Does dietary learning occur outside awareness?

Consciousness and Cognition Consciousness and Cognition 13 (2004) 453–470 www.elsevier.com/locate/concog

Does dietary learning occur outside awareness? Jeffrey M. Brunstrom* Department of Human Sciences, Loughborough University, Loughborough, Leicestershire, LE11 3TU, England, UK Received 9 April 2003 Available online 17 June 2004

Abstract Several forms of dietary learning have been identified in humans. These include flavor–flavor learning, flavor–postingestive learning (including flavor–caffeine learning), and learned satiety. Generally, learning is thought to occur in the absence of contingency (CS–US) or demand awareness. However, a review of the literature suggests that this conclusion may be premature because measures of awareness lack the rigor that is found in studies of other kinds of human learning. If associations do configure outside awareness then this should be regarded as a rare instance of automatic learning. Conversely, if awareness is important, then successful learning may be governed by an individualÕs beliefs and predilection to attend to stimulus relationships. For researchers of dietary learning this could be critical because it might explain why learning paradigms have a reputation for being unreliable. Since most food preferences are learned, asking questions about awareness can also tell us something fundamental about everyday dietary control. Ó 2004 Elsevier Inc. All rights reserved. Keywords: Learning; Awareness; Implicit cognition; Flavor preference; Conditioning; Associative learning; Human; Dietary restraint; Caffeine

1. Introduction Humans have a conspicuous tendency to evaluate food. Yet, new-borns are generally only able to respond to the affective quality of basic tastes, sweet, sour, bitter, and saltiness (Crook, 1978; Ganchrow, Steiner, & Daher, 1983). Our propensity to encode complex and rich evaluative opinion implies that the process awards us an important biological advantage (for a review see * Fax: +44-(0)1509-223940. E-mail address: [email protected].

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Birch, 1992). For example, one benefit is that it helps us to avoid foods that make us ill (Garcia & Koelling, 1966). At a procedural level, these conditioned taste aversions can be regarded as a form of Pavlovian conditioning. Typically, a flavor (the conditioned stimulus—CS) becomes disliked after it is paired with ingestion of a substance that evokes nausea (e.g., lithium chloride; the unconditioned stimulus—US). A similar process may be fundamental to normal dietary control, because it is found to facilitate both food selection and intake. Typically, researchers distinguish between three types of dietary learning. First, flavor–flavor learning occurs when two different flavors become associated. When a neutral flavor (CS) is presented in close temporal proximity to an already liked flavor (US), then the valance of the neutral flavor is found to increase. This form of dietary learning is found in both humans (Brunstrom, Downes, & Higgs, 2001; Zellner, Rozin, Aron, & Kulish, 1983) and rats (Lavin, 1976). Similarly, a disliked US can reduce the valance of a neutral flavor (Baeyens, Eelen, & Crombez, 1995; Baeyens, Eelen, Van den Bergh, & Crombez, 1990a; Capaldi & Hunter, 1994). Flavor–flavor learning relies on temporal proximity between the CS and the US (Lavin, 1976; Lyn & Capaldi, 1994). However, this is not the case with all forms of dietary learning. Flavor–postingestive learning occurs when a flavor (CS) becomes liked after it is paired with a substance (US) that is rewarding after it is ingested. In cases where the US is rewarding metabolically (i.e., it is food) then this is referred to as flavor–nutrient learning. In both rats and humans, flavor–nutrient learning has been demonstrated using a range of USs, including; starch (Booth, Mather, & Fuller, 1982; Elizalde & Sclafani, 1988); fats (Johnson, McPhee, & Birch, 1991; Kern, McPhee, Fisher, Johnson, & Birch, 1993; Sclafani, 1990) and protein (Baker, Booth, Duggan, & Gibson, 1987; Gibson, Wainwright, & Booth, 1995). Other substances that offer similar postingestive reinforcement are alcohol (Ackroff & Sclafani, 2001) and caffeine (Rogers, Richardson, & Elliman, 1995). The final type of dietary learning is germane to the control of the eating episode itself. During a short meal, food may evoke insufficient postingestive feedback to terminate eating behavior (Le Magnen, 1956). Instead, future anticipatory control of meal size occurs when the flavor of a food (CS) becomes associated with visceral cues (US) that are present towards the end of a meal. The acquisition of this information is called learned satiety. Its discovery has been critical, because it demonstrates that intake and satiety are not determined solely by sensory and biological feedback during a meal. Rather, dietary control is achieved by incorporating information gleaned from previous experience with a food. This phenomenon was first demonstrated by Booth in rats (Booth, 1972; Davis & Campbell, 1973) and then later in humans (Birch & Deysher, 1985; Booth, Lee, & McAleavey, 1976; Booth et al., 1982; Booth & Toase, 1983; Shaffer & Tepper, 1994; Tepper & Farkas, 1994). Perhaps around 95% of all dietary-learning experiments are conducted using animals. This may frustrate researchers who are interested to observe learning in humans. Yet, in part, this bias probably reflects a good deal of sound reasoning. To understand the underlying process it is important to be able to observe clear and reliable instances of the phenomenon under scrutiny. Human studies are more likely to encounter nuisance or confounding variables, some of which may be difficult to measure. In particular, previous experience with flavor stimuli may limit the extent to which learning can occur (Boakes, Rossiarnaud, & Garciahoz, 1987; Kalat & Rozin, 1973), or it may influence responding in other unpredictable ways. Furthermore, finding test stimuli that are truly novel (and neutral) is not trivial. In contrast, animals can be reared in an

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environment that ensures no previous contact with flavor stimuli. Researchers are also able to take advantage of pharmacological interventions (Azzara, Bodnar, Delamater, & Sclafani, 2001) and brain-lesion procedures (Touzani & Sclafani, 2002) that would otherwise be regarded as unethical in a human population. Despite the advantages of animal-based studies, one question can only be addressed using human participants. This relates to the role of awareness in learning. Field (2000a) draws a useful distinction between two types of awareness. The extent to which a person acquires cognizance that a particular CS has previously preceded a particular US can be called contingency awareness. In contrast, the term demand awareness refers to the participantÕs knowledge of the behavioral outcome that is predicted. The reader is referred to several excellent reviews that consider the role of awareness in a range of Pavlovian conditioning paradigms (Field, 2000a, 2000b; Lovibond & Shanks, 2002; Shanks & St. John, 1994; De Houwer, Thomas, & Baeyens, 2001). This paper focuses on the importance (or otherwise) of awareness in relation to dietary learning. Asking questions about awareness in this context is neither trivial nor pedantic. Rather, I will argue that this is actually rather urgent and important. The first of three sections explores how awareness has been measured in studies of dietary learning. I hope to show that in most cases awareness has either been ignored or researchers have used measurement techniques that lack the sophistication that is needed to rule out alternative accounts based on demand awareness. The second section considers reasons why issues relating to awareness may be fundamental to understanding everyday dietary control. In the final section I attempt to contextualize dietary learning by drawing comparisons with what is known about awareness in other kinds of human learning.

2. What do we know about measures of awareness in dietary learning experiments? When assessing evidence for dietary learning it is very important to consider alternative explanations based around demand awareness. It is also important to scrutinize ways in which contingency awareness is measured. This is because a lack of demand awareness is often inferred from an inability to report contingency relationships. 2.1. Flavor–nutrient learning and learned satiety In studies of flavor–nutrient learning and learned satiety, the role of awareness has rarely been addressed. One notable exception is BoothÕs first study of learned satiety in humans (Booth et al., 1976). During training, participants were given a lemon-flavored drink that contained high or low levels of starch (US). Efforts were made to match the taste, texture, and density of the two drinks. Participants were instructed to gulp down one of the drinks to minimize the extent to which it was tasted. Immediately afterwards, they were given a novel-tasting yogurt (CS). A different variety of yogurt was paired with the high- and low-energy drink, on alternate days. After this exposure, Booth et al. found reduced food intake following the high-energy-paired yogurt, even when the energy of the preceding drink was no longer modulated. At the end of the experiment, participants completed a questionnaire to probe subjective opinion on the taste of the drinks and other aspects of the experiment. After interpreting the questionnaire responses, Booth et al. noted, ‘‘. . . there

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was nothing in any subjectÕs questionnaire responses which indicated an awareness that the intake difference between the two yogurts was a dependent variable related to a difference between the experimental drinks with which each lunch began.’’ However, before concluding that demand and contingency awareness were definitely absent we should be mindful of the potential problems associated with using a questionnaire in this context. A questionnaire can probe only a finite set of hypotheses. This means that participants might have important explicit knowledge that they find difficult to express. Alternatively, partial awareness may contaminate responding yet not be identified within the structure of the questions. In this regard, it may be relevant that despite failing to articulate the fact that intake differences were related to drink characteristics, a substantial proportion of participants were aware that the starch drinks varied in their sensory properties during acquisition. This makes it likely that the participants acquired some form of contingency awareness. In fairness, Booth et al. (1976) acknowledge the potential problems in interpretation and note that their aim was simply to establish behavioral evidence for learned control of meal size. Indeed, they state explicitly, ‘‘It remains controversial whether the subjectÕs awareness of learning of the contingency between conditioned and unconditioned stimuli is necessary for all forms of conditioning in man.’’ I have outlined Booth et al.Õs (1976) approach to the measurement of awareness because, along with Booth et al. (1982, Experiment 2B), it represents one of the few examples where any attempt is made to measure awareness. Other studies report little or no assessment of either contingency or demand awareness (Birch, McPhee, Steinberg, & Sullivan, 1990; Booth & Toase, 1983; Gibson et al., 1995; Johnson et al., 1991; Kern et al., 1993; however, see Booth, 1994, p. 63, and Mela, 1999). The importance of awareness measurement may have been overlooked. Alternatively, researchers may have chosen to ignore it for technical reasons, particularly in studies involving children (Birch et al., 1990; Birch, Birch, Marlin, & Kramer, 1982; Birch, McPhee, Shoba, Steinberg, & Krehbiel, 1987; Johnson et al., 1991; Kern et al., 1993). However, it is also possible that researchers have assumed that awareness is irrelevant if the textural and flavor characteristics of low- and high-calorie USs are matched exactly. Unfortunately, this is not the case. Matching high- and low-calorie versions of a food is extremely difficult. More importantly, however, this can only help to distinguish between flavor–flavor learning and flavor–nutrient learning. If flavor– flavor learning can be ruled out, then awareness can still occur in the form of an association between a flavor and a visceral sensation that may or may not be easy to describe. In particular, this point may have been overlooked in studies in which caffeine has been used to condition flavor preferences. Caffeine-based research has been studied more extensively than any other form of dietary learning. It is also relevant because the consensual view is that this form of preference learning probably occurs outside awareness (Richardson, Rogers, & Elliman, 1996; Yeomans, Jackson, Lee, Nesic, & Durlach, 2000a; Yeomans et al., 2000b; Yeomans, Spetch, & Rogers, 1998). The next section considers the merits of this claim. 2.2. Awareness and flavor–caffeine learning In early studies of the reinforcing effects of caffeine, it was usual for participants to be told that they were ingesting caffeine, and their attention was often drawn to important stimulus relationships in the experiment (Griffiths, Bigelow, & Liebson, 1986; Griffiths & Woodson, 1988; Hughes et al., 1991). This is unfortunate, because it makes it difficult to eliminate demand

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characteristics. More recently, studies have attempted to ameliorate this problem by concealing the true reason for the experiment and by issuing caffeine in a form that makes it difficult for participants to distinguish between caffeine and placebo. Specifically, participants have been told that the aim of the experiment is to explore the role of food ingredients in flavor preference (Rogers et al., 1995), the development of flavor preferences (Richardson et al., 1996), or the effects of food on mood (Yeomans et al., 2000a, 2000b; Yeomans, Ripley, Lee, & Durlach, 2001), and caffeine has been administered either in a capsule (Richardson et al., 1996; Rogers et al., 1995; Yeomans et al., 2000b) or it has been dissolved into the CS itself (Yeomans et al., 1998, 2000a, 2001). We now know more about caffeine-based flavor learning than any other form of flavor-preference learning. For example, conditioning is more likely in regular caffeine users (Rogers et al., 1995) and in individuals who are caffeine deprived (Yeomans et al., 1998). Also, the expression of flavor preferences seems to be modulated by levels of caffeine deprivation (Yeomans et al., 2000a) and there would appear to be little evidence for latent learning (Yeomans et al., 2001). As in BoothÕs earlier work on learned satiety in humans (Booth et al., 1976), it is fair to say that the role of awareness has not been a primary concern. Most paradigms have incorporated some measure of awareness. However, interpretation is often problematic because measures: (i) lack sensitivity to all forms of demand and contingency awareness, and (ii) are designed such that important comparisons are impossible. These points are discussed in more detail below. Most measures tend to focus more on demand awareness than on contingency awareness. For example, a general lack of awareness has been inferred from responses to open-ended interviews and from questionnaires issued at the end of the experiment. Yeomans et al. (1998, 2000a, 2000b, 2001) have noted that participants are generally unable to report the true purpose of the experiment. Instead, they tend to report that its objective is to study mood changes. However, of this kind should be treated with caution measures, because they do not allow for the possibility that more than one hypothesis can co-occur. The fact that participants mention mood is perhaps not surprising, because they were required to complete a battery of mood questionnaires throughout the experiment. Moreover, in two cases they were told explicitly that mood was the focus of the study when they first volunteered as participants (Yeomans et al., 2000b, 2001). Notwithstanding this issue, Yeomans et al. have argued that a lack of demand awareness is further evidenced by a general inability to indicate whether caffeine had been involved in any aspect of the experiment. Indeed, in two studies, they report that this is the case because yes/no responding did not differ from chance (Yeomans et al., 2000a, 2000b). Whether humans readily bring to mind the concept ÔcaffeineÕ when they experience changes in systemic caffeine levels is a moot point. Perhaps more likely, if learning relies on demand or contingency awareness, then it will be based on cognizance of a relationship between a flavor and information about a somatic event, e.g., ‘‘IÕve had this drink for 4 days and . . .,’’ ‘‘IÕve felt OK,’’ or ‘‘I havenÕt felt as good as I normally do in the morning.’’ This information may not be offered up at interview because it may be difficult to articulate, or otherwise participants may be reluctant to volunteer this type of casual observation. Either way, existing methods probably go only partway to identifying demand awareness and they may fail to provide a sufficiently comprehensive assessment of contingency awareness. A second potential problem is that between-subjects designs have been used (Richardson et al., 1996; Rogers et al., 1995; Yeomans et al., 1998, 2000a, 2000b, 2001). Different groups are given caffeine (CS+) or no caffeine (CS)). Therefore, participants never make CS+/CS) discrimina-

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tions. This makes it difficult to rule out an alternative account of the findings. Consider a typical experiment in which caffeine-deprived subjects are given a flavored drink containing either placebo or caffeine, on four separate but non-consecutive days. An individualÕs evaluative reaction to the flavored drink will be influenced by its affective properties directly, and indirectly, by other contextual factors such as general satisfaction with the way in which the experiment has progressed. Caffeine changes mood (it has been shown previously to influence relevant ratings; tired, energetic, lively, and so on; Richardson, Rogers, Elliman, & Odell, 1996; Yeomans et al., 1998). This means that if the CS) group recognize that their arousal levels are depressed (by dint of missing their daily caffeine intake) then, relative to the CS+ group, they may feel less inclined to rate the CS flavor favorably. Consequently, a fragment of information about mood may be sufficient to produce a pattern of responding that is identical to what one might expect from associative learning. Although this interpretation is quite speculative, it is noteworthy that participants in the CS+ and CS) groups do experience an improved and a deteriorated mood state, respectively (Yeomans et al., 2000a, 2000b, 2001). Note also that this interpretation does not require both CS+ and CS) groups to acquire information. Instead, fragmentary information may influence responding in the CS) group, whereas in the CS+ group, increased affective responding might result from Ômere exposureÕ (Zajonc, 1968). In summary, compared with nutrient or flavor reinforcers, rather more is known about flavor preferences acquired using a caffeine reward. This probably reflects the fact that caffeine-based learning is a more reliable phenomenon. However, the use of between-subjects designs and limited awareness measurement mean that compelling evidence for learning outside awareness remains to be established. 2.3. Awareness and flavor–flavor learning There are surprisingly few published examples of flavor–flavor learning in humans. The first paper on this topic presented results from three separate studies, each exploring different aspects of the phenomenon (Zellner et al., 1983). Zellner et al. found that adding sucrose to particular teas resulted in greater liking for these teas when they were subsequently presented without sucrose. A measure of awareness was only included in one of these studies, and the precise details of the measure are not discussed. Nevertheless, Zellner et al. (1983) conclude that demand characteristics provide an unsatisfactory account of the results. More recently, Baeyens et al. have reached the same conclusion after exploring basic flavor–flavor learning (Baeyens et al., 1990a, 1995; See also Brunstrom et al., 2001), and also occasion setting (Holland, 1986) in flavor–flavor learning (Baeyens, Crombez, DeHouwer, & Eelen, 1996, 1998). One study in particular has been singled out for its rigorous approach to the measurement of awareness (Baeyens et al., 1990a). Indeed, Lovibond and Shanks (2002) note that this study ‘‘. . . provides one of the most intriguing pieces of evidence for conditioning without awareness. Efforts to replicate and explore the findings should be given high priority.’’ In one condition, one of two flavors (CSs) was paired systematically with either sugar or Tween (a bad tasting US). At the same time, the color of the CSs varied—each sample was presented in one of four different colors. At the end of the experiment the subjects were invited to answer one of the following questions: (a) sugar (or Tween) was most of the time presented with; flavor 1, flavor 2, color 1, color 2, color 3, or color 4? (the actual flavor and color names were inserted as appropriate) or (b) sugar (or Tween) did not covary systematically

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with any stimulus feature. None of the participants identified the correct CS used during conditioning, 54% selected (b), and the remaining 46% selected a color when they should have selected a flavor. Prima facie this appears to represent strong evidence that learning occurred in the absence of explicit knowledge. However, a nagging concern is the fact that the number of false hits (i.e., color selection) is well above what one might expect from chance responding. Scrutiny of the question reveals a reason why this might be the case. The participants were asked to choose between a color and a flavor in (a). My personal observations of responding in a similar context suggest that participants readily encode relationships between US flavors and colors. When questioned about contingency awareness they are likely to volunteer a response along the lines, ‘‘IÕm not exactly sure which flavor was paired most often, but I distinctly remember a red drink that tasted awful’’. If Baeyens et al.Õs, 1990a, 1990b participants applied this same line of reasoning, then this might explain why they readily picked a color. In so doing, their response was taken to imply a lack of awareness of a flavor–flavor pairing, when in fact, cognizance of this stimulus relationship was not rigorously assessed. A better approach might be to ask participants to choose between a list of flavors and colors in separate questions, thereby providing a more comprehensive assessment of the range of explicit information that can be acquired.

3. Implications for our understanding of dietary control Associative learning is thought to play a major role in dietary control (Mela, 1999). Flavor– postingestive learning is particularly important, because it enables us to select foods that are biologically ÔusefulÕ and to avoid foods that are potentially harmful. Since almost all flavor preferences are acquired (rather than innate), our preference for specific unhealthy foods is probably learned. In our ancient past, flavor–nutrient associations served us well because they created a propensity to seek out and ingest foods that offered protection against periods when food was scarce. Unfortunately, energy-dense food is now widely available in many societies. So, as fast food becomes cheaper and more readily available, our waistlines expand. As in other parts of the world, dietary behavior in the UK is an increasing cause for concern. In England, the prevalence of obesity has trebled since 1980 (National Audit Office, 2001). This is alarming, because obesity is recognized as a good predictor of illnesses such as heart disease, type II diabetes, and osteoarthritis. Programmes can be developed that try to promote normal eating behavior (e.g., Steinhardt, Bezner, & Adams, 1999). However, their value may be limited because our understanding of what constitutes ÔnormalÕ is not sufficiently advanced. In this regard, it is particularly curious that relatively little attention has been paid to understanding how unhealthy associations are formed and how awareness might interact with this process. If associations are found to configure outside awareness then learning may be established outside voluntary control. This is important, because evidence of this kind could shift the responsibility for overeating and obesity away from the individual and toward those agencies (e.g., parents, food manufacturers/outlets) that expose the individual to potentially damaging CS–US relationships. It might also help to clarify how conditioning can best be applied in practical domains, including clinical settings. Another possibility is that dietary learning gives rise to contingency awareness, but awareness is not causally related to the production of a conditioned response (Lovibond & Shanks, 2002). By this account, dietary learning might still be regarded as a

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process that occurs outside voluntary control. Conversely, dietary learning might be best described as propositional in nature, in which case contingency awareness is awarded causal status. This being the case, we should be interested to understand how higher-level cognitive activity interacts with the process. For example, how do beliefs and attitudes (possibly generated by advertising) about food influence the way that associations are formed and expressed? If learning does require cognizance of CS–US relationships, then we can also expect the process to vie with demands imposed by other everyday non-food-related tasks, activities, and day-to-day stresses. On occasions when we fail to attend to CS–US co-occurrences, we should expect learning to be impaired. This possibility should be taken seriously, because it might explain why human dietary learning paradigms are noted for their unreliability (Birch et al., 1990; Rozin, Wrzesniewski, & Byrnes, 1998; Zandstra, Stubenitsky, De Graaf, & Mela, 2002). Consistent with this idea, Brunstrom and Higgs (2002) gave participants a counting task to complete and at the same time presented a set of three different abstract patterns (CSs). Each pattern was paired differentially with a small chocolate reward (US), 90, 50, or 10% of the time. Following exposure to these CS–US pairings, pattern preferences shifted to become correlated with reward contingency. However, this evidence for conditioning was modulated by the complexity of the concurrent counting task—learning became less evident as the task became more complex. This was the case despite the fact that presentation of the evaluative stimuli remained unchanged. General decrements in cognitive performance have been found in individuals who are currently dieting to lose weight (Green & Rogers, 1993, 1995). This may result from a preoccupation with task-irrelevant cognitions that relate to food, body shape, and dieting (Green, Elliman, Rogers, & Welch, 1997; Green & Rogers, 1995, 1998). If dietary learning is found to require CS–US awareness, then it may be disrupted by this cognitive activity. This intriguing possibility warrants investigation, because it implies that aberrant dietary activity might actually vitiate a process that is otherwise important to normal dietary control. Consistent with this theorizing, Brunstrom et al. (2001) have found that restrained eaters may be insensitive to flavor–flavor learning. Similarly, Booth and Toase (1983) found that flavor–nutrient learning might be state dependent (hungry or replete) in successful dieters, but not in unsuccessful dieters. An equally interesting possibility is that conditioning is a higher-order cognitive activity that may be mediated by beliefs, expectations, and other cognitive states that are characterized by their accessibility to consciousness. If this is the case, then the efficacy of the US may be influenced by beliefs, health concerns, and other more complex values, associated with its ingestion. Whether dietary restraint results from, or causes, insensitivity to dietary learning is, at present, unclear. One possibility is that our proclivity to allocate attentional resource to particular CS–US relationships is formed during childhood. In accordance with this claim, it has been argued that pressurizing children to eat during a meal may engender insensitivity to hunger and satiety signals. Specifically, Carper, Fisher, and Birch (2000) have proposed that moving the locus of control to the parent inhibits the acquisition of key flavor–nutrient relationships. Consistent with this idea, children are able to use the energy density of a meal to control appropriate meal size (Birch & Deysher, 1986). However, the efficacy of this feedback appears to be greatly reduced when children are actively encouraged to eat until Ôthe plate is cleanÕ rather than attend to their own ÔinternalÕ signals (Birch et al., 1987). Interestingly, parents who closely control diet in this way also tend to have children who exhibit some of the aberrant dietary behaviors associated with dietary restraint and Ôexternal disinhibitionÕ (overeating) in adulthood (Carper et al., 2000).

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4. Awareness and learning in cognate research: Dietary learning in context As we have seen, in the majority of studies, awareness has not been explored systematically, and in some cases, it has been ignored. This failure is surprising, because psychologists have developed sophisticated tools for exploring awareness in other forms of learning that share characteristics with dietary learning. Researchers of dietary control have tended to pursue their interests in isolation. Yet in many ways, researchers of both dietary and non-dietary learning have much to gain from cross-fertilization of ideas. After a brief review of how issues of awareness have been treated in cognate research, we then consider how dietary learning compares with other forms of associative learning. This is then followed by some suggestions for future research. 4.1. Awareness and learning in cognate research The existence of implicit learning has now been debated for three decades. The term refers to the acquisition of new information despite having neither the intention to do so, nor the ability to recall what knowledge has been acquired. Typically, demonstrations of implicit learning have three phases: (a) exposure to an environment that has a stimulus structure that can be learned, (b) a measure of learning, often using a task to assess performance, where performance is directly related to learning, and (c) a measure of the extent to which an individual has explicit understanding of the underlying stimulus structure. Implicit learning is inferred when above-chance performance is observed in (b) but awareness is absent in (c). It has been explored from a variety of perspectives, including neuropsychological, connectionist, and empirical. Reber is credited with the earliest evidence for implicit learning. In his artificial grammar-learning task (Reber, 1967), participants were asked to memorize a set of letter strings generated by a finite-state grammar. After exposure to this task, they demonstrated a better-than-chance ability to categorize novel letter strings as being valid or invalid grammatical constructions. Despite this skill, participants were typically unable to report the rules of grammar that they were using in their categorization judgments. Since that time, implicit learning has been explored using a range of paradigms, including sequence learning tasks (Lewicki, Hill, & Bizot, 1988; Nissen & Bullemer, 1987) and tasks testing aptitude for controlling complex real-world environments (e.g., Berry & Broadbent, 1984; Broadbent, 1977). One potential problem with grammar-learning and other implicit-learning tasks is that above-chance classification of performance might not always require the rules of an implicit structure to be known. Instead, performance might be based on explicitly acquired instances or chunks of information, acquired during training (Dulany, Carlson, & Dewey, 1984; Perruchet, Gallego, & Savy, 1990; Perruchet & Pacteau, 1990; Servan-Schreiber & Anderson, 1990). To overcome this problem, it has been suggested that valid tests of awareness should involve forced-choice tests based on recognition, rather than on tests that require the participant to articulate the underlying structure (Reed & Johnson, 1994; Stadler, 1989; Willingham, Nissen, & Bullemer, 1989). It is fair to say that the field has been, and remains, divided over the central issue of whether learning involves unconscious cognition. Measurement of awareness has been critical because it is this dimension that may help to delineate separate learning processes. In this regard, an important contribution was made by Shanks and St. John (1994), who proposed two criteria that must be satisfied before it can be concluded that learning operates outside awareness. First, the test of

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awareness must probe the same information that is responsible for a change in performance. This is referred to as the information criterion. It addresses the problem that participants can form a conscious hypothesis that influences responding but which is not detected because the measure of awareness is directed towards a different but correlated set of hypotheses. Second, Shanks and St. John proposed a sensitivity criterion. This requires that the measure of awareness must be sensitive enough to detect all relevant conscious knowledge. It is needed, because an insensitive measure may falsely identify aware subjects as unaware. Shanks and St. JohnÕs criteria are controversial, because it is unclear how they might be satisfied within the context of most implicit learning paradigms. There has also been a tendency to reconsider the original problem in a way that circumvents the need for rigid criteria. For example, Cleermans (1997) has proposed that binary-state classifications (implicit or explicit) simply advocate a classical idea of cognitive modularity (Newell & Simon, 1972) that fails to capture the complexity of phenomena. Instead, we should embrace the possibility that learning may be more or less implicit and reject the notion of an Ôarchitectural dichotomy.Õ 4.2. ‘Biologically relevant’ associations In most implicit learning paradigms, stimulus relationships are potentially complex, making it difficult to identify what explicit information has actually been acquired. Conditioning paradigms, however, can be tightly controlled and constrained. Hence, they lend themselves to comprehensive assessment of awareness. For example, in the eyeblink conditioning procedure, participants are exposed to CSs such as lights or tones followed by a puff of air to one eye (US). Conditioning is assumed when the CS brings about an eyeblink when it is presented on its own. In this case, the explicit information that can be acquired is very specific. Hence, it can be assessed more easily. Despite numerous examples of this basic association (e.g., Lipp, Neumann, Siddle, & Dall, 2001; Steinmetz, Tracy, & Green, 2001; Thompson & Krupa, 1994), incontrovertible evidence for learning outside awareness has not been forthcoming. Rather, it would appear that it is the norm rather than the exception for learning to occur at least in conjunction with awareness of contingency relationships (Knuttinen, Power, Preston, & Disterhoft, 2001). A similar conclusion has been drawn after scrutinizing other examples of associative learning (see Lovibond & Shanks, 2002), including; autonomic conditioning (Dawson & Schell, 1985), conditioning in amnesiac patients (Clark & Squire, 1998), conditioning under anesthesia (Ghoneim, Block, & Fowles, € 1992), and conditioning with subliminal stimuli (Soares & Ohman, 1993). One exception to this rule may be evaluative conditioning (Martin & Levey, 1978). Evaluative conditioning is assumed to take place when the valence of a novel target (CS) comes to be altered by the valence (positive or negative) of stimuli or events (US) that are presented contingently or spatio-contiguously with the CS. Evaluative conditioning has been demonstrated successfully using a range of stimuli, including odors (Todrank, Byrnes, Wrzesniewski, & Rozin, 1995), faces (Baeyens, Eelen, & Van den Bergh, 1990b), pictures of outdoor sculptures (Hammerl & Grabitz, 1993, 1996), and tactile stimuli (Hammerl & Grabitz, 2000). Some studies suggest that learning is not predicated on contingency awareness (De Houwer, 2001; Fulcher & Hammerl, 2001a, 2001b; Hammerl & Grabitz, 1993, 1996). On this basis, learning has been described as primitive, automatic, and possibly based on a simple Hebbian learning rule (Baeyens, Crombez, Hendrickx, & Eelen, 1995, 1998). This referential process complements a functionally different expectancy

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system in which a CS comes to generate a prediction of what is actually going to occur in future. In the referential system, the CS merely elicits a representation of the US, along with its associated valance. The research community is still divided over the plausibility of this process (De Houwer et al., 2001; Field, 2000a, 2000b; Lovibond & Shanks, 2002). However, even its most ardent critics concede that a potential exception to this rule might be where biologically relevant USs are used (Field, 2000b; Lovibond & Shanks, 2002; Shanks & St. John, 1994). In relation to dietary learning, this is important because flavor–flavor learning has been regarded as an example of evaluative conditioning (Baeyens et al., 1990a; Brunstrom et al., 2001; De Houwer et al., 2001). Baeyens et al.Õs (1990a) account of flavor–flavor learning has been regarded as an example where the information and the sensitivity criterion are satisfied reasonably well (Field, 2000b; Lovibond & Shanks, 2002; Shanks & St. John, 1994). We have already explored reasons why this optimism may not be entirely justified. However, more recently, another important source of evidence has emerged that also supports the idea that biologically relevant stimuli should be treated as special. In a series of studies, Stevenson et al. have shown that basic taste properties can become conditioned by odors, possibly via a configural process, whereby the odor and taste fuse to form a single perceptual experience. For example, Stevenson, Prescott, and Boakes (1995) and Stevenson, Boakes, and Prescott (1998) report that an odor smells sweeter after being paired with sucrose and more sour after it is paired with citric acid. This phenomenon does not appear to be restricted to special circumstances in which taste and smell become confused since the perceptual quality of an odor also appears to be modified when it is mixed with another odor (Stevenson, 2001). Importantly, the extent to which odor–taste or odor–odor learning takes place appears to be unrelated to the whether participants are classified as aware or unaware. Unlike previous examples of evaluative conditioning, Stevenson et al.Õs claims about the implicit nature of conditioned odor quality have not been questioned, partly because they have used a highly sensitive and novel test of knowledge. Moreover, this kind of learning appears to have another unusual characteristic that has been used previously to distinguish evaluative conditioning from other forms of expectancy or signal learning. In addition to operating outside contingency awareness, evaluative conditioning may be special because it is highly resistant to extinction (Baeyens, Crombez, Vandenbergh, & Eelen, 1988, 1989, 1990b, 1995, 1988). This claim contrasts the hitherto universal finding from human conditioning studies that extinction typically occurs within a few trials (e.g., Dawson & Schell, 1985). Likewise, Stevenson, Boakes, and Wilson (2000) have shown resistance to extinction in conditioned odor perceptions. On this basis, Stevenson et al. have argued that flavor–flavor learning and odor– quality conditioning may share a common set of functional characteristics. The extent to which this should be taken as evidence for a common underlying mechanism remains to be established. However, it is consistent with the idea that these kinds of biologically relevant stimuli should be treated as special and can configure outside contingency awareness. The general idea that biologically relevant stimuli combine in an unusual way can also be extended to flavor–postingestional learning. This type of learning is quite remarkable because it takes place even though there is a long delay between exposure to the CS and detection of the US in the gut (compare this with most other learning paradigms that require temporal contiguity in the range 500–2000 ms). This extended period also makes it difficult to imagine what form contingency awareness might actually take. As we have seen, researchers of flavor–nutrient learning have barely begun to consider this issue. However, clues from the literature on food-aversion learning would seem consistent with the idea that this form of dietary learning can also configure

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outside awareness. Earlier, we noted that food aversion learning occurs when an organism learns to avoid the taste or odor of a particular food (CS) after it has been paired with a treatment that induces transient illness (US) (Garcia & Koelling, 1966). Food aversion learning may not require contingent experience of the US (i.e., experience of nausea). Thus, rats will acquire aversions even when they are completely anesthetized both during and after the time that the US is administered (Bermudez-Rattoni, Forthman, Sanchez, Perez, & Garcia, 1988; Roll & Smith, 1972; although see Pelchat & Rozin, 1982). Even more striking, in humans, a food aversion may be formed when a food is eaten shortly before and chemotherapy, even when the person is aware that the target food was not the cause of their illness (Bernstein, 1978; Logue, Ophir, & Strauss, 1981). All forms of dietary learning do a similar job, they modulate our approach or avoidance of specific food. However, it is remarkable, and perhaps more than coincidental, that they are all quite unlike ÔtraditionalÕ forms of expectancy learning. It is also curious that these biologically relevant associations all appear to represent plausible candidates for learning outside awareness. These differences are potentially bewildering if we assume a single associative mechanism. Instead, it might make more sense to assume that separate subsystems have evolved in response to particular needs (for a related point see Garcia, Brett, & Rusuniak, 1989). Indeed, one suggestion is that dietary learning is so important that it makes sense to learn in a spontaneous way and this may best be achieved by learning without conscious control (Field, 2000a, 2000b). 4.3. Directions for future research Initially, priority should be given to paradigms where it is possible to limit the opportunity to acquire explicit information. In the standard flavor–flavor learning paradigm this is more difficult because the CS and US are contiguous and so the opportunity to acquire a simple rule that describes stimulus relationships is considerable. Baeyens et al. (1990a) have previously masked flavor–flavor relationships by varying the color of their samples. However, as we have seen, the effectiveness of this approach remains to be established. Other promising alternatives include using paradigms based around the Implicit Association Task (Mitchell, Anderson, & Lovibond, 2003), which can be used to eliminate demand characteristics, or studying conditioning in amnesic patients (Johnsrude, Owen, White, Zhao, & Bohbot, 2000). A different strategy might be to explore learning based on postingestional consequences. This is because the CS and US are temporally separate and hence rule extraction is less likely. However, this advantage is lost if the nutrient source is also detected in the mouth (thereby making the US and CS more proximate). One way to address this problem might be to refine methods of US delivery. For example, calories might be swallowed encapsulated in a set of capsules that open in the stomach. Alternatively, it might be more appropriate to bypass the oral cavity altogether by delivering a nutrient source via an intragastric infusion (see French & Cecil, 2001; Lavin, French, Ruxton, & Read, 2002). Albeit possible, it is still a technical challenge to deliver a food reward without the participant acquiring any immediate information (e.g., about volume, density, color, and so on), even when the food is encapsulated or infused. Therefore, an alternative first step might be to explore awareness in caffeine-based learning, since this compound can be delivered unobtrusively, in a single dose. Owing to recent theoretical advances, the reinforcing properties of caffeine are also better understood, which makes it easier to design experiments that can elicit robust examples of learning.

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To date, Shanks and St. JohnÕs (1994) criteria have tended to be applied in non-associative learning paradigms (e.g., rule abstraction in complex systems). However, they can also be useful in studies of dietary learning. One starting point might be to consider an experiment in which participants are exposed to a novel caffeine-paired drink (A) and a novel placebo-paired drink (B), on alternate days. Assessment of awareness might then be relatively straightforward. This is because contingency awareness can exist in only one of two forms: knowledge of a CS given a particular US, or knowledge of a US given a particular CS. The first of these can be established by giving participants either caffeine or placebo and then asking them to pick the drink (A or B) that was previously paired with how they feel at that moment in time. Likewise, the ability to recognize a US when given a CS can be explored by giving participants caffeine or placebo and then asking them to confirm (yes/no) whether they remember a correspondence between how they felt last time they consumed one of the drinks and how they currently feel. Both of these measures represent a test of memory recognition rather than memory recall. Therefore, neither relies on the participantÕs ability to articulate complex visceral or sensory information. Since recognition measures are highly effective at probing explicit knowledge (Dawson & Reardon, 1973), they satisfy the sensitivity criterion. Together, both relate to the kind of explicit information that might account for differential responding. Therefore, they also go some way towards satisfying Shanks and St. JohnÕs information criterion. One omission here is the possibility that learning takes place after a period of rationalization along the lines, ‘‘I feel more alert today and this probably has something to do with this drink. Therefore, I should probably like it more.’’ Information of this kind could be assessed by questionnaire. If learning is evidenced in participants who are classified as unaware on all of these measures, then this would provide a reasonable initial basis for a claim that associations have configured outside awareness.

5. General summary For students of dietary learning the issue of awareness may seem arcane. In this paper, however, I have attempted to show that theoretical advance will be limited until we are able to resolve whether contingency awareness is a necessary component of the phenomenon that is under scrutiny. This is because a resolution to this issue might lead to one of two conclusions—either dietary learning is automatic and involuntary, or it interacts with other conscious cognition and hence it can be governed by intentional control. If unconscious learning takes place, then dietary learning should be regarded as a very rare form of learning, and it could be of interest to a broad constituency of researchers with interests in implicit learning. Alternatively, evidence that learning interacts with intentional control could pave the way for research that elucidates the factors that determine when learning will or will not occur. It might even generate a novel theoretical context within which to predict those types of environment that engender the development of aberrant eating behaviors. Examples of dietary learning are not hard to find in humans. Indeed, we seem to have an effortless ability to offer up detailed evaluative opinion on foods and beverages that we like and dislike, and we readily develop preferences for foods that are high in energy density. This contrasts the paucity of laboratory-based examples of this phenomenon. As we have seen, most researchers would argue that learning is free from awareness. However, for the most part, this

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