Preprint Open Access

Estimation of olfactory sensitivity using a Bayesian adaptive method

Höchenberger, Richard; Ohla, Kathrin


Citation Style Language JSON Export

{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.2840358", 
  "author": [
    {
      "family": "H\u00f6chenberger, Richard"
    }, 
    {
      "family": "Ohla, Kathrin"
    }
  ], 
  "issued": {
    "date-parts": [
      [
        2019, 
        5, 
        15
      ]
    ]
  }, 
  "abstract": "<p>The ability to smell is crucial for most species as it enables the detection of environmental&nbsp;threats like smoke, fosters social interactions, and contributes to the sensory evaluation of food&nbsp;and eating behavior. The high prevalence of smell disturbances throughout the life span calls&nbsp;for a continuous effort to improve tools for quick and reliable assessment of olfactory function.&nbsp;Odor-dispensing pens, called Sniffin&rsquo; Sticks, are an established method to deliver olfactory stimuli&nbsp;during diagnostic evaluation. We tested the suitability of a Bayesian adaptive algorithm (QUEST) to&nbsp;estimate olfactory sensitivity using Sniffin&rsquo; Sticks by comparing QUEST sensitivity thresholds with&nbsp;those obtained using a procedure based on an established standard staircase protocol. Thresholds&nbsp;were measured twice with both procedures in two sessions (Test and Retest). Overall, both procedures&nbsp;exhibited considerable overlap with QUEST displaying slightly higher test-retest correlations, less&nbsp;variability between measurements, and reduced testing duration. Notably, participants were more&nbsp;frequently presented with the highest concentration during the QUEST which may foster adaptation&nbsp; and habituation effects. We conclude that further research is required to better understand and&nbsp;optimize the procedure for&nbsp;assessment of olfactory performance.</p>", 
  "title": "Estimation of olfactory sensitivity using a Bayesian adaptive method", 
  "type": "article", 
  "id": "2840358"
}
245
252
views
downloads
All versions This version
Views 245107
Downloads 25296
Data volume 477.2 MB211.2 MB
Unique views 22599
Unique downloads 20175

Share

Cite as