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Available from: Charles C Wykoff, Feb 15, 2014
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    ABSTRACT: The aim of this study was to review research conducted in 2003–2006 in the EU-15 countries on how consumers perceive, understand, like and use nutrition information on food labels. Based on a search of databases on academic publications, Google-based search, and enquiries directed to a range of food retailers, food companies, consumer associations and government agencies, a total of 58 studies were identified. These studies were summarised using a standard format guided by a model of consumer information processing, and these summaries were subsequently processed using the MAXqda software in order to identify key findings and common themes across the studies. The studies show widespread consumer interest in nutrition information on food packages, though this interest varies across situations and products. Consumers like the idea of simplified front of pack information but differ in their liking for the various formats. Differences can be related to conflicting preferences for ease of use, being fully informed and not being pressurised into behaving in a particular way. Most consumers understand the most common signposting formats in the sense that they themselves believe that they understand them and they can replay key information presented to them in an experimental situation. There is, however, virtually no insight into how labelling information is, or will be, used in a real-world shopping situation, and how it will affect consumers’ dietary patterns. Results are largely in line with an earlier review by Cowburn and Stockley (Public Health Nutr 8:21–28, 2005), covering research up to 2002, but provide new insights into consumer liking and understanding of simplified front of pack signposting formats. There is an urgent need for more research studying consumer use of nutritional information on food labels in a real-world setting.
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    ABSTRACT: Using an eye-tracking methodology, we evaluated food nutrition labels' ability to support rapid and accurate visual search for nutrition information. Participants (5 practiced label readers and 5 nonreaders) viewed 180 trials of nutrition labels on a computer, finding answers to questions (e.g., serving size). Label manipulations included several alternative line arrangements, location of the question target item, and label size. Dependent measures included search time and number of fixations prior to visually capturing the target, as well as the accuracy and duration of the capturing fixation. Practiced label readers acquired the target more quickly and accurately than did less-practiced readers. Targets near the denser center of the label required 33% more time and were harder to find than targets at the top or bottom of the label. Thinner alignment lines were more influential than thicker anchoring lines on visual search time. Overall, the current nutrition label supported accurate and rapid search for desired information. Potential applications of the present methodology include the evaluation of warning labels and other static visual displays.
    Human Factors The Journal of the Human Factors and Ergonomics Society 10/1999; 41(3):425-37. DOI:10.1518/001872099779611021 · 1.29 Impact Factor
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    ABSTRACT: The E-Z Reader model (Reichle et al. 1998; 1999) provides a theoretical framework for understanding how word identification, visual processing, attention, and oculomotor control jointly determine when and where the eyes move during reading. In this article, we first review what is known about eye movements during reading. Then we provide an updated version of the model (E-Z Reader 7) and describe how it accounts for basic findings about eye movement control in reading. We then review several alternative models of eye movement control in reading, discussing both their core assumptions and their theoretical scope. On the basis of this discussion, we conclude that E-Z Reader provides the most comprehensive account of eye movement control during reading. Finally, we provide a brief overview of what is known about the neural systems that support the various components of reading, and suggest how the cognitive constructs of our model might map onto this neural architecture.
    Behavioral and Brain Sciences 09/2003; 26(4):445-76; discussion 477-526. DOI:10.1017/S0140525X03000104 · 14.96 Impact Factor
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