Multisensory integration (MSI) is the integration by the brain of environmental information acquired through more than one sense. Accurate MSI has been shown to be a key component of successful aging and to be crucial for processes underlying activities of daily living (ADLs). Problems in MSI could prevent older adults (OA) to age in place and live independently. However, there is a need to know how to assess changes in MSI in individuals. This systematic review provides an overview of tests assessing the effect of age on MSI in the healthy elderly population (aged 60 years and older). A literature search was done in Scopus. Articles from the earliest records available to January 20, 2016, were eligible for inclusion if assessing effects of aging on MSI in the healthy elderly population compared to younger adults (YA). These articles were rated for risk of bias with the Newcastle-Ottawa quality assessment. Out of 307 identified research articles, 49 articles were included for final review, describing 69 tests. The review indicated that OA maximize the use of multiple sources of information in comparison to YA (20 studies). In tasks that require more cognitive function, or when participants need to adapt rapidly to a situation, or when a dual task is added to the experiment, OA have problems selecting and integrating information properly as compared to YA (19 studies). Additionally, irrelevant or wrong information (i.e., distractors) has a greater impact on OA than on YA (21 studies). OA failing to weigh sensory information properly, has not been described in previous reviews. Anatomical changes (i.e., reduction of brain volume and differences of brain areas’ recruitment) and information processing changes (i.e., general cognitive slowing, inverse effectiveness, larger time window of integration, deficits in attentional control and increased noise at baseline) can only partly explain the differences between OA and YA regarding MSI. Since we have an interest in successful aging and early detection of MSI issues in the elderly population, the identified tests form a good starting point to develop a clinically useful toolkit to assess MSI in healthy OA.

While studies exist that compare different physiological variables with respect to their association with mental workload, it is still largely unclear which variables supply the best information about momentary workload of an individual and what is the benefit of combining them. We investigated workload using the n-back task, controlling for body movements and visual input. We recorded EEG, skin conductance, respiration, ECG, pupil size and eye blinks of 14 subjects. Various variables were extracted from these recordings and used as features in individually tuned classification models. Online classification was simulated by using the first part of the data as training set and the last part of the data for testing the models. The results indicate that EEG performs best, followed by eye related measures and peripheral physiology. Combining variables from different sensors did not significantly improve workload assessment over the best performing sensor alone. Best classification accuracy, a little over 90%, was reached for distinguishing between high and low workload on the basis of 2 min segments of EEG and eye related variables. A similar and not significantly different performance of 86% was reached using only EEG from single electrode location Pz.

Besides sensory characteristics of food, food-evoked emotion is a crucial factor in predicting consumer's food preference and therefore in developing new products. Many measures have been developed to assess food-evoked emotions. The aim of this literature review is (i) to give an exhaustive overview of measures used in current research and (ii) to categorize these methods along measurement level (physiological, behavioral, and cognitive) and emotional processing level (unconscious sensory, perceptual/early cognitive, and conscious/decision making) level. This 3 × 3 categorization may help researchers to compile a set of complementary measures (“toolbox”) for their studies. We included 101 peer-reviewed articles that evaluate consumer's emotions and were published between 1997 and 2016, providing us with 59 different measures. More than 60% of these measures are based on self-reported, subjective ratings and questionnaires (cognitive measurement level) and assess the conscious/decision-making level of emotional processing. This multitude of measures and their overrepresentation in a single category hinders the comparison of results across studies and building a complete multi-faceted picture of food-evoked emotions. We recommend (1) to use widely applied, validated measures only, (2) to refrain from using (highly correlated) measures from the same category but use measures from different categories instead, preferably covering all three emotional processing levels, and (3) to acquire and share simultaneously collected physiological, behavioral, and cognitive datasets to improve the predictive power of food choice and other models.

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