Collaborative taste sensitivity estimation[Bearbeiten]
The sense of taste is the key to distinction of potentially nutritious or harmful food constituents and thereby governing the the acceptance or rejection of food. Changes in taste perception, as might occur during aging or as the result of diseases, are thought to foster deviant eating behavior (see e.g. Hardikar et al., 2016). While decreased taste sensitivity may lead to malnutrition and associated weight loss, decreased hedonic experience or valuation has been suggested to contribute to excess food intake and associated weight gain. Age-related decline of taste function goes commonly unnoticed or is mistaken for loss of smell (Bromley, 2000), partly because taste experiences have little to no external or social reference – unlike difficulties in reading the daily newspaper or following a conversation, which are typical indicators for impairment of sight and hearing, respectively. Instead, impaired taste is often attributed externally, e.g. to poor food quality. Reduced taste function may reduce social participation (cooking or dining with friends) and limit the pleasure associated with eating itself (anhedonic effect). Together, these findings show that measurement of taste function or taste sensitivity (i.e., the ability to taste) can be of high diagnostic value.
Sensory sensitivity can be measured by preparing a set of pre-defined stimuli of different intensities, e.g. solutions with different amounts of sugar or sounds with different volume, and presenting them to participants following to a particular algorithm. The participant would then respond whether they perceived the stimulus or not, and the response determines which stimulus is to be presented next. The goal is to find the stimulus intensity the participant can perceive only a certain number of times (e.g., in 50% or 75% of all stimulations), owing to the stochastic properties of stimulus perception. While very efficient methods for measuring sensory sensitivity exist in the non-chemical senses (vision, audition, and touch), their application to taste is typically limited by the fact that relatively long breaks (inter-stimulus intervals, ISIs) have to be inserted between subsequent stimulus presentations. That is, to avoid adaptation, ISIs of 20 to 30 s are typically used. Therefore, measuring taste sensitivity requires long-lasting testing session, posing strain on participants’ memory and cognitive resources, and thereby making these methods unsuitable for the application in the elderly population or for bed-side testing in hospitals.
For this reason, we adapted QUEST (Watson & Pelli, 1983), an algorithm for sensitivity estimation commonly used in vision science, for the application in taste. We could show that it allows for a quick (6.5 min on average) and reliable estimation of taste sensitivity in participants aged 18 to 65 years (Höchenberger et al., 2017). The procedure has been presented at international conferences already and was highly acclaimed.
However, to be of larger practical use for clinical diagnostics, more data needs to be collected to build a “norm database” based on measurements of healthy participants, i.e. a large set of reference data. These data can later be used to determine whether an individual’s tasting abilities are within “normal” range, or whether their sense of taste is impaired, and to what degree.
During the funding period, I will implement a method that will enable researchers to collaboratively collect and share data, simplifying the process of creating a such norm database.
By allowing researchers from different institutions to work collaboratively very easily, I am confident my project will help spread the ideas behind and the benefits of an Open Science approach in this particular research area, and potentially even beyond.
Bromley, SM (2000): Smell and taste disorders: a primary care approach. American Academy of Family Physicians, 61(2):427-436.
Hardikar, S, Höchenberger, R, Villringer, A, & Ohla, K (2016): Higher sensitivity to sweet and salty taste in obese compared to lean individuals. Appetite, 111, 158–168. doi: 10.1016/j.appet.2016.12.017
Höchenberger, R, & Ohla, K (2017): Rapid Estimation of Gustatory Sensitivity Thresholds with SIAM and QUEST. Frontiers in Psychology, 8:981. doi: 10.3389/fpsyg.2017.00981
Watson, AB, & Pelli, D (1983): QUEST: A Bayesian adaptive psychometric method. Perception & Psychophysics, 33(2):113–120. doi: 10.3758/BF03202828
Zwischenbericht (15. Februar 2018)[Bearbeiten]
- Name: Richard Höchenberger
- Institution: Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Jülich
- Kontakt: email@example.com