. Logistic regression models identified univariate and multivariate predictors of COVID-19 status and post-COVID-19 olfactory recovery. Both C19+ and C19− groups exhibited smell loss, but it was significantly larger in C19+ participants (mean ± SD, C19+: −82.5 ± 27.2 points; C19−: −59.8 ± 37.7). Smell loss during illness was the best predictor of COVID-19 in both univariate and multivariate models (ROC AUC = 0.72). Additional variables provide negligible model improvement. VAS ratings of smell loss were more predictive than binary chemosensory yes/no-questions or other cardinal symptoms (e.g., fever). Olfactory recovery within 40 days of respiratory symptom onset was reported for ~50% of participants and was best predicted by time since respiratory symptom onset. We find that quantified smell loss is the best predictor of COVID-19 amongst those with symptoms of respiratory illness. To aid clinicians and contact tracers in identifying individuals with a high likelihood of having COVID-19, we propose a novel 0–10 scale to screen for recent olfactory loss, the ODoR-19. We find that numeric ratings ≤2 indicate high odds of symptomatic COVID-19 (4 < OR < 10). Once independently validated, this tool could be deployed when viral lab tests are impractical or unavailable.
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| 6. |
- Menelaou, Georgios, et al.
(author)
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Hippocampal subfield volumes and olfactory performance : Emerging longitudinal associations over a 5-year interval
- 2022
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In: Neuropsychologia. - : Elsevier. - 0028-3932 .- 1873-3514. ; 176
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Journal article (peer-reviewed)abstract
- Olfaction, the sense of smell, provides important behavioral functions in many species. The hippocampus (HC) is critical for identifying odors, and hippocampal volume is associated with odor identification ability. Impaired odor identification is often reported in old age and might provide an early marker of cognitive decline and dementia. Here, we explored cross-sectional (n = 225) and longitudinal (n = 118) associations between odor identification ability and hippocampal subfield volumes in a sample of middle-aged and older persons (25-80 years). In older participants, longitudinally decreasing volumes of the hippocampal tail, subiculum, CA4 and the dentate gyrus correlated with changes in odor identification. None of these correlations were observed in younger participants, but there was a significant correlation between longitudinal volume reduction in the tail subfield of the hippocampus and odor identification change across all participants. There were no significant cross-sectional associations between hippocampal subfields and odor identification. These exploratory results provide new information regarding precisely where and when declining HC subfield volumes might be associated with odor identification.
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| 7. |
- Hedner, Margareta, et al.
(author)
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Age-Related Olfactory Decline is Associated with the BDNF Val66met Polymorphism : Evidence from a Population-Based Study
- 2010
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In: Frontiers in Aging Neuroscience. - : Frontiers Media SA. - 1663-4365. ; 2:7, s. 24-
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Journal article (peer-reviewed)abstract
- The present study investigates the effect of the brain-derived neurotrophic factor (BDNF) val66met polymorphism on change in olfactory function in a large scale, longitudinal population-based sample (n = 836). The subjects were tested on a 13 item force-choice odor identification test on two test occasions over a 5-year-interval. Sex, education, health-related factors, and semantic ability were controlled for in the statistical analyses. Results showed an interaction effect of age and BDNF val66met on olfactory change, such that the magnitude of olfactory decline in the older age cohort (70–90years old at baseline) was larger for the val homozygote carriers than for the met carriers. The older met carriers did not display larger age-related decline in olfactory function compared to the younger group. The BDNF val66met polymorphism did not affect the rate of decline in the younger age cohort (45–65years). The findings are discussed in the light of the proposed roles of BDNF in neural development and maintenance.
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| 8. |
- Hörberg, Thomas, 1979-, et al.
(author)
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A parosmia severity index based on word-classification predicts olfactory abilities and impairment
- 2023
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In: European Archives of Oto-Rhino-Laryngology. - : Springer Nature. - 0937-4477 .- 1434-4726. ; 280:8, s. 3695-3706
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Journal article (peer-reviewed)abstract
- Parosmia is an olfactory disorder that involves distortions of specific odors that may co-occur with anosmia, loss of smell of other odors. Little is known about which odors frequently trigger parosmia, and measures of parosmia severity are lacking. Here, we present an approach to understand and diagnose parosmia that is based on semantic properties (e.g., valence) of words describing odor sources (“fish”, “coffee”, etc.). Using a data-driven method based on natural language data, we identified 38 odor descriptors. Descriptors were evenly dispersed across an olfactory-semantic space, which was based on key odor dimensions. Parosmia patients (n = 48) classified the corresponding odors in terms of whether they trigger parosmic or anosmic sensations. We investigated whether these classifications are related to semantic properties of the descriptors. Parosmic sensations were most often reported for words describing unpleasant odors of inedibles that are highly associated to olfaction (e.g., “excrement”). Based on PCA modeling, we derived the Parosmia Severity Index—a measure of parosmia severity that can be determined solely from our non-olfactory behavioral task. This index predicts olfactory-perceptual abilities, self-reported olfactory impairment, and depression. We thus provide a novel approach for investigating parosmia and establishing its severity that does not require odor exposure. Our work may enhance our understanding of how parosmia changes over time and how it is expressed differently across individuals.
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| 9. |
- Hörberg, Thomas, 1979-, et al.
(author)
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The semantic organization of the English odor vocabulary
- 2019
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Conference paper (peer-reviewed)abstract
- Most people find it difficult to name familiar odors (e.g. Herz & Engen, 1996; Jönsson & Stevenson, 2014). Most languages, including English, lack a vocabulary that is devoted to describing odor qualities (as compared to, e.g., a color term vocabulary). Across languages, olfaction has been shown to be the sense with the poorest linguistic codability (i.e. naming consistency, see e.g. Majid et al., 2018). Instead of using devoted, abstract terms for describing odors, speakers of many languages often resort to source-based (e.g. ‘citrusy’) odor descriptions, and relatively little is still known about the vocabulary that is used to describe odors. Attempts to establish “primary odor descriptors” have been unsuccessful in describing wider varieties of odor qualities, and no standard has been agreed upon (e.g. Kaeppler & Mueller, 2013).To date, research on odor vocabulary has rarely been done from a data-driven, empirical perspective.We present a study on the semantic organization of the odor vocabulary, based on the distribution of words in olfactory and gustatory contexts, using a three-billion-word corpus of written English. Using a data-driven, computational linguistic approach recently developed in our lab (Iatropoulos et al., 2018), we quantify terms with respect to degree of olfactory-semantic content they convey. We then derive the semantic organization of the top 200 olfactory-related terms, using a distributional-semantic word vector model, which represents semantic distances as vector distances in a multidimensional space. In order to capture olfactory and gustatory word senses, the model is trained on olfactory and gustatory contexts, using the word2vec neural network implementation (Mikolov, Chen, Corrado, & Dean, 2013). Based on the semantic distances, we then use dimensionality reduction and clustering techniques (i.e., PCA and hierarchical clustering) to derive a 3-dimensional, corpus-based semantic space of the descriptors, and six principal descriptor clusters.Using descriptor distances based on the Draveneiks odor-term rating data set (Dravnieks, 1992), we also derive a semantic space with six specific clusters for the Draveneiks terms. The organization and clustering of our corpus-based semantic space matches with the ratings-based semantic space, thereby showing the viability of our corpus-based approach. Based on our analyses of the corpus-based data, we finally propose a novel domain-general odor term taxonomy (i.e., a domain-general odor wheel) that captures the dimensions and clusters identified in our analyses.Dravnieks, A. (1992). Atlas of odor character profiles. Philadelphia, PA, USA: American Society for Testing and Materials.Herz, R. S., & Engen, T. (1996). Odor memory: Review and analysis. Psychonomic Bulletin & Review, 3(3), 300–313.Iatropoulos, G., Herman, P., Lansner, A., Karlgren, J., Larsson, M., & Olofsson, J. K. (2018). The language of smell: Connecting linguistic and psychophysical properties of odor descriptors. Cognition, 178, 37–49.Jönsson, F. U., & Stevenson, R. J. (2014). Odor Knowledge, Odor Naming, and the “Tip-of-the-Nose” Experience. I B. L. Schwartz & A. S. Brown (Red.), Tip-of-the-Tongue States and Related Phenomena (s. 305–326).Kaeppler, K., & Mueller, F. (2013). Odor Classification: A Review of Factors Influencing Perception-Based Odor Arrangements. Chemical Senses, 38(3), 189–209.Majid, A., Roberts, S. G., Cilissen, L., Emmorey, K., Nicodemus, B., O’Grady, L., … Levinson, S. C. (2018). Differential coding of perception in the world’s languages. Proceedings of the National Academy of Sciences, 115(45), 11369–11376.Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. arXiv:1301.3781 [cs].
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| 10. |
- Hörberg, Thomas, 1979-, et al.
(author)
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The Semantic Organization of the English Odor Vocabulary
- 2022
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In: Cognitive science. - : Wiley. - 0364-0213 .- 1551-6709. ; 46:11
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Journal article (peer-reviewed)abstract
- The vocabulary for describing odors in English natural language is not well understood, as prior studies of odor descriptions have often relied on preselected descriptors and odor ratings. Here, we present a data-driven approach that automatically identifies English odor descriptors based on their degree of olfactory association, and derive their semantic organization from their distributions in natural texts, using a distributional-semantic language model. We identify 243 descriptors that are much more strongly associated with olfaction than English words in general. We then derive the semantic organization of these olfactory descriptors, and find that it is captured by four clusters that we name Offensive, Malodorous, Fragrant, and Edible. The semantic space derived from our model primarily differentiates descriptors in terms of pleasantness and edibility along which our four clusters are positioned, and is similar to a space derived from perceptual data. The semantic organization of odor vocabulary can thus be mapped using natural language data (e.g., online text), without the limitations of odor-perceptual data and preselected descriptors. Our method may thus facilitate research on olfaction, a sensory system known to often elude verbal description.
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