Psychophysical Curves for Tasting Based on A Dissociation Model

Abstract

An experiment on taste sensory analysis of an artificially arranged compound of salty and sour flavors was conducted with the goal of evaluating sensory performance to a mixture of flavors akin to natural food, but that allows for keeping a higher experimental control than natural foods do. Participants performed a detection task in which the concentration of salt was manipulated between-subjects (from 0.07 to 0.75%). A procedure based on a dissociative model that allows for obtaining independent measures of sensitivity and criterion performance was used. In general, sensitivity index increased with the increase in signal salience, with actual performance approaching a Weibull distribution. A slight but reliable lenient response bias was found regardless of the salt concentration. The relevance of using independent measures of sensitivity and bias in sensory evaluation of complex compounds, and its relevance for sensory analysis of food is discussed. PRACTICAL APPLICATIONSIn tasting situations, perception seems to be easily influenced by cognitive factors and different response biases might appear, modifying the response criterion location. Difficulty of discrimination may also increases by qualitative changes that occur in taste mixtures. Signal Detection Theory allows the measurement of both: sensory and nonsensory components. Taking apart cognitive and sensory components is central to uncovering small differences in sensory qualities and developing accurate models of perception. This approach is of special interest to study the psychological processes involved in tasting situations, including the evaluation of training programs conducted to improve tasters' discriminative abilities. In addition, function modelization has important implications for the area of perceptual research since psychophysical functions for binary compounds as it allows conducting predictions about interaction effects between studied components in complex stimuli.

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“…3c reflects the presence of distinct cell types, we employed a modeling approach. Non-linear modeling has been applied to characterize the dynamics of other processes, such as learning curves 41 , psychophysics 42 , and biological processes 43 . Importantly, modeling approaches have also been used to characterize the behavioral relevance of distinct neuronal cell types 44 .…”