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Dataset Open Access

Estimation of olfactory sensitivity using a Bayesian adaptive method

Höchenberger, Richard; Ohla, Kathrin

This is the dataset analyzed by Höchenberger & Ohla (2019).

Abstract of the publication:

The ability to smell is crucial for most species as it enables the detection of environmental threats like smoke, it fosters social interactions, and it contributes to the sensory evaluation of food and eating behavior. The high prevalence for smell disturbances throughout the life span call for a continuous effort to improve tools for the quick and reliable assessment of the ability to smell. Odor-dispensing pens, called Sniffin' Sticks, are an established tool to test olfactory function. We tested the suitability of a Bayesian adaptive algorithm (QUEST) to estimate olfactory sensitivity using Sniffin' Sticks by comparing its results with those obtained via the established standard protocol, which relies on a staircase procedure. Thresholds were measured according to both procedures in two sessions (Test and Retest). The staircase successfully yielded threshold estimates in more cases than QUEST. Yet, Test-Retest correlations showed stronger reliability for QUEST (rho=0.70) than for staircase thresholds (rho=0.50). A strong correlation (rho=0.80) between the results of both procedures indicated good validity of QUEST. We conclude that the QUEST procedure may offer quicker convergence and reduced testing time in some cases, but fail to yield a threshold estimate in others.

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