Abstract We review research on eye movements in decision making and show that decision makers are subject to several visual biases such as the size, salience, position, emotional valence, predictability, and number of information elements. These biases lead decision makers to allocate their attention in ways that are arbitrary to their goals and sometimes bias their choices. We show that while some visual biases can be minimized, others are unavoidable. Consequently, it is impossible to present information in a completely neutral way. Any presentation format will bias decision makers to attend or ignore different information and thereby influence their choices. Attention, eye movements, bottom up control, downstream effects, consumer policy During the last few decades, agricultural economics has increasingly dealt with topics related to food and consumer decision making, analyzing not only the effect of prices and income on consumer decisions on what and how much to buy, but also looking at factors related to information and quality attributes (Unnevehr et al. 2010). In analyzing the effects of information, a major issue is the explanation of which information, among the multitude of information available, actually has an effect on the decision being made. Traditionally, this question has been addressed by acknowledging that the acquisition of information many times is associated with costs (Stigler 1961). However, this would imply that all information to which a consumer is exposed at the time of decision making, and which hence is available without cost, will be used. It is well known that decision makers ignore a large part of the information that is available to them at the time of decision making. Inattention to attribute information on alternatives in a decision-making situation, also termed attribute non-attendance (Scarpa et al. 2013), leads to challenges in estimating utilities in choice experiments, and a growing body of literature in agricultural economics has begun to address this challenge (Van Loo, Grebitus, et al. 2018; Van Loo, Nayga, et al. 2018). However, there is also a more fundamental theoretical issue: how can ignoring available information in decision making be compatible with the assumption of rational choice? One way in which this issue has been integrated into economic theory is by acknowledging—in accordance with insights in psychology—that human attention is a scarce resource. Decision makers therefore need to be selective about which information to attend, and that this selection process is based on relevance of the information. According to this view, when attention is limited, it is rational to be inattentive (Sims 2003). Studies have demonstrated that decision makers often make good use of their limited attention, ignoring information when it is rational to do so, that is, when the relevance of the information to the decision maker is low (Dean and Neligh 2017). In this article, we show that while decision makers can be inattentive in a way that is compatible with the assumption of rational choice, there are also factors affecting what is attended and what is not that cannot be linked to considerations of personal relevance in preference formation. We review findings from the intersection of vision science and decision making and show that attention is influenced by environmental factors that are exogenous, and often unrelated to the goals of the decision maker. These visual biases include, for instance, the position or ordering of information, the size and color of information, or the predictability of where information will appear. Following on from that, we review studies demonstrating that the way decision makers allocate their attention can influence their choices, such that environmental factors unrelated to the decision-maker’s goals can have an influence on the decision made. Our line of argumentation corresponds to research in information systems showing that how information is presented has pervasive effects on judgment and decision making (Kelton, Pennington, and Tuttle 2010). Foreshadowing our conclusions, we will argue that there is no such thing as a neutral information presentation format. Irrespective of how information is presented to decision makers, it will activate one or more unavoidable visual biases. This then influences how attention is allocated, which in turn is likely to influence choices. This insight has implications for all policy areas that are based on the goal of enabling the consumer to make an “informed choice”, which assumes that if consumers are given all relevant information, they will be able to make the choice that is best for them. However, when the way in which this relevant information is presented influences the choices made, then the concept of “informed choice” loses some of its appeal. As a result, policy makers have more recently begun designing the decision environment, typically referred to as choice architecture or nudging (Thaler and Sunstein 2008; Costa-Font et al. 2014), as a supplement to giving information, the argument being that if we cannot avoid influencing decision makers, we might as well try helping them to make better decisions. Needless to say, such an approach raises some ethical issues (see Sunstein 2014 for a defence and Bovens 2009, for a critical treatment). In the remainder of this article, we first describe the basics of attention and how to measure it with eye-tracking technology. Second, we review findings on the environmental factors that bias the allocation of attention. In the next section, we review findings on how attention can influence choices. Finally, we conclude by discussing the implications of the existence of visual biases in decision making for economic and social policy. Measuring Attention We can use many methods to infer how decision makers allocate their attention. Some methods are indirect and infer attention through behaviors, while others are more direct such as measures of eye movements or neurological processes (Gabaix 2017). In this article, we focus on direct measures of visual attention by means of eye tracking and will refer to this simply as attention. However, it is worth noting that this term is a slight simplification of the underlying psychological processes. Although a close connection exists between eye movements and attention, the two are not always identical. We therefore review the basics of eye-movement research. These basics can be summarized in four main points. First, it is important to understand that although we experience our surroundings as having high visual acuity, it is actually only a limited part of our visual field in which we can see things clearly. This part of the visual field is called fovea, and one of the purposes of eye movements is to reposition the fovea whenever we wish to inspect an object or location of interest (Gregory 1997). Generally, there is a close coupling of attention and the location of the fovea, so that the two move in synchrony (Kowler et al. 1995). Attention can be voluntarily decoupled from the fovea by maintaining gaze in one location while concentrating on visual stimuli in another location (Posner, Snyder, and Davidson 1980). This decoupling is rare, so most applied eye tracking studies do not concern themselves with this (Orquin and Holmqvist 2017). One exception is the work from Wästlund and colleagues, who found that peripheral vision (the visual field outside the fovea) does indeed play a role in guiding consumer attention (Wästlund, Shams, and Otterbring 2017). Second, when visually inspecting an object, we position our eyes so that the object is in the fovea of both eyes and retain this position for about 200 to 400 msec. After inspecting the object, we reposition our eyes on the next object of interest and retain this position until the object has been processed. These two phases are known as fixations, that is, periods of relative stable fovea positions, and saccades, that is, periods in which the eyes move to a new position. During saccades, there is little if any visual processing of the environment (Duchowski 2007). Third, these properties of the visual system lend themselves to eye-tracking analysis. Since we are practically blind during saccades and only have visual acuity in the fovea during fixations, any technique for measuring the position of fixations is useful for understanding what information is processed and what is ignored. Eye-tracking methodology varies slightly between different types of hardware, but generally the position of the eyes is computed using the reflex of infrared light shone into the eyes (Holmqvist et al. 2011). Fourth, eye tracking provides data about the position of one or both eyes over time. These data can answer three types of questions: what objects are fixated, how often are these objects fixated, and finally, when are they fixated? The first question is answered by producing an eye-movement metric commonly known as fixation likelihood, that is, the probability of a participant fixating on an object. The second question is answered with the metric known as fixation count, that is, how many times did a participant fixate on the object. The third question is answered with the metric known as fixation order or time to fixation. Biased Attention Depending on environmental and personal factors, decision makers may ignore smaller or larger proportions of the information available in a decision situation. For instance, in a modern supermarket, consumers are regularly faced with up to 400 products within a single product category (Perkovic and Orquin 2018). In such situations, it seems reasonable to ignore a large proportion of products, and in fact, it has been suggested that consumers ignore 90% of available products (Jansson 2016). Similarly, when products are cluttered with information, consumers tend to ignore most of the information and focus on the few dimensions relevant to their decision, such as brand and price. In this section we describe six mechanisms that influence the allocation of attention. These mechanisms are visual characteristics of the decision environment, namely visual salience, surface size, position, set size, random location and emotional stimuli. In vision research, these factors are often referred to as bottom-up factors and are distinguished from top-down factors. The former refers to the influence of the environment in driving our attention, whereas the latter refers to the influence of goals, task instructions, and motives in driving attention (Corbetta and Shulman 2002). For instance, bottom-up control occurs when our eyes are caught by a flickering banner advertisement or by bright sales tags. Top-down control can in principle result in attention to the same objects, for example, red sales tags, but for different reasons. If, for instance, our goal is to purchase products on sale, we typically pay more attention to sales tags increasing attention to objects that fit this general description. In the continuation of this section, we describe in more detail the previously mentioned mechanisms related to bottom-up control. As will become clear, these mechanisms are completely independent of the goals of the decision maker, and by changing these visual characteristics, marketers, policy makers, or choice architects can influence our allocation of attention. More importantly, some of these mechanisms lead to unavoidable visual biases. Visual Salience The term visual salience refers to the conspicuity of a visual element relative to the visual surroundings in which it appears. Several models of salience have been proposed, based on different aspects of visual conspicuity such as contrast, color, edge orientation, or motion (Itti and Koch 2001). A salience model takes as input any visual scene and produces a topographical map of the most conspicuous locations, that is, those locations that are brighter, have sharper edges, or different colors than their surroundings. Salience maps have been shown to predict attention in various tasks, such as scene viewing (Parkhurst, Law, and Niebur 2002; Foulsham and Underwood 2008), visual search (Itti and Koch 2000; Rutishauser and Koch 2007), and decision making (Towal, Mormann, and Koch 2013; Orquin and Lagerkvist 2015). In fact, several studies suggest that salience exerts a small but robust effect on fixation likelihood so that decision makers are more likely to fixate on objects with a higher salience (Lohse 1997; Milosavljevic et al. 2012; Navalpakkam et al. 2012). Salience also seems to influence fixation order, with more salient objects being fixated on earlier (Peschel and Orquin 2013). Salience can be harnessed to direct decision makers’ attention to specific information by increasing the contrast of the information or changing its colors or orientation to stand out from the environment. Bogomolova and colleagues, for instance, showed that making unit prices more salient helps consumers identify and use unit prices during grocery shopping (Bogomolova et al. 2015). One limitation of employing salience to attract attention is that it cannot be used to enhance attention to all information. Since salience is a relative feature of the environment, making one object salient is always done at the expense of making all other objects in the environment relatively less salient. One open question regarding visual salience is whether and to what extent it interacts with top-down control. Some studies suggest that salience interacts with top-down control (Nordfang, Dyrholm, and Bundesen 2013), but other studies suggest that this may not apply in the context of consumer decision making (Orquin and Lagerkvist 2015). Set Size By set size we refer to the number of visible objects or information elements in a specific decision situation. For instance, when buying pasta in a supermarket, the set size is the number of visible pasta alternatives on the shelf. When focusing on a specific product, the set size is the number of features on that given product. Since attention is a limited resource, increasing the set size typically leads decision makers to fixate on a smaller proportion of the set (Spinks and Mortimer 2015). Adding elements to a set therefore reduces the likelihood of any of the elements being fixated, whereas removing elements from the set increases the likelihood of the remaining elements being fixated. Visschers and colleagues have, for instance, shown that consumers are less likely to fixate on nutrition labels on visually cluttered products with large set sizes (Visschers, Hess, and Siegrist 2010), and reducing the set size can be used to strategically increase the chance that consumers view nutrition labels (Orquin et al. 2016). Surface Size The relative surface size of an object refers to the area of the visual environment occupied by that object. Surface size exerts a robust and medium to strong effect on fixation likelihood so that decision makers are more likely to fixate on larger objects (for a review, see Peschel and Orquin 2013). This effect is not merely a function of the object being more likely to attract fixations by chance, but is probably a function of properties of the visual system since surface size seems to adhere to general laws of psychophysics, that is, a diminishing marginal effect on fixation likelihood (Dehaene 2003). Several studies have demonstrated the effect of surface size on attention capture in decision-making tasks, for instance, showing that products with more shelf facings as well as larger yellow page ads are more likely to be looked at by consumers (Lohse 1997; Chandon et al. 2009; Gidlöf et al. 2017). Similar to visual salience, larger objects are likely to cannibalize attention from smaller objects. Position Effects The position of objects in our environment has a strong influence on attention capture. Position effects consist of two types: positions in one-dimensional arrays and positions in two-dimensional arrays. When information is structured in a one-dimensional array such as rows or columns, observers have a strong tendency to begin reading from the top of a column towards the bottom (Sütterlin, Brunner, and Opwis 2008; Chen 2010) and from the left of a row towards the right (Navalpakkam et al. 2012) (the latter holds only in Western societies, where the reading direction is from left to right). Since observers frequently fail to notice all information, objects at the top of a column and objects to the left in a row are more likely to be seen than objects at the bottom of the column and to the right in the row. In two-dimensional arrays, observers tend to fixate on the middle of the array, whereas the corners often go unnoticed (Atalay, Bodur, and Rasolofoarison 2012; Clarke and Tatler 2014; Meißner, Musalem, and Huber 2016). This role of position in guiding attention is well known in retailing, where products placed in the middle of the shelf are chosen more frequently by consumers, presumably because they capture attention better than the products in the corners of the shelf (Chandon et al. 2009; Gidlöf et al. 2017). Position effects can also be harnessed to guide attention to nutrition labels by placing these closer to the center of food products (Orquin et al. 2016). Emotional Stimuli Emotional stimuli such as angry faces or images of babies generally attract attention faster and with a higher likelihood than emotionally neutral stimuli (Calvo and Lang 2004). Furthermore, a negative emotional stimulus influences attention more than a positive emotional stimulus (Nummenmaa, Hyönä, and Calvo 2006). The effect of emotional stimuli on attention has also been demonstrated in decision making. For instance, Orquin and Lagerkvist (2015) showed that participants were more likely to fixate on a food label when the label had a negative emotional connotation compared to a positive one. In both emotional conditions, participants were more likely to fixate on the label compared to when it had no emotional connotations. Emotional content has also been shown to increase attention to advertising (Wedel and Pieters 2008), a principle that seems to be well understood by marketers and policy makers, who often use emotional content in their advertisement and campaigns. Unpredictable Locations Decision makers often wish to ignore health, safety, or medical information when this information leads to unpleasant emotions or urges them to take undesirable actions (Sweeny et al. 2010). Recent work by Orquin, Chrobot, and Grunert (2017) has shown that the predictability of object locations plays a role in our ability to fixate on interesting objects and ignore irrelevant ones. The authors have shown that an unpredictable location increases attention to otherwise ignored nutrition labels. The (un)predictability effect works by either enhancing or impeding top-down control. Predictable locations enhance top-down control by allowing decision makers to attend or ignore information that they perceive to be important or irrelevant. Unpredictable locations impede top-down control, making it more difficult for decision makers to ignore any piece of information in general, thereby increasing attention to otherwise ignored objects. Orquin and colleagues have shown that unpredictable locations enhance attention to nutrition labels when decision makers would have otherwise ignored the labels. When decision makers, on the other hand, are motivated to attend to nutrition labels, predictable locations have increased the likelihood of finding and attending to the labels. Effects of Attention on Choice (Downstream Effects) When attention influences decision making, we refer to it as a downstream effect (Wedel and Pieters 2012; Orquin and Mueller Loose 2013). In the following sections, we describe three such effects, namely the gatekeeping effect, the mere exposure effect, and order effects. The Gatekeeping Effect The gatekeeping effect was defined by Orquin and Mueller Loose (2013) as the effect of exogenously-driven visual nonattendance on decision making, that is, when factors such as those described in the previous section lead decision makers to ignore choice options or attributes. We begin by describing gatekeeping for options. If a decision maker fixates on two products on a supermarket shelf, the decision maker’s preferences for these two products will determine which of them is chosen. If there is a third product on the shelf that the decision maker does not attend to, this product will not be represented in the mind of the decision maker. Without a cognitive representation, the product cannot be chosen by the decision maker (Rangel, Camerer, and Montague 2008). Since attention is also affected by bottom-up control factors unrelated to the preferences of the decision maker, the visual system acts as a gatekeeper for the choice options between which the decision is made. Choice options that are less salient, smaller, less centrally-positioned, etc. are less likely to be fixated on and therefore less likely to be represented in the mind of the decision maker. However, the gatekeeping effect only holds when information is presented visually and the choice options or option features cannot be retrieved from memory. Gatekeeping effects have been demonstrated in different areas and with different bottom-up mechanisms, for instance, as position effects on choice (Atalay, Bodur, and Rasolofoarison 2012; Navalpakkam et al. 2012), effects of surface size on choice (Chandon et al. 2009), effects of salience on choice (Milosavljevic et al. 2012) and effects of set size on choice (Orquin et al. 2016). Understanding the principles of attention capture described in the previous section naturally helps us in understanding to what extent the gatekeeping effect can influence decision makers. Gatekeeping can also act at the attribute level when decision makers fail to notice some attributes of a product or choice option. Given that the product or its features cannot be recalled from memory, the unattended attributes do not have a representation in the mind of the decision maker. Gatekeeping at the attribute level can influence the preferences for choice options in either a positive or a negative direction. If the ignored attribute is positive, then failing to notice it may lower the preference for the choice option and vice versa if the attribute is negative. The latter effect is proverbially described as the small print effect, that is, the idea that presenting undesirable information in a smaller font will lower the likelihood of decision makers reading it and taking it into account. One example is small nutrition labels or nutrition labels of low salience that fail to attract attention and influence decision makers in a more healthful direction (Orquin and Lagerkvist 2015; Orquin et al. 2016). The Mere Exposure Effect The mere exposure effect was first described by Zajonc (1968), who asked participants to guess the meaning of a series of unknown Chinese characters. Before the guessing task, participants were exposed to some of the characters without any description. Zajonc found that the pre-exposed characters were generally perceived as having a more positive meaning than those not pre-exposed. Zajonc and others have replicated this effect numerous times showing that people generally prefer more familiar stimuli. The idea of mere exposure was later introduced in vision research when Shimojo and colleagues proposed the gaze cascade effect, that is, attending to choice options creates a mere exposure effect during the decision process (Shimojo et al. 2003). The gaze cascade effect inspired a range of new studies and models such as the attentional Drift Diffusion Model (aDDM) by Krajbich and colleagues, which proposes that decision makers accumulate evidence in favor of choice options while fixating on those options (Krajbich, Armel, and Rangel 2010; Krajbich et al. 2012). It has also been shown that consumers may accumulate negative evidence against an option when fixating on aversive options or attributes (Armel, Beaumel, and Rangel 2008; Krajbich et al. 2012). The gaze cascade and the aDDM models make a clear and interesting prediction: if decision makers attend more to one choice option than another, preference for the more attended option will increase faster and it is more likely to be chosen. We will refer to this prediction as the evidence accumulation effect. In a simple two-alternative choice task, where both options are fixated and both options are equally preferred, a slight bias in fixations to one option increases the choice likelihood for that option. The evidence accumulation effect has been demonstrated in different domains such as consumer choice (Armel, Beaumel, and Rangel 2008), aesthetic judgments, and image typicality judgments (Glaholt and Reingold 2011). Despite these demonstrations, there are some issues with the evidence accumulation effect, and some studies have failed to replicate this effect (Glaholt and Reingold 2009). First, many studies demonstrating an evidence accumulation effect fail to separate this clearly from a gatekeeping effect. It is not always clear whether the definition of a less attended choice option means that the option receives fewer fixations or that the option fails to be fixated at all. In the latter case, the option would lack a cognitive representation and lead to a gatekeeping effect (Orquin and Mueller Loose 2013). Second, even if there is an evidence accumulation effect, it is unclear if this stems from directly fixating on choice options, as proposed in the gaze cascade and aDDM models. Other studies suggest that this effect is indistinguishable from a more general mere exposure effect, that is, the effect works even when options are not fixated directly but only attended in the peripheral visual field (Nittono and Wada 2009; Bird, Lauwereyns, and Crawford 2012). The mere exposure effect based on peripheral vision is, for instance, obtained by letting participants fixate on one part of the screen while attending to choice options shown in another part of the screen. Third, studies fitting the aDDM cannot distinguish the evidence accumulation effect (attended options become more preferred) from the effect of top-down control (preferred options receive more fixations). Distinguishing top-down control from downstream effects can only be accomplished by experimental designs in which attention is manipulated. Overall, it seems that attention may have an effect on choice beyond the gatekeeping effect. The mere exposure effect has been exploited for decades by advertisers to increase familiarity and liking for their brands, and more recently online companies have begun to use behavioral targeting to re-expose consumers to products across internet platforms, which presumably leads to a similar effect. Order Effects One of the most well-known order effects in judgment and decision making is the anchoring effect described by Tversky and Kahneman (1974). The effect proposes that information presented first serves as an anchor for later judgments that are adjusted relative to the original anchor. For instance, Ariely, Loewenstein, and Prelec (2003) asked participants to recall the last two digits in their social security number. Later these participants provided estimates of their willingness to pay for different products. Those participants whose last two digits were higher provided higher estimates for the products. The idea that the order in which we process or attend to information influences choice has also been explored in visual decision making. For instance, Bergus, Levin, and Elstein (2002) found that when patients were presented with risk information first, they perceived the treatment more positively than when presented with risk information last. Similar order effects have been shown for perceptual judgments (Trueblood and Dasari 2017) and in risky gambles (Kwak and Huettel 2018). However, Heard, Rakow, and Foulsham (2017) failed to replicate the findings of Bergus and colleagues. Furthermore, van der Laan et al. (2015) found that manipulating the location of the first fixation towards an option did not influence its likelihood of being chosen. A slightly different stream of research has shown that truncating information search by removing choice options biases decisions in favor of the last seen option (Richardson, Spivey, and Hoover 2009; Pärnamets et al. 2015). This suggests that attention may have a recency effect, rather than a primacy effect on decision making, that is, information attended to last impacts choice, whereas information attended to first does not. It is clear that more research is needed to draw substantial conclusions about attention order effects. Even if such effects exist and exert a robust influence on decision makers, it remains to be shown how this might occur in naturalistic environments. Conclusion In this article, we have reviewed studies on attention and decision making and discussed a range of principles on how we allocate our attention in a decision-making situation. We have also shown that the way in which we allocate attention has consequences for the decision made subsequently. This has important implications for understanding consumer food choices, a topic that has received increasing attention in agricultural economics during the past decades. Since consumers make such choices in information-overloaded environments, the mechanisms governing selective attention will therefore have a strong impact in framing and potentially biasing choices. We have also shown that the way information is presented will always to some extent bias our choices, as there is no neutral way of presenting information. These biases arise due to environmental factors that are exogenous and often unrelated to our goals. We may, for instance, on a trip to the supermarket search for a product that maximizes value for money. Whether we find such a product depends on our time constraints as well as the environment, for example, the number of available products and the position, size, and salience of these products. Since we usually do not choose what we do not see, we are more likely to choose products that are positioned in the middle of the shelf, that have more facings and are more salient than competing brands. The more products there are, the stronger these biases become. Producers and retailers most likely understand these visual biases very well. For example, unit prices, which are meant to make it easier for consumers to compare prices, are often presented with small or cluttered font, which makes consumers less likely to see and use unit prices (Bogomolova et al. 2015). Of course, the external environment may sometimes be aligned with consumer goals, for instance, when supermarket shelves or online stores are sorted according to prices (Valenzuela, Raghubir, and Mitakakis 2013). The existence of these biases has implications for the study of consumer preferences. It also has more practical implications for public policy in all those cases where public policy tries to promote certain types of choices by making information more readily available. When consumer choices are affected not only by their preferences, but also by factors related to the way in which choice alternatives are presented, and hence unrelated to preferences, this implies that observed consumer choices, like in a choice experiment, are an imperfect basis for inferring consumer preferences, even in incentive-compatible situations. Across people, careful study design can probably eliminate many of these effects by using random ordering of alternatives and attributes, rotating placements of different pieces of information, variation of salience across attributes, etc. At the individual level, it is more questionable whether that is practically possible. In public policy, making additional information available has a long tradition in areas like promoting healthy choices or, more recently, promoting sustainable choices. We believe that policy makers have an emerging understanding of visual biases and this may be particularly true for behaviorally-oriented policy makers. We therefore see three possible reactions by policy makers to these biases: (a) ignore the biases, (b) attempt to minimize the negative impact of biases, and (c) attempt to maximize the positive impact of biases. We discuss the three possibilities below. The first and simplest approach to visual biases in decision making is to ignore them. While visual biases affect individual decision making in such a way that the decisions made are not exclusively based on consumer preferences, this will affect the functioning of the market mechanism only if the biases favor one particular direction; if, for example, all sellers present choice alternatives in a way that systematically leads to a de-emphasis of certain alternatives or certain attributes. But while some producers might be tempted to take advantage of these biases by hiding disadvantageous information and promoting advantageous information, other producers may display information in a more honest way. Since consumers are free to choose the honest and transparent producers, the manipulative producers will eventually disappear or their market share will consist only of those consumers who do not wish to be informed about specific attributes. For instance, when purchasing hedonic products such as sweets or luxury goods, we may not wish to know the amount of calories we are about to consume or learn about the disadvantages of this particular luxury item. Another policy approach is to counteract the impact of visual biases. Regulations concerning the size and placement of, for instance, nutrition information would fall under this approach. By forcing producers to place nutrition information on the front of food products there is a greater chance that consumers will notice such information (Graham, Orquin, and Visschers 2012). Such regulations have the advantage of minimizing visual biases that would otherwise lead consumers to ignore nutrition information, while being relatively easy to be implemented by producers. However, such regulations may prescribe changes that are too small to actually make a difference. For example, the EU regulation on nutrition information specifies that “mandatory food information shall be marked in a conspicuous place in such a way as to be easily visible, [and] clearly legible” (European Parliament 2011). The regulation also specifies a minimum font size for nutrition labels equal to or greater than 1.2 mm. For products with a surface size less than 80 cm2 the minimum font size is 0.9 mm. To bring this into context: If a product has a surface size just below 80 cm2 a word such as “calories” has to cover at least 0.04% of the product surface. The entire nutrition table could probably be restricted to less than 0.5% of the product’s surface. The final policy approach is to make use of visual biases to achieve policy goals. This approach is gaining grounds as more governmental agencies adopt behaviorally-oriented policies. Using visual biases is seen as part of a larger toolbox for designing choice architecture (Münscher, Vetter, and Scheuerle 2016). As discussed above, the position effect leads decision makers to ignore information lower in the list, but this effect can also be used to increase healthy food choices in restaurants by placing healthy foods higher on the menu (Dayan and Bar-Hillel 2011). Such nudging-type approaches have been advocated widely in the context of the recent move towards using nudging as a policy tool (Liu et al. 2014) and is usually ethically justified on the grounds of “libertarian paternalism” (Sunstein 2014). It is clear that such approaches to “manipulate” consumers for the sake of their own good are controversial (see Sunstein 2014 for a defence, and Bovens 2009, for a critical treatment). The authors would like to thank Erik S. Lahm, Martin Meissner, and Julia Nafziger for comments on a previous version of this manuscript. References Ariely D. , Loewenstein G. , Prelec D. . 2003 . “ Coherent Arbitrariness”: Stable Demand Curves without Stable Preferences . The Quarterly Journal of Economics 118 1 : 73 – 106 . Google Scholar Crossref Search ADS Armel K.C. , Beaumel A. , Rangel A. . 2008 . Biasing Simple Choices by Manipulating Relative Visual Attention . Judgment and Decision Making 3 5 : 396 – 403 . Atalay A.S. , Bodur H.O. , Rasolofoarison D. . 2012 . Shining in the Center: Central Gaze Cascade Effect on Product Choice . Journal of Consumer Research 39 4 : 848 – 66 . Google Scholar Crossref Search ADS Bergus G.R. , Levin I.P. , Elstein A.S. . 2002 . Presenting Risks and Benefits to Patients . Journal of General Internal Medicine 17 8 : 612 – 17 . Google Scholar Crossref Search ADS PubMed Bird G.D. , Lauwereyns J. , Crawford M.T. . 2012 . The Role of Eye Movements in Decision Making and the Prospect of Exposure Effects . Vision Research 60 (May): 16 – 21 . Google Scholar Crossref Search ADS PubMed Bogomolova S. , Oppewal H. , Cohen J. , Yao J. . 2015 . How the Layout of a Price Label Influences Unit Price Visual Attention and Choice during Grocery Shopping . SSRN Electronic Journal . doi:10.2139/ssrn.2610063. Bovens L. 2009 . The Ethics of Nudge. In Preference Change. Theory and Decision Library, Vol 42, ed. T. Grüne-Yanoff and S.O. Hansson, 207–19. Dordrecht, The Netherlands: Springer. Calvo M.G. , Lang P.J. . 2004 . Gaze Patterns When Looking at Emotional Pictures: Motivationally Biased Attention . Motivation and Emotion 28 3 : 221 – 43 . Google Scholar Crossref Search ADS Chandon P. , Hutchinson J.W. , Bradlow E.T. , Young S.H. . 2009 . Does in-Store Marketing Work? Effects of the Number and Position of Shelf Facings on Brand Attention and Evaluation at the Point of Purchase . Journal of Marketing 73 6 : 1 – 17 . Google Scholar Crossref Search ADS Chen L. 2010 . Eye-Tracking Study of User Behavior in Recommender Interfaces . User Modeling, Adaptation, and Personalization 375 – 80 . doi:10.1007/978-3-642-13470-8_35. Clarke A.D.F. , Tatler B.W. . 2014 . Deriving an Appropriate Baseline for Describing Fixation Behaviour . Vision Research 102 : 41 – 51 . Google Scholar Crossref Search ADS PubMed Corbetta M. , Shulman G.L. . 2002 . Control of Goal-Directed and Stimulus-Driven Attention in the Brain . Nature Reviews Neuroscience 3 3 : 215 – 29 . Google Scholar Crossref Search ADS Costa-Font J. , Just D.R. , Fasolo B. , Powdthavee N. . 2014 . Nudging Ourselves? The Limits of Incentivizing “Good Behavior” . Applied Economic Perspectives and Policy 36 1 : 1 – 5 . Google Scholar Crossref Search ADS Dayan E. , Bar-Hillel M. . 2011 . Nudge to Nobesity II: Menu Positions Influence Food Orders . Judgment and Decision Making 6 4 : 333 – 42 . Dean M. , Neligh N. . 2017 . Experimental Tests of Rational Inattention. Manuscript in Preparation. http://www.columbia.edu/∼md3405/Working_Paper_21.pdf. Accessed August 24, 2018. Dehaene S. 2003 . The Neural Basis of the Weber–Fechner Law: A Logarithmic Mental Number Line . Trends in Cognitive Sciences 7 4 : 145 – 7 . Google Scholar Crossref Search ADS PubMed Duchowski A. 2007 . Eye Tracking Methodology: Theory and Practice . Berlin, Heidelberg, Germany : Springer Science & Business Media . European Parliament . 2011 . Legislative Observatory: 2008/0028 (COD)—06/07/ 2011 Text Adopted by Parliament, 2nd Reading. European Parliament . http://www.europarl.europa.eu/oeil/popups/summary.do?id=1159341&l=en&t=D. Accessed August 24, 2018. Foulsham T. , Underwood G. . 2008 . What Can Saliency Models Predict about Eye Movements? Spatial and Sequential Aspects of Fixations during Encoding and Recognition . Journal of Vision 8 2 : 1 – 17 . Google Scholar Crossref Search ADS PubMed Gabaix X. 2017 . Behavioral Inattention. NBER Working Paper No. 24096, Cambridge, MA. doi:10.3386/w24096. Gidlöf K. , Anikin A. , Lingonblad M. , Wallin A. . 2017 . Looking is Buying. How Visual Attention and Choice Are Affected by Consumer Preferences and Properties of the Supermarket Shelf . Appetite 116 (September): 29 – 38 . Google Scholar Crossref Search ADS PubMed Glaholt M.G. , Reingold E.M. . 2009 . Stimulus Exposure and Gaze Bias: A Further Test of the Gaze Cascade Model . Attention, Perception & Psychophysics 71 3 : 445 – 50 . Google Scholar Crossref Search ADS PubMed Glaholt M.G. , Reingold E.M. 2011 . Eye Movement Monitoring as a Process Tracing Methodology in Decision Making Research . Journal of Neuroscience, Psychology, and Economics 4 2 : 125 – 46 . Google Scholar Crossref Search ADS Graham D.J. , Orquin J.L. , Visschers V.H.M. . 2012 . Eye Tracking and Nutrition Label Use: A Review of the Literature and Recommendations for Label Enhancement . Food Policy 37 4 : 378 – 82 . Google Scholar Crossref Search ADS Gregory R.L. 1997 . Eye and Brain: The Psychology of Seeing . 5th Ed . Princeton, NJ : Princeton University Press . Heard C.L. , Rakow T. , Foulsham T. . 2017 . The Role of Presentation Order and Orientation on Information Search and Evaluations: An Eye-Tracking Study. In Proceedings of the 39th Annual Conference of the Cognitive Science Society, ed. G. Gunzelmann, A. Howes, T. Tenbrink, and J. Davelaar, 2174–79. Austin, TX: Cognitive Science Society. Holmqvist K. , Nyström M. , Andersson R. , Dewhurst R. , Jarodzka H. , van de Weijer J. . 2011 . Eye Tracking: A Comprehensive Guide to Methods and Measures . United Kingdom : Oxford University Press . Itti L. , Koch C. . 2000 . A Saliency-Based Search Mechanism for Overt and Covert Shifts of Visual Attention . Vision Research 40 ( 10–12 ): 1489 – 506 . Google Scholar Crossref Search ADS PubMed Itti L. , Koch C. 2001 . Computational Modelling of Visual Attention . Nature Reviews Neuroscience 2 3 : 194 – 203 . Google Scholar Crossref Search ADS PubMed Jansson E. 2016 . Tracking the Eye of the Beholder: The Roles of Effort and Preferences in Everyday Decision Making (Master’s Thesis). Lund University, Lund, Sweden. Kelton A.S. , Pennington R.R. , Tuttle B.M. . 2010 . The Effects of Information Presentation Format on Judgment and Decision Making: A Review of the Information Systems Research . Journal of Information Systems 24 2 : 79 – 105 . Google Scholar Crossref Search ADS Kowler E. , Anderson E. , Dosher B. , Blaser E. . 1995 . The Role of Attention in the Programming of Saccades . Vision Research 35 13 : 1897 – 916 . Google Scholar Crossref Search ADS PubMed Krajbich I. , Armel C. , Rangel A. . 2010 . Visual Fixations and the Computation and Comparison of Value in Simple Choice . Nature Neuroscience 13 10 : 1292 – 98 . Google Scholar Crossref Search ADS PubMed Krajbich I. , Lu D. , Camerer C.F. , Rangel A. . 2012 . The Attentional Drift-Diffusion Model Extends to Simple Purchasing Decisions . Frontiers in Psychology 3 : 1 – 18 . Google Scholar Crossref Search ADS PubMed Kwak Y. , Huettel S. . 2018 . The Order of Information Processing Alters Economic Gain-Loss Framing Effects . Acta Psychologica 182 (January): 46 – 54 . Google Scholar Crossref Search ADS PubMed Liu P.J. , Wisdom J. , Roberto C.A. , Liu L.J. , Ubel P.A. . 2014 . Using Behavioral Economics to Design More Effective Food Policies to Address Obesity . Applied Economic Perspectives and Policy 36 1 : 6 – 24 . Google Scholar Crossref Search ADS Lohse G.L. 1997 . Consumer Eye Movement Patterns on Yellow Pages Advertising . Journal of Advertising 26 1 : 61 – 73 . Google Scholar Crossref Search ADS Meißner M. , Musalem A. , Huber J. . 2016 . Eye Tracking Reveals Processes that Enable Conjoint Choices to Become Increasingly Efficient with Practice . Journal of Marketing Research 53 1 : 1 – 17 . Google Scholar Crossref Search ADS Milosavljevic M. , Navalpakkam V. , Koch C. , Rangel A. . 2012 . Relative Visual Saliency Differences Induce Sizable Bias in Consumer Choice . Journal of Consumer Psychology 22 1 : 67 – 74 . Google Scholar Crossref Search ADS Münscher R. , Vetter M. , Scheuerle T. . 2016 . A Review and Taxonomy of Choice Architecture Techniques . Journal of Behavioral Decision Making 29 5 : 511 – 24 . Google Scholar Crossref Search ADS Navalpakkam V. , Kumar R. , Li L. , Sivakumar D. . 2012 . Attention and Selection in Online Choice Tasks. In User Modeling, Adaptation, and Personalization , ed. Masthoff J. , Mobasher B. , Desmarais M. , Nkambou R. , 200 – 11 . Berlin, Heidelberg, Germany : Springer . Nittono H. , Wada Y. . 2009 . Gaze Shifts Do Not Affect Preference Judgments of Graphic Patterns . Perceptual and Motor Skills 109 1 : 79 – 94 . Google Scholar Crossref Search ADS PubMed Nordfang M. , Dyrholm M. , Bundesen C. . 2013 . Identifying Bottom-up and Top-down Components of Attentional Weight by Experimental Analysis and Compu-Tational Modeling . Journal of Experimental Psychology: General 142 2 : 510 – 35 . Google Scholar Crossref Search ADS PubMed Nummenmaa L. , Hyönä J. , Calvo M.G. . 2006 . Eye Movement Assessment of Selective Attentional Capture by Emotional Pictures . Emotion 6 2 : 257 – 68 . Google Scholar Crossref Search ADS PubMed Orquin J.L. , Chrobot N. , Grunert K.G. . 2017 . Guiding Decision Makers’ Eye Movements with (Un)Predictable Object Locations . Journal of Behavioral Decision Making . doi:10.1002/bdm.2060. Orquin J.L. , Holmqvist K. . 2017 . Threats to the Validity of Eye-Movement Research in Psychology . Behavior Research Methods 1 – 12 . doi:10.3758/s13428-017-0998-z. Orquin J.L. , Lagerkvist C.J. . 2015 . Effects of Salience Are Both Short- and Long-Lived . Acta Psychologica 160 : 69 – 76 . Google Scholar Crossref Search ADS PubMed Orquin J.L. , Lahm E. , Redere L. , Bagger M.P. , Tsalis G. , Grunert K.G. . 2016 . Forbrugeradfærd Ved Valg Af Nøglehulsmærkede Produkter i Detailhandlen (DCA RAPPORT NR. 087). https://pure.au.dk/ws/files/107375605/DCArapport087.pdf. Accessed August 24, 2018. Orquin J.L. , Mueller Loose S. . 2013 . Attention and Choice: A Review on Eye Movements in Decision Making . Acta Psychologica 144 1 : 190 – 206 . Google Scholar Crossref Search ADS PubMed Parkhurst D. , Law K. , Niebur E. . 2002 . Modeling the Role of Salience in the Allocation of Overt Visual Attention . Vision Research 42 1 : 107 – 23 . Google Scholar Crossref Search ADS PubMed Perkovic S. , Orquin J.L. . 2018 . Implicit Statistical Learning in Real-World Environments Leads to Ecologically Rational Decision Making . Psychological Science 29 1 : 34 – 44 . Google Scholar Crossref Search ADS PubMed Peschel A.O. , Orquin J.L. . 2013 . A Review of the Findings and Theories on Surface Size Effects on Visual Attention . Frontiers in Psychology 4 : 21 – 30 . Google Scholar Crossref Search ADS PubMed Posner M.I. , Snyder C.R. , Davidson B.J. . 1980 . Attention and the Detection of Signals . Journal of Experimental Psychology: General 109 2 : 160 – 74 . Google Scholar Crossref Search ADS Pärnamets P. , Johansson P. , Hall L. , Balkenius C. , Spivey M.J. , Richardson D.C. . 2015 . Biasing Moral Decisions by Exploiting the Dynamics of Eye Gaze . Proceedings of the National Academy of Sciences 112 13 : 4170 – 75 . Google Scholar Crossref Search ADS Rangel A. , Camerer C. , Montague P.R. . 2008 . A Framework for Studying the Neurobiology of Value-Based Decision Making . Nature Reviews Neuroscience 9 7 : 545 – 56 . Google Scholar Crossref Search ADS PubMed Richardson D.C. , Spivey M.J. , Hoover M.A. . 2009 . How to Influence Choice by Monitoring Gaze. In Proceedings of the 31st Annual Conference of the Cognitive Science Society, ed. N. Taatgen and H. van Rijn, 2244–49. Austin, TX: Cognitive Science Society. Rutishauser U. , Koch C. . 2007 . Probabilistic Modeling of Eye Movement Data during Conjunction Search via Feature-Based Attention . Journal of Vision 7 6 : 1 – 20 . Google Scholar Crossref Search ADS Scarpa R. , Zanoli R. , Bruschi V. , Naspetti S. . 2013 . Inferred and Stated Attribute Non-Attendance in Food Choice Experiments . American Journal of Agricultural Economics 95 1 : 165 – 80 . Google Scholar Crossref Search ADS Shimojo S. , Simion C. , Shimojo E. , Scheier C. . 2003 . Gaze Bias Both Reflects and Influences Preference . Nature Neuroscience 6 12 : 1317 – 22 . Google Scholar Crossref Search ADS PubMed Sims C.A. 2003 . Implications of Rational Inattention . Journal of Monetary Economics 50 3 : 665 – 90 . Google Scholar Crossref Search ADS Spinks J. , Mortimer D. . 2015 . Lost in the Crowd? Using Eye-Tracking to Investigate the Effect of Complexity on Attribute Non-Attendance in Discrete Choice Experiments . BMC Medical Informatics and Decision Making 16 14 : 1 – 13 . Stigler G.J. 1961 . The Economics of Information . Journal of Political Economy 69 3 : 213 – 25 . Google Scholar Crossref Search ADS Sunstein C.R. 2014 . Why Nudge?: The Politics of Libertarian Paternalism . New Haven, CT : Yale University Press . Sütterlin B. , Brunner T.A. , Opwis K. . 2008 . Eye-Tracking the Cancellation and Focus Model for Preference Judgments . Journal of Experimental Social Psychology 44 3 : 904 – 11 . Google Scholar Crossref Search ADS Sweeny K. , Melnyk D. , Miller W. , Shepperd J.A. . 2010 . Information Avoidance: Who, What, When, and Why . Review of General Psychology 14 4 : 340 – 53 . Google Scholar Crossref Search ADS Thaler R.H. , Sunstein C.R. . 2008 . Nudge . London : Penguin . Towal R.B. , Mormann M. , Koch C. . 2013 . Simultaneous Modeling of Visual Saliency and Value Computation Improves Predictions of Economic Choice . Proceedings of the National Academy of Sciences of the United States of America 110 40 : 3858 – 67 . Google Scholar Crossref Search ADS Trueblood J.S. , Dasari A. . 2017 . The Impact of Presentation Order on the Attraction Effect in Decision-Making. In Proceedings of the 39th Annual Conference of the Cognitive Science Society, ed. G. Gunzelmann, A. Howes, T. Tenbrink, and J. Davelaar, 3374–79. Austin, TX: Cognitive Science Society. Tversky A. , Kahneman D. . 1974 . Judgment under Uncertainty: Heuristics and Biases . Science (New York, N.Y.) 185 : 1124 – 31 . Google Scholar Crossref Search ADS PubMed Unnevehr L. , Eales J. , Jensen H. , Lusk J. , McCluskey J. , Kinsey J. . 2010 . Food and Consumer Economics . American Journal of Agricultural Economics 92 2 : 506 – 21 . Google Scholar Crossref Search ADS Valenzuela A. , Raghubir P. , Mitakakis C. . 2013 . Shelf Space Schemas: Myth or Reality? Journal of Business Research 66 7 : 881 – 8 . Google Scholar Crossref Search ADS van der Laan L.N , Hooge I.T.C. , de Ridder D.T.D. , Viergever M.A. , Smeets P.A.M. . 2015 . Do You like What You See? The Role of First Fixation and Total Fixation Duration in Consumer Choice . Food Quality and Preference 39 (January): 46 – 55 . Google Scholar Crossref Search ADS Van Loo E.J. , Grebitus C. , Nayga R.M. Jr , Roosen J. , Verbeke W. . 2018 . On the Measurement of Consumer Preferences and Choice Behavior: The Role of Visual Attention . Applied Economic Perspectives & Policy 40 4 : 538 – 62 . Van Loo E.J. , Nayga R.M. , Campbell D. , Seo H.-S. , Verbeke W. . 2018 . Using Eye Tracking to account for Attribute Non-Attendance in Choice Experiments . European Review of Agricultural Economics . doi:10.1093/erae/jbx035. Visschers V.H.M. , Hess R. , Siegrist M. . 2010 . Health Motivation and Product Design Determine Consumers’ Visual Attention to Nutrition Information on Food Products . Public Health Nutrition 13 07 : 1099 – 106 . Google Scholar Crossref Search ADS PubMed Wästlund E. , Shams P. , Otterbring T. . 2017 . Unsold Is Unseen … or Is It? Examining the Role of Peripheral Vision in the Consumer Choice Process Using Eye-Tracking Methodology . Appetite 120 : 49 – 56 . Google Scholar Crossref Search ADS PubMed Wedel M. , Pieters R. . 2008 . A Review of Eye-Tracking Research in Marketing. In Review of Marketing Research , Vol. 4 , ed. Malhotra N.K. , 123 – 47 . Armonk, NY : M.E. Sharpe . Wedel M. , Pieters R. , eds. 2012 . Visual Marketing: From Attention to Action . London : Psychology Press . Zajonc R.B. 1968 . Attitudinal Effects of Mere Exposure . Journal of Personality and Social Psychology 9 (2, Pt.2) : 1 – 27 . Google Scholar Crossref Search ADS © The Author(s) 2018. Published by Oxford University Press on behalf of the Agricultural and Applied Economics Association. All rights reserved. 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Published: Dec 1, 2018
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