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A Role for Sweet Taste: Calorie Predictive Relations in Energy Regulation by Rats


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A Role for Sweet Taste: Calorie Predictive Relations in Energy Regulation

by Rats

 

Susan E. Swithers and Terry L. Davidson

 

Purdue University

 

 

Animals may use sweet taste to predict the caloric contents of food. Eating sweet noncaloric substances

may degrade this predictive relationship, leading to positive energy balance through increased food intake

and/or diminished energy expenditure. These experiments were designed to test the hypothesis that

experiences that reduce the validity of sweet taste as a predictor of the caloric or nutritive consequences

of eating may contribute to deficits in the regulation of energy by reducing the ability of sweet-tasting

foods that contain calories to evoke physiological responses that underlie tight regulation. Adult male

Sprague?Dawley rats were given differential experience with a sweet taste that either predicted increased

caloric content (glucose) or did not predict increased calories (saccharin). We found that reducing the

correlation between sweet taste and the caloric content of foods using artificial sweeteners in rats resulted

in increased caloric intake, increased body weight, and increased adiposity, as well as diminished caloric

compensation and blunted thermic responses to sweet-tasting diets. These results suggest that consumption of products containing artificial sweeteners may lead to increased body weight and obesity by interfering with fundamental homeostatic, physiological processes.

 

Keywords: learning, energy balance, cephalic-phase responses, thermic effect of food. One of the most well-established concepts in psychology is that animals can detect and learn about relationships among the events they experience and that sensitivity to these relationships can be indexed by changes in behavioral and physiological responses (e.g., Dworkin & Dworkin, 1995; Pavlov, 1927; Siegel, 2005). The

purpose of the present research is to explore some of the implications of a Pavlovian analysis for understanding the control of food intake and body weight. During the past 30 years, the incidence of

people who are overweight or obese has increased dramatically both in the U.S. and throughout the rest of the world (Flegal, 2005; Rigby, Kumanyika, & James, 2004). Although much research has

focused on identifying disturbances in the neural and hormonal mechanisms involved with maintaining energy balance, the rate of body-weight gain during this period suggests that the current

obesity epidemic has environmental origins (Hill & Peters, 1998; Lowe, 2003; Nicklas, Baranowski, Cullen, & Berenson, 2001).

 

One change in the food environment that is correlated with current obesity trends is the wide scale introduction of noncaloric, high-intensity sweeteners (The Freedonia Group, 2001; Schiweck,

1999; Zuckerindustrie, 1999). In nature, sweetness might be described as a salient orosensory stimulus that is a highly valid predictor of the postingestive caloric consequences of eating, as humans and other animals repeatedly encounter, beginning very early in life (e.g., at first contact with breast milk), naturally sweet foods that are more calorically dense than less sweet foods. With the growing use of noncaloric sweeteners in the current food environment, millions of people are being exposed to sweet tastes that are not associated with caloric or nutritive consequences. On the basis of Pavlovian conditioning principles, we suggested that a consequence of this type of exposure might be impaired energy regulation (Davidson & Swithers, 2004; Swithers & Davidson, 2005b).

 

It is well established that orosensory cues (e.g., the taste, flavor, texture of food) can be quickly and strongly associated with the postingestive consequences of eating. For example, the phenomenon

of conditioned taste aversion demonstrates that animals will rapidly learn to avoid consuming a taste that is paired with gastric malaise (Welzl, D?Adamo, & Lipp, 2001). More recent studies indicate that animals also readily associate orosensory cues with the postingestive caloric or nutritive consequences of eating (e.g., Sclafani, 1997, 2001).

 

Sweet tasting substances have also been identified as strong elicitors of a number of preabsorptive (e.g., hormonal, thermogenic, metabolic) ?cephalic-phase? reflexes related to ingestion (Berthoud, Trimble, Siegel, Bereiter, & Jeanrenaud, 1980; Bruce, Storlien, Furler, & Chisholm, 1987; Teff, Devine, & ngelman,

1995; Tordoff, 1988). Functionally, cephalic-phase reflexes are thought to anticipate and prepare for the arrival of nutrients in the gastrointestinal tract, thereby increasing the efficiency of nutrient utilization and minimizing the degree to which those nutrients perturb homeostasis by producing positive energy balance(Mattes, 1997; Powley & Berthoud, 1985; Teff, 2000). By some accounts (e.g., Woods & Ramsay, 2000), even small changes in the evocation of these cephalic-phase responses could produce changes in

Susan E. Swithers and Terry L. Davidson, Department of Psychological Sciences, Ingestive Behavior Research Center, Purdue University.

 

The research was supported by National Institutes of Health Grants R01HD44179, R01HD29792, and R01DK76078 and by a grant from the Purdue Research Foundation. We thank Melissa McCurley, Jennie Mak, and Lindsey Schier for technical assistance. Correspondence concerning this article should be addressed to Susan E. Swithers, Department of Psychological Sciences, Ingestive Behavior Research

Center, Purdue University, 703 Third Street, West Lafayette, IN 47907. E-mail: swithers@purdue.edu

Behavioral Neuroscience Copyright 2007 by the American Psychological Association

2007, Vol. 00, No. 0, 000?000 0735-7044/07/$12.00 DOI: 10.1037/0735-7044.00.0.0001

 

the efficiency of energy utilization, leading to significant longterm increases in food intake and body weight (Cooling & Blundell, 2000).

 

If the efficiency of energy regulation depends, at least in part, on the elicitation of cephalic-phase responses, and if the elicitation of cephalic-phase responses depends, in part, on the ability of sweet tastes to signal calories, then experiences that weaken this signaling or predictive relationship might also disturb the control of food intake and body weight. The present research evaluated this basic hypothesis. We manipulated the strength of this relationship by exposing rats to sweet tastes that were either highly valid predictors of, or did not predict, an increase in the caloric content of plain yogurt. In our studies, rats were given both plain unsweetened and sweetened yogurt. When the yogurt was sweetened with glucose, the sweet taste was a valid predictor of increased calories, whereas when the yogurt was sweetened with nonnutritive saccharin, the sweet taste was not predictive of increased calories. The total

volume of yogurt consumed was equated across groups by offering identical quantities and by excluding rats that failed to consume at least 70% of the yogurt diets offered. As a result, although the predictive group received more calories from the yogurt supplements than did the nonpredictive group (because of the caloric sweetener), the hypothesis was that the nonpredictive group would demonstrate increased food intake and body weight gain and decreased caloric compensation relative to that seen in the predictive group. Experiment 1 examined the effects of varying the strength of the predictive relationship between sweet taste and the caloric consequences of eating on food intake, body weight, and body adiposity. Experiment 2 assessed the effects of this manipulation on the ability of rats to compensate for calories consumed on one occasion by reducing intake at a subsequent meal. Experiment 3 assessed whether or not the increment in core body temperature produced by food intake (i.e., the thermic effect of

food; de Jonge & Bray, 1997, 2002) can be modified by prior exposure to a nonpredictive relationship between sweet tastes and calories.

 

Experiment 1

Method

Subjects were adult male Sprague?Dawley rats (Harlan, Indianapolis,

IN) weighing 375?425 g at the start of testing. Rats were

housed individually in hanging wire cages with pellet laboratory

chow (Lab Diets 5001) and water available ad libitum except

during testing as described below. The colony room was maintained

on a 14:10-hr light? dark cycle with lights on at 7:00 a.m.

Dietary supplements were provided at approximately 11:00 a.m.

each day. Temperature in the colony room was maintained at

21?23 ?C.

Dietary manipulation. During testing, rats received 30 g of

low-fat, plain yogurt (Dannon, Allentown, PA) daily for 6 days per

week in addition to ad-lib lab chow and water. On the 7th day, only

lab chow and water were provided. Yogurt diets and lab chow

were provided in small enamel camping cups attached to the inside

of the cage. Diets were available for approximately 23 hr per day,

and yogurt and chow intake were recorded daily by weighing the

cups, with chow intake adjusted for spillage; Rats were weighed

daily and were randomly assigned to one of three yogurt diet

conditions: Rats in the sweet predictive group received plain,

unsweetened yogurt (0.6 kcal/g) for 3 of the 6 days each week

that yogurt was provided and received yogurt sweetened with 20%

glucose (wt/wt; 1.2 kcal/g) for the other 3 days that week. Thus,

for rats in the sweet predictive group, the sweetened diet was

associated with more calories than was the unsweetened diet. Rats

in the sweet nonpredictive group received plain, unsweetened

yogurt for 3 of the 6 days each week that yogurt was provided and

received yogurt sweetened with 0.3% saccharin for the other 3

days that week. Thus, for rats in the sweet nonpredictive group, the

sweetened diet was not associated with more calories than was the

unsweetened diet. Across each week of testing, the sweet predictive

group received a total of approximately 162 kcal per week

from the yogurt supplements, whereas the sweet nonpredictive

group received a total of approximately 104 kcal per week from

the yogurt supplements. Therefore, a third group, the sweet predictive

control group, was included to control for the total number

of calories from the yogurt supplement per week (approximately

104 kcal per week). This group received only yogurt sweetened

with 20% glucose (wt/wt; 1.2 kcal/g) on the 3 days per week

that rats in the sweet nonpredictive group received sweetened

yogurt (i.e., this group did not receive any unsweetened yogurt).

The order of presentation of the sweetened and unsweetened diet

was randomized each week.

After excluding rats that did not consume at least 70% of the

yogurt, there were eight rats in the sweet predictive group, 9 rats

in the sweet nonpredictive group, and 10 rats in the sweet predictive

control group. Rats received the yogurt diet supplements for 5

weeks. At the end of 5 weeks, rats were lightly anesthetized with

ketamine and xylazine (10 mg/kg?70 mg/kg ip), and body composition

was determined using dual-energy x-ray absorptiometry

(DEXA; pDEXA Sabre, Norland, Cranberry, NJ), which has been

used and validated in multiple species as a reliable estimate of fat

and lean mass (e.g., 8, 11, 12, 70)

Statistical analysis. The principal outcome measure in this

study was cumulative body weight gain, which was analyzed at

the end of each of the 5 weeks of training with a two-way,

repeated measures analysis of covariance (ANCOVA) using

group as a between-subjects factor, exposure week as a withinsubjects

factor, and initial body weight as the covariate, followed

by one-way ANCOVAs on each week. In addition,

consumption of the sweetened and unsweetened yogurts were

analyzed using separate three-way (Group Yogurt Type

[sweetened vs. unsweetened] Week) repeated measures analyses,

with unsweetened yogurt consumption assessed for only

the sweet predictive and sweet nonpredictive groups (as sweet

predictive control rats did not receive unsweetened yogurt).

Total caloric intake at the end of each of the 5 weeks of training

was analyzed with separate two-way, repeated measures analyses

of variance (ANOVAs) using group as a between-subjects

factor and exposure week as a within-subjects factor. In addition,

total cumulative caloric intake across all 5 weeks of

training was analyzed with a one-way ANOVA. Adiposity,

expressed as percent fat derived from DEXA analysis, was

analyzed with a one-way (group) ANOVA. Post hoc tests were

done using Newman-Keuls tests where indicated, and p .05

was taken as significant for all analyses.

2 SWITHERS AND DAVIDSON

Results

Body weights across predictive (404 5 g), predictive control

(394 3g) and nonpredictive (398 4 g) groups did not differ at

the start of the experiment, F(2, 24) 1.62, p .22. An

ANCOVA indicated that during the 5 weeks of training, body

weight gain was significantly predicted by the covariate (starting

body weight)?main effect of body weight, F(1, 23) 9.19, p

.05. In addition, the diet group and the week of exposure significantly

affected cumulative body weight gain (see Figure 1)? main

effect of group, F(2, 23) 4.44, p .05; main effect of week, F(4,

92) 3.92, p .05; Week Starting Body Weight interaction,

F(4, 92) 6.55, p .05; Week Group interaction, F(8, 92)

2.34, p .05. Post hoc analyses using one-way ANCOVAs

revealed that during the first week, there were no significant

effects of group on body weight gain. However, during Weeks 2,

3, and 5, rats in the sweet nonpredictive group had gained significantly

more weight than did rats in either the sweet predictive or

sweet predictive control groups.

Analysis of mean food intake per week during the 5 week

training period revealed that yogurt consumption varied by week,

but not by group (see Figure 2)?main effect of week, F(4, 96)

9.45, p .05. An effect of week, but not of group, was significant

when total caloric intake per week (yogurt plus chow) was evaluated

across the 5 weeks of consumption, with intake during the

1st week significantly lower than intake during the remaining 4

weeks (see Figure 3)?main effect of week, F(4, 96) 9.58, p

.05. Differences due to group also failed to achieve significance

when cumulative caloric intake was calculated across the entire 5

week training period (predictive 3,837 109 kcal, predictive

control 3,809 75 kcal, nonpredictive 3,986 67 kcal), F(2,

24) 1.33, p .28.

Because of technical difficulties, DEXA analysis was not completed

on 3 rats (1 sweet predictive control rat and 2 sweet

nonpredictive rats). Analysis of the remaining rats indicated that

body fat composition was significantly affected by group (see

Figure 4)?main effect of group, F(2, 21) 7.27, p .05, with

sweet nonpredictive rats having significantly greater adiposity than

that seen in sweet predictive and sweet predictive control rats.

 

Experiment 2

Caloric compensation involves the ability to adjust for excess

calories consumed on one occasion by reducing intake at other times

(Booth, 1972; Foltin, Fischman, Moran, Rolls, & Kelly, 1990; Mattes,

1996; Mazlan, Horgan, & Stubbs, 2006; Rowland, Nasrallah, &

Robertson, 2005). Weakened caloric compensation could therefore

result in positive energy balance and increased tendencies toward

being overweight and toward obesity. Experiment 2 examined

whether or not the strength of the predictive relationship between

sweet taste and calories could influence the strength of caloric compensation.

One group of rats was trained under conditions in which

sweet taste was a highly valid predictor of increased calories, and

another group was trained under conditions in which sweet taste did

not predict more calories. After completion of this training, both

groups consumed a small amount of a novel high-calorie sweet tasting

premeal. The ability to compensate for the calories contained in this

premeal by reducing their caloric intake of lab chow at a subsequent

test meal was compared for the two groups.

Method

The subjects were adult male Sprague?Dawley rats (Harlan)

weighing 300?350 g at the start of testing. Rats were housed

individually in hanging wire cages with pellet laboratory chow

(Lab Diets 5001) and water available ad libitum except during

testing as described below. The colony room was maintained on a

14:10-hr light? dark cycle, with lights on at 7:00 a.m. Dietary

Weeks

1 2 3 4 5

Cumulative Body Weight Gain (g)

0

20

40

60

80

100

Non-Predictive

Predictive

Predictive Control

*

*

*

Figure 1. Cumulative body weight gain across 5 weeks exposure to sweet predictive, sweet nonpredictive, or

sweet predictive control diets. Error bars represent standard error. *p .05.

SWEET TASTE CUES AND ENERGY DYSREGULATION IN RATS 3

supplements were provided at approximately 11:00 a.m. each day.

Temperature in the colony room was maintained at 21?23 ?C.

During testing, premeals were provided at 11:00 a.m.

Training. During training, rats received 30 g of a low-fat, plain

yogurt (Dannon) daily for 14 days in addition to ad-lib lab chow and

water. An exposure of 14 days was chosen because significant effects

on body weight were observed after 2 weeks exposure in Experiment

1. Diets were available for approximately 23 hr per day, and yogurt

and chow intake were recorded daily by weighing the cups, with chow

intake adjusted for spillage. Rats were weighed daily and were randomly

assigned to one of two yogurt diet conditions as described for

Experiment 1. Rats in the sweet predictive group (n 13) received

plain, unsweetened yogurt (0.6 kcal/g) for 7 of the 14 days and

yogurt sweetened with 20% glucose (wt/wt; 1.2 kcal/g) for 7 of the

14 days. Rats in the sweet nonpredictive group (n 12) received

plain, unsweetened yogurt for 7 days and yogurt sweetened with 0.3%

saccharin for 7 days. The order of presentation of the sweetened and

unsweetened diet was semirandomized such that no rat received the

same yogurt (sweetened or unsweetened) more than 3 days in a row.

At the end of training, total consumption of the sweetened and

unsweetened diets for each group of rats was determined, and rats that

failed to consume at least 70% of the yogurt diets were excluded from

analysis, resulting in final sample sizes of 11 rats in the predictive

group and 9 rats in the nonpredictive group.

Testing. Following the 14 days of daily yogurt consumption,

rats were given 1 day of chow and water alone?the chow was

then removed overnight. Half of the rats in each group were then

offered a premeal of 5 g of a novel sweet diet, Chocolate Ensure

Plus, thickened with 2% guar to approximate the viscosity of

yogurt (1.4 kcal/g). The premeal was provided for 30 min; the

remaining rats were given no premeal. Chow was then returned to

all rats, and chow intake was measured after 1, 2, 4, and 24 hr. Rats

then received 3 days of chow and water alone; the chow was then

removed overnight, and the rats were tested with the premeal

conditions reversed. The order of testing was counterbalanced.

Statistical analysis. Intake of yogurt diets and total caloric

intake during the 14-day training period were analyzed with separate

one-way ANOVAs. Body weight gain during the 14-day

training period as analyzed with a one-way ANCOVA with initial

body weight as the covariate. Premeal intake was analyzed with a

one-way ANOVA. Chow intake following premeal was initially

assessed using a four-way (Testing Order Premeal Training

Diet Time) ANOVA. There were no significant main effects or

interactions of testing order (premeal vs. no premeal)?therefore,

the data were collapsed across testing order and analyzed with a

three-way ANOVA (Premeal Training Diet Time). Post hoc

tests were done using Newman-Keuls tests where indicated, and a

p .05 was taken as significant for all analyses.

Results

Even after excluding rats that did not consume at least 70% of

the yogurt diet offered, there were significant differences in the

quantity of unsweetened and sweetened diets consumed by rats in

the sweet predictive and sweet nonpredictive groups during training?

main effect of yogurt type, F(1, 18) 13.03, p .05; Yogurt

Type Group interaction, F(1, 18) 5.79, p .05. Post hoc

analyses revealed that rats in the sweet predictive group consumed

Figure 2. Sweetened (A) and unsweetened (:thumb: yogurt intake across 5 weeks of exposure. Yogurt intake was

significantly lower during the 1st week than for all other weeks. Error bars represent standard error.

4 SWITHERS AND DAVIDSON

significantly more sweetened yogurt than unsweetened yogurt

(208 4 g vs. 177 7 g; M SEM); there were no significant

differences in yogurt intake between the predictive and nonpredictive

groups (187 4 g sweetened and 181 8 g unsweetened

yogurt in the nonpredictive group). Total caloric intake at the end

of training (chow plus yogurt) was significantly affected by group

(see Figure 5), main effect of group, F(1, 18) 5.99, p .05, with

sweet nonpredictive rats consuming significantly more calories

over the course of training than did sweet predictive rats. Body

weights at the start of training did not differ across the groups

Figure 3. Total energy intake (chow plus yogurt diets) across 5 weeks of yogurt diet consumption. Energy

intake was significantly lower during Week 1 than for all other weeks. Error bars represent standard error.

Figure 4. Adiposity as determined by dual-energy x-ray absorptiometry analysis. Error bars represent standard

error. *p .05.

 

SWEET TASTE CUES AND ENERGY DYSREGULATION IN RATS 5

(330 5 g vs. 325 5 g, predictive vs. nonpredictive, respectively),

F(1, 18) 1. During training, sweet nonpredictive rats

gained significantly more weight than did sweet predictive rats

(see Figure 6)?main effect of group, F(1, 17) 7.31, p .05; no

effect of initial body weight, (F 1).

During testing, there were no significant differences in the

quantity of premeal consumed (7.0 0.2 kcal for the predictive

group and 6.6 0.2 kcal for the nonpredictive group), F(1, 18)

2.7, p .12. However, chow intake following the premeal was

significantly affected by training history, premeal, and time of

Figure 5. Total energy intake during 14 days of consumption of sweet predictive or sweet nonpredictive yogurt

diets in Experiment 2. Error bars represent standard error. *p .05.

Figure 6. Body weight gain during 14 days of consumption of sweet predictive or sweet nonpredictive yogurt

diets in Experiment 2. Error bars represent standard error. *p .05.

6 SWITHERS AND DAVIDSON

test?main effect of group, F(1, 18) 12.51, p .05; main effect

of time, F(3, 54) 1,833, p .05; Premeal Group interaction,

F(1, 18) 6.18, p .05. Post hoc analyses revealed that rats in

the sweet predictive group consumed significantly less chow on

the day they consumed the premeal than they did on the day they

did not consume the premeal (see Figure 7a). In contrast, there

were no significant differences in chow intake in rats in the sweet

nonpredictive group on the test day when the premeal was consumed

or on the test day when no premeal was consumed (see

Figure 7b). In other words, sweet predictive rats showed caloric

compensation for novel sweet-tasting calories by decreasing subsequent

chow intake, whereas sweet nonpredictive rats did not.

These results confirm and extend preliminary findings we reported

with rats that were exposed to calorically and noncalorically

sweetened fluids (Davidson & Swithers, 2004)

 

Experiment 3

Ingestion of food evokes a reflexive thermogenic response (Jequier,

1983; Tappy, 1996), and this form of heat production

appears to be mediated in both humans and animals by preabsorptive

(e.g., orosensory) food cues. For example, in humans and in

sham-feeding dogs, when food is tasted but not swallowed, the

thermic response can exceed that produced by a normal meal

(Diamond, Brondel, & LeBlanc, 1985; LeBlanc & Cabanac, 1989).

In contrast, for both humans and dogs, when nutrients bypass the

oropharyngeal cavity (e.g., via gavage or feeding tube), cephalicphase

thermogenic responses are either not observed or are much

weaker than those produced by normal feeding (Diamond et al.,

1985; LeBlanc, Cabanac, & Samson, 1984). If sweet tastes evoke

thermic responses based, in part, on the degree to which they

predict the arrival of calories in the gut, one might expect that

tasting a sweet, high-calorie food would evoke a greater increase

in core body temperature for rats that have been exposed to a

highly reliable predictive relationship between sweet tastes and

calories than it would in rats that that have not been exposed to this

relationship. Experiment 3 tested this prediction.

Method

Subjects were 16 adult male Sprague?Dawley rats (Harlan) weighing

375?425 g at the time of surgery; these rats had previously been

used in an unrelated study that did not involve provision of diets other

than standard rat chow. Rats were housed individually in polycarbonate

cages lined with aspen bedding to allow for continuous monitoring

of core body temperature and gross motor activity. Laboratory chow

(Lab Diets 5001) and water were available ad libitum except during

testing as described below. The colony room was maintained on a

14:10-hr light?dark cycle, with lights on at 7:00 a.m. Dietary supplements

were provided at approximately 12:40 p.m. each day. Temperature

in the colony room was maintained at 21?23 ?C.

Surgery. For remote monitoring of core body temperature and

activity, intraperitoneal implantation of transmitters for a battery-free

radiotelemetry system (HR E-Mitters; PDT-4000 HR; Mini Mitter,

Sun River, OR) was performed after the rats were anaesthetized with

xylazine/ketamine (10 mg/kg/90 mg/kg ip) following procedures similar

to those described elsewhere (Harkin, O?Donnell, & Kelly, 2002).

After the rat was flaccid and unresponsive to foot-pad pinch, the

ventral surface of the abdomen was shaved. The shaved area was

swabbed with Betadine, and the rat was placed on a warm, sterile

Figure 7. Chow intake following a novel sweet premeal or no premeal in sweet predictive (A) or sweet

nonpredictive (:lol: groups. Error bars represent standard error. *p .05.

SWEET TASTE CUES AND ENERGY DYSREGULATION IN RATS 7

surgical surface. The abdomen was opened by making a 2-cm incision

below the diaphragm along the white line of fascia where the abdominal

muscles join on the midline. The E-mitter was inserted into the

abdominal cavity and was positioned dorsal to the digestive organs

and in front of the caudal arteries and tethered to the muscle wall with

a single stay suture. The abdominal cavity was then massaged gently

to allow the internal organs to settle in place before nondissolvable

suture was used to close the muscle layer of the abdominal incision.

The skin layer of the abdominal opening was then closed using

dissolvable suture in combination with stainless steel wound clips.

Rats were allowed to recover for 2 weeks prior to the start of yogurt

exposure.

Training and testing. At the start of training exposures, rats

were matched on baseline core body temperature and assigned to

sweet predictive or sweet nonpredictive groups as in Experiment 2.

Training was identical to Experiment 2. After training, all the rats

were tested following overnight food deprivation with a premeal of

5-g thickened Chocolate Ensure Plus. During training, one rat in

the sweet nonpredictive group failed to consume at least 70% of

the diet, and we removed its data from analysis. In addition, a

transmitter in one of the sweet nonpredictive rats failed during

training. Final sample sizes were 8 rats in the sweet predictive

group and 6 rats in the sweet nonpredictive group. During training

and testing, a receiver placed under each cage monitored transmitter

output of the implanted emitters. Both temperature and activity

data were collected at 1-min intervals using the VitalView dataacquisition

system (Mini Mitter; see Harkin et al., 2002). Temperature

and activity readings were averaged over 5-min time intervals

30 min prior to and during presentations of taste stimuli

(premeal) and during short-term (0-4 hr) food-intake testing. To

minimize disturbances to the rats during collection of body temperature

and activity data, we did not measure food intake.

Statistical analysis. Temperature and activity data from the 2

min prior to presentation of the yogurt diets during training or prior to

the premeal during testing were averaged to determine baseline body

temperatures and activity. Departures from baseline were analyzed

every 5 min for 60 min following presentation of the yogurt diets

during training and every 5 min for 30 min following presentation of

the premeal during testing. Effects of consumption of sweetened and

plain yogurt on core body temperature during training were examined

using three-way, ANOVAs (Yogurt Type [sweetened vs. unsweetened]

Group Time (5-min period after presentation of the

premeal). Effects of predictive and nonpredictive training history on

core body temperature and activity during testing were examined with

a separate two-way, repeated measures ANOVA, with training group

treated as a between-subjects variable and with time after premeal

presentation treated as a within-subjects variable. Post hoc analyses

were done using Newman-Keuls tests where indicated, and p .05

was taken as significant for all tests.

Results

During training, departures from core body temperature during

the first 60 min following presentation of the diet were affected by

time after presentation of yogurt, the type of yogurt presented, and

the training group (see Figure 8)?main effect of time, F(11,

132) 82.5, p .05; Time Group interaction, F(11, 132)

2.39, p .05; Yogurt Type (sweet vs. plain) Time interaction,

Figure 8. Changes in core body temperature over the first 60 min following yogurt presentation during sweet

predictive (A) or sweet nonpredictive (B) training. Error bars represent standard error. *p .05.

8 SWITHERS AND DAVIDSON

F(11, 132) 5.83, p .05; Yogurt Type Time Group

interaction, F(11, 132) 4.29, p .05. Post hoc analyses revealed

that during training in the sweet predictive group, departures from

baseline body temperature were significantly higher when the

sweetened yogurt was available than they were when the unsweetened

yogurt was available for the final 30 min of measurement. In

the sweet nonpredictive group, core body temperature was not

higher following consumption of the sweetened yogurt than after

consumption of the unsweetened yogurt at any time; in fact, the

only significant difference was a small increase in body temperature

during the final 5-min bin when sweetened yogurt was consumed

as compared with body temperature when unsweetened

yogurt was consumed.

During testing, when the same novel premeal was provided to

both groups, core body temperature changes were significantly

affected by the training group and time (see Figure 9)?main effect

of group, F(1, 12) 5.29, p .05; main effect of time, F(5, 60)

153.11, p .05, Time Group Interaction, F(5, 60) 4.18, p

.05. Sweet predictive rats showed significantly greater increases in

core body temperature than did nonpredictive rats. Activity levels

during testing were significantly affected by the time since presentation

of the diet (see Figure 10)?main effect of time, F(5,

60) 3.15, p .05. There was a trend for activity level to vary

by training group, but neither the main effect of group nor the

Group Time interaction reached statistical significance (see

Figure 10)?main effect of group, F(1, 12) 3.79, p .075;

Time Group Interaction, F(5, 60) 1.62, p .17. Although the

differences between the groups approached significance, the activity

levels of both groups were quite low and did not vary

directly across time with core body temperature.

General Discussion

A number of researchers have suggested that Pavlovian conditioning

may contribute to the control of energy intake (e.g.,

Woods, 1991; Woods & Ramsey, 2000) and other types of regulatory

processes (e.g., Dworkin & Dworkin, 1995; Siegel, Baptista,

Kim, McDonald, & Weise-Kelly, 2000). The present research

extends these earlier ideas by investigating the possibility that

experiences that weaken Pavlovian conditioning also make regulatory

mechanisms involved with the control of energy intake and

body weight less effective. An idea fundamental to contemporary

theories of Pavlovian conditioning is that learning about cues and

outcomes is promoted to the extent that these events are embedded

in predictive (i.e., contingent) relationships. The strength of a

predictive relationship is an increasing function of the number of

occasions in which a cue and its outcome occur together and are

omitted together. Conversely, this predictive relationship is weakened

by increasing the number of occasions in which either the cue

or the outcome occur alone (e.g., Escobar & Miller, 2004; Rescorla,

1969).

From a Pavlovian conditioning perspective, sweet tastes can be

conceptualized as orosensory cues (i.e., conditioned stimuli) that

are normally very valid predictors of the occurrence of postoral

caloric or nutritive outcomes (i.e., unconditioned stimuli). The

strength of this predictive relationship determines the ability of

orosensory cues to evoke a variety of conditioned cephalic-phase

reflexes (e.g., hormonal, metabolic, thermogenic) that have been

hypothesized to contribute to the tight physiological control of

energy regulation. The present experiments assessed the effects of

varying the strength of this predictive relationship on the ability of

rats to regulate their food intake and body weight. The strength of

Figure 9. Changes in core body temperature during the first 30 min following presentation of the same novel,

sweet premeal to sweet predictive and sweet nonpredictive rats. Sweet predictive rats showed significantly

greater increases in core body temperature than did sweet nonpredictive rats.

SWEET TASTE CUES AND ENERGY DYSREGULATION IN RATS 9

the sweet-taste/caloric-outcome contingency was manipulated by

exposing rats to plain yogurt in which sweet taste was either

consistently paired with an increment in calories or not.

The results demonstrated that, in comparison with rats for which

sweet taste did predict an increase in calories, rats that received the

nonpredictive sweet-taste/calorie relationship exhibited greater caloric

intake, greater body weight gain, increased body adiposity, an

impaired ability to compensate for the calories contained in a novel

sweet food by eating less during a subsequent test meal, and a

smaller increment in core body temperature following consumption

of a novel, sweetened high-calorie food. In our research, the

volume of yogurt (unsweetened and calorically and noncalorically

sweetened) eaten and the conditions of access to the yogurt were

equated for rats exposed to the respective predictive and nonpredictive

relations. Thus, differences in preference for or palatability

of the calorically and noncalorically sweetened yogurts cannot

explain the observed differences in our dependent measures (body

weight gain, adiposity, food intake, core body temperature). In

addition, because the amount consumed of each type of yogurt was

equated, more calories would have been derived from the glucose

sweetened-yogurt consumed by rats in the predictive group than

from the saccharin-sweetened yogurt consumed by rats in the

nonpredictive groups. However, rather than exhibiting less weight

gain and adiposity, the rats in the nonpredictive groups that ate the

lower calorie, saccharin-sweetened yogurt gained more weight and

body fat than did rats in the predictive groups that ate the higher

calorie yogurt sweetened with glucose. The finding that consuming

a lower calorie food yielded more weight gain and body adiposity

than did consuming an equal amount of a higher calorie version of

the same food appears to pose difficulties for views that emphasize

the homeostatic aspects of energy regulation (Cummings & Overduin,

2007; Murphy & Bloom, 2006; Seeley & York, 2005).

The results of Experiments 2 and 3 provide one basis for

interpreting this outcome. In Experiment 2, rats trained with nonpredictive

and predictive sweet-taste/calorie relations were tested

for caloric compensation after both groups consumed a common

novel, sweet tasting, relatively high-calorie premeal (Chocolate

Ensure Plus). In comparison with rats trained with the predictive

relationship, rats trained with the nonpredictive relationship were

less able to compensate for calories contained in the premeal by

reducing their intake of lab chow in the subsequent test meal.

Experiment 3 extended the results of Experiment 2 by showing

that eating the Chocolate Ensure premeal also produced a smaller

increment in core body temperature for rats trained with the

nonpredictive sweet-taste/calorie relationship than was seen in rats

trained with the predictive sweet-taste/calorie relationship. If the

increments in core body temperature that we observed in Experiment

3 index preabsorptive energy utilization, then consuming the

Chocolate Ensure premeal was associated with less energy utilization

by rats given prior nonpredictive sweet-taste/calorie training.

It may be that caloric compensation during a meal depends not

only on the amount of energy consumed but also on the amount of

energy utilized prior to the meal. If this is the case, a blunted

Figure 10. Changes in activity during the first 30 min following presentation of the same novel, sweet premeal

to sweet predictive and sweet nonpredictive rats. There were no significant differences in activity across the two

training groups.

10 SWITHERS AND DAVIDSON

thermic response to the premeal might have contributed to impaired

caloric compensation.

The mechanisms that link experience with a nonpredictive sweettaste/

calorie relationship to a blunting of the thermic response to food

need to be specified. Viewed from the present Pavlovian perspective,

preabsorptive increases in core body temperature could index the

evocation of a conditioned cephalic-phase response that anticipates

and promotes the increased utilization of calories that is normally

produced by the nutrient absorption. We assume that the elicitation of

these responses depends, at least in part, on the ability of sweet taste

to predict these postabsorptive caloric or nutritive consequences.

Accordingly, manipulations that disrupt or degrade this predictive

relationship would also interfere with the ability of sweet-tasted cues

to evoke conditioned thermic and other cephalic-phase responses.

This interference could lead to reduced energy utilization and, ultimately,

to increased weight gain.

There is little doubt that sweet tastes can evoke responses in

addition to the thermic reflexes that we measured in Experiment 3

(Mattes, 1997; Teff, 2000). For example, ingestion of sweet food

is also accompanied by preabsorptive or cephalic-phase insulin

release (CPIR). It is interesting that both reduced CPIR and

cephalic-phase thermic responses have been linked to energy dysregulation

in humans. Teff, Mattes, Engelman, and Mattern (1993)

reported that the magnitude of the CPIR is diminished in obese

humans when expressed as a proportion of basal insulin levels,

whereas Hashkes, Gartside, and Blondheim (1997) reported that

obese humans exhibited a weaker cephalic-phase thermic response

than did nonobese controls following consumption of a palatable

(Ensure) test meal. It may be that the magnitude of the thermic

response to food is mediated by insulin release (Laville et al.,

1993). In keeping with this possibility, Storlien and Bruce (1989)

proposed that a primary failure of normal cephalic-phase responses

eventually leads to increased postprandial hyperglycemia and decreased

thermogenesis. Persistent hyperglycemia leads to insulin

resistance (as insulin does not effectively dispose of glucose), and

diminished postprandial thermogenesis promotes weight gain

based on reduced energy expenditure (Watanabe et al., 2006). This

analysis suggests a potential mechanism whereby degrading the

predictive relationship between sweet taste and calories could lead

to excess food intake and body-weight gain. Previous findings

showed that the thermic effect of food was reduced when meal

taking occurred on an irregular basis as compared with that seen

when meal taking occurred on a regular basis (Farshchi, Taylor, &

Macdonald, 2004). In keeping with the present analysis, it may be

that temporal cues that are associated with caloric intake are also

degraded in their ability to promote the evocation of thermic

reflexes when meal times are difficult to predict.

In addition to sweet taste, cephalic-phase responses could be

evoked by other types of orosensory cues that are also valid signals

of postabsorptive caloric outcomes. For example, the caloric content

of a food is typically directly correlated with oiliness or fatty

taste. The viscosity of food may also provide a similar signal in

that, holding other sensory properties constant, energy-rich foods

are more likely to be thick and creamy than thin and watery.

Accordingly, from the current theoretical perspective, manipulations

that reduce the predictive validity of oily, fatty tastes or of

viscosity with respect to caloric outcomes should produce changes

in energy regulation similar to those reported with sweet tastes in

the present studies.

Recent research in our laboratories supports these predictions.

For example, Swithers, Doerflinger, and Davidson (2006) gave

rats potato chips as a dietary supplement along with ad-libitum rat

chow. For some rats, the potato chips were a consistent source of

high fat and high calories (regular potato chips). For other rats, the

chips provided high fat and high calories on some occasions

(regular potato chips) and provided no digestible fat and fewer

calories at other times (reduced calorie chips manufactured with a

fat substitute). Thus, the fatty taste of the potato chips was a

stronger predictor of high calories for the former group than it was

for the latter group. Adult rats that were exposed to the nonpredictive

relationship between potato chips and calories exhibited

increased intake of a novel high-fat, high calorie corn chip and an

impaired ability to compensate for calories contained in a novel,

high-fat premeal by reducing intake of lab chow in a subsequent

test meal. Considering viscosity cues, Davidson and Swithers,

(2005) showed that when adult rats were offered dietary supplements

matched on caloric density and both macronutrient and

micronutrient composition but differing in viscosity, consumption

of a lower viscosity (milklike) supplement was associated with

reduced caloric compensation on short-term intake tests and increased

long-term body weight gain as compared with rats that

consumed a higher viscosity (puddinglike) dietary supplement. In

addition, juvenile rats given 9 weeks access to low-viscosity versions

of supplemental diets had greater adiposity both immediately

following the exposure and up to 3 months after return to chow

alone than did rats given 9 weeks access to identical diets given in

high-viscosity form (Swithers & Davidson, 2005a).

In our research, increased body weight gain, energy intake,

adiposity, decreases in core body temperature, and blunted caloric

compensation for sweet-tasting calories were observed in rats that

experienced a nonpredictive relationship between sweet tastes and

calories. One interpretation of these results is that they are directly

related to each other; in other words, increased body weight gain

and adiposity result directly from altered physiological changes

that reduce preprandial cephalic-phase energy expenditure as indexed

by blunted thermic responses to food. However, an alternative

possibility is that these outcomes, while linked, are mediated

through some other common process. For example, although levels

of activity during the premeal test were low, there was a trend for

rats in the predictive group to show more activity than did rats in

the nonpredictive group. Thus, differences in core body temperature

could be related to differences in activity. Whether the activity

drives the increased body temperature or increased body temperature

drives activity remains to be determined. In addition, it is

possible that the changes in temperature do not reflect preabsorptive

(e.g., cephalic) responses but are instead related to differences

in patterns of intake of the premeal or gastrointestinal handling of

the ingested premeal. Each of these possibilities warrants investigation.

However, independent of the particular mechanism that

produces these changes, the data clearly indicate that consuming a

food sweetened with no-calorie saccharin can lead to greater

body-weight gain and adiposity than would consuming the same

food sweetened with high-calorie sugar.

Such an outcome may seem counterintuitive, if not an anathema,

to human clinical researchers and health care practitioners who

have long recommended the use of low-and no-calorie sweeteners

as a means of weight control (Duffy & Sigman-Grant, 2004).

According to the Calorie Control Council (www.caloriecontrol.

SWEET TASTE CUES AND ENERGY DYSREGULATION IN RATS 11

org), the number of Americans that consume products containing

sugar-free sweeteners grew from fewer than 70 million in 1987 to

about 160 million in 2000. These substances are now commonly

found in a wide variety of low-calorie, health conscious foods,

with increased consumption in the form of diet soft drinks being

especially dramatic. Over the same period, the incidence of obesity

in the United States increased from about 15% to 30% and continues

to increase in the present day (Flegal, 2005). This alarming

trend toward weight gain is apparent, in varying degrees, across all

age groups, ethnic groups, and social strata in all regions of the

country (Flegal, Carroll, Ogden, & Johnson, 2002). Of special

concern, findings that overweight or obese children tend to become

overweight or obese adults suggests that, unless effective interventions

can be developed, the current obesity epidemic is likely to

continue (Freedman, Khan, Serdula, Srinivasan, & Berenson,

2001; Freedman et al., 2004).

A common interpretation of the direct correlation between increased

use of noncaloric sweeteners and increased incidence of

obesity is that people have turned to calorie-free sweeteners as a

means of reducing energy intake and controlling body weight.

However, our findings and theoretical framework are in closer

agreement with the possibility that increased intake of no-calorie

sugar substitutes could promote increased intake and body weight

gain, which is consistent with recent data from prospective human

clinical studies that have documented increased risk for obesity

and metabolic syndrome in individuals consuming beverages

sweetened with high-intensity sweeteners (e.g., Dhingra et al.,

2007; Liebman et al., 2006). Although much research has been

directed at selecting among these alternatives, a consensus opinion

about the effectiveness of consuming artificially sweetened substances

as means of weight control has yet to emerge (see Bellisle

& Drewnowski, 2007, and Blundell & Green, 1996, for reviews).

Our data were obtained under the highly controlled experimental

conditions that research with a rat model can afford. Furthermore,

guided by our Pavlovian framework, we studied the effects of consuming

saccharin on intake and body weight regulation under conditions

that had not been investigated previously with either humans or

animals. Specifically, we assessed the effects of varying the predictive

relationship between sweet taste and calories on the ability of rats to

regulate their intake of lab chow and to utilize the calories contained

in a novel, sweet, high-calorie food. The increases in food intake and

body weight we obtained with this approach may be considered to be

modest when compared with that seen in animals that have undergone

hypothalamic (e.g., lesions, stimulation) or genetic manipulations

(King, 2006; Zhang et al., 1994). However, it is also the case that very

few humans show dramatic increases in food intake and body weight

similar to that seen in hypothalamic-lesioned or genetically altered

rodents. Thus, the gradual increases in body weight shown by our rats

makes them more similar to the current U.S. human population, which

has exhibited about a 10% increase in body weight over the past 10

years (Lewis et al., 2000).

The generality of findings obtained with rats in the laboratory to

humans in their much more complex food environments can and

should be questioned. However, it is conceivable that just as

exposure to nonpredictive sweet taste-calorie relationships in the

laboratory appears to promote increased body weight and body

adiposity in rats, the widespread use of noncaloric sweeteners in

the food environment of humans may have similar effects on the

predictive validity of sweet tastes and ultimately on the normal

ability of humans to control their intake and body weight.

References

Bellisle, F., & Drewnowski, A. (2007). Intense sweeteners, energy intake

and the control of body weight. European Journal of Clinical Nutrition,

61, 691?700.

Berthoud, H. R., Trimble, E. R., Siegel, E. G., Bereiter, D. A., &

Jeanrenaud, B. (1980). Cephalic-phase insulin secretion in normal and

pancreatic islet-transplanted rats. American Journal of Physiology, 238,

E336?E340.

Blundell, J. E., & Green, S. M. (1996). Effect of sucrose and sweeteners on

appetite and energy intake. International Journal of Obesity, 20(Suppl.

2), S12?S17.

Booth, D. A. (1972). Satiety and behavioral caloric compensation following

intragastric glucose loads in the rat. Journal of Comparative &

Physiological Psychology, 78, 412?432.

Bruce, D. G., Storlien, L. H., Furler, S. M., & Chisholm, D. J. (1987).

Cephalic phase metabolic responses in normal weight adults. Metabolism:

Clinical and Experimental, 36, 721?725.

Cooling, J., & Blundell, J. E. (2000). Lean male high- and low-fat

phenotypes? different routes for achieving energy balance. International

Journal of Obesity, 24, 1561?1566.

Cummings, D. E., & Overduin, J. (2007). Gastrointestinal regulation of

food intake. Journal of Clinical Investigation, 117, 13?23.

Davidson, T. L., & Swithers, S. E. (2004). A Pavlovian approach to the

problem of obesity. International Journal of Obesity, 28, 933?935.

Davidson, T. L., & Swithers, S. E. (2005). Food viscosity influences

caloric intake compensation and body weight in rats. Obesity Research,

13, 537?544.

de Jonge, L., & Bray, G. A. (1997). The thermic effect of food and obesity:

A critical review. Obesity Research, 5, 622?631.

de Jonge, L., & Bray, G. A. (2002). The thermic effect of food is reduced

in obesity. Nutrition Reviews, 60, 295?297.

Dhingra, R., Sullivan, L., Jacques, P. F., Wang, T. J., Fox, C. S., Meigs,

J. B., et al. (2007). Soft drink consumption and risk of developing

cardiometabolic risk factors and the metabolic syndrome in middle-aged

adults in the community. Circulation, 116, 480?488.

Diamond, P., Brondel, L., & LeBlanc, J. (1985). Palatability and postprandial

thermogenesis in dogs. American Journal of Physiology, 248, E75?E79.

Duffy, V. B., & Sigman-Grant, M. (2004). Position of the American

Dietetic Association: Use of nutritive and nonnutritive sweeteners. Journal

of the American Dietetic Association, 104, 255?275.

Dworkin, B. R., & Dworkin, S. (1995). Learning of physiological response:

II. Classical conditioning of the baroreflex. Behavioral Neuroscience,

109, 1119?1136.

Escobar, M., & Miller, R. R. (2004). A review of the empirical laws of

basic learning in Pavlovian conditioning. International Journal of Comparative

Psychology, 17, 279?303.

Farshchi, H. R., Taylor, M. A., & Macdonald, I. A. (2004). Decreased

thermic effect of food after an irregular compared with a regular meal

pattern in healthy lean women. International Journal of Obesity, 28,

653?660.

Flegal, K. M. (2005). Epidemiologic aspects of overweight and obesity in

the United States. Physiology & Behavior, 86, 599?602.

Flegal, K. M., Carroll, M. D., Ogden, C. L., & Johnson, C. L. (2002).

Prevalence and trends in obesity among U.S. adults, 1999?2000. Journal

of the American Medical Association, 288, 1723?1727.

Foltin, R. W., Fischman, M. W., Moran, T. H., Rolls, B. J., & Kelly, T. H.

(1990). Caloric compensation for lunches varying in fat and carbohydrate

content by humans in a residential laboratory. American Journal of

Clinical Nutrition, 52, 969?980.

Freedman, D. S., Khan, L. K., Serdula, M. K., Dietz, W. H., Srinivasan,

12 SWITHERS AND DAVIDSON

S. R., & Berenson, G. S. (2004). Inter-relationships among childhood

BMI, childhood height, and adult obesity: The Bogalusa Heart Study.

International Journal of Obesity, 28, 10?16.

Freedman, D. S., Khan, L. K., Serdula, M. K., Srinivasan, S. R., &

Berenson, G. S. (2001). BMI rebound, childhood height and obesity

among adults: The Bogalusa Heart Study. International Journal of

Obesity, 25, 543?549.

The Freedonia Group. (2001). Artificial sweeteners and fat replacers.

International Sugar Journal, 103, 328.

Harkin, A., O?Donnell, J. M., & Kelly, J. P. (2002). A study of VitalView

for behavioural and physiological monitoring in laboratory rats. Physiology

& Behavior, 77, 65?77.

Hashkes, P. J., Gartside, P. S., & Blondheim, S. H. (1997). Effect of food

palatability on early (cephalic) phase of diet-induced thermogenesis in

nonobese and obese man. International Journal of Obesity, 21, 608?613.

Hill, J. O., & Peters, J. C. (1998, May 29). Environmental contributions to

the obesity epidemic. Science, 280, 1371?1374.

Jequier, E. (1983). Thermogenic responses induced by nutrients in man:

Their importance in energy balance regulation. Experientia: Supplementum,

44, 26?44.

King, B. M. (2006). The rise, fall, and resurrection of the ventromedial

hypothalamus in the regulation of feeding behavior and body weight.

Physiology & Behavior, 87, 221?244.

Laville, M., Cornu, C., Normand, S., Mithieux, G., Beylot, M., & Riou,

J. P. (1993). Decreased glucose-induced thermogenesis at the onset of

obesity. American Journal of Clinical Nutrition, 57, 851?856.

LeBlanc, J., & Cabanac, M. (1989). Cephalic postprandial thermogenesis

in human subjects. Physiology & Behavior, 46, 479?482.

LeBlanc, J., Cabanac, M., & Samson, P. (1984). Reduced postprandial heat

production with gavage as compared with meal feeding in human

subjects. American Journal of Physiology, 246, E95?E101.

Lewis, C. E., Jacobs, D. R., Jr., McCreath, H., Kiefe, C. I., Schreiner, P. J.,

Smith, D. E., & Williams, O. D. (2000). Weight gain continues in the

1990s: 10-year trends in weight and overweight from the CARDIA

study. Coronary artery risk development in young adults. American

Journal of Epidemiology, 151, 1172?1181.

Liebman, M., Pelican, S., Moore, S. A., Holmes, B., Wardlaw, M. K.,

Melcher, L., et al. (2006). Dietary intake-, eating behavior-, and physical

activity-related determinants of high body mass index in the 2003 Wellness

in the Rockies cross-sectional study. Nutrition Research, 26, 111?117.

Lowe, M. R. (2003). Self-regulation of energy intake in the prevention and

treatment of obesity: Is it feasible? Obesity Research, 11(Suppl.), 44S?

59S.

Mattes, R. D. (1996). Dietary compensation by humans for supplemental

energy provided as ethanol or carbohydrate in fluids. Physiology &

Behavior, 59, 179?187.

Mattes, R. D. (1997). Physiologic responses to sensory stimulation by

food: Nutritional implications. Journal of the American Dietetic Association,

97, 406?413.

Mazlan, N., Horgan, G., & Stubbs, R. J. (2006). Energy density and weight

of food effect short-term caloric compensation in men. Physiology &

Behavior, 87, 679?686.

Murphy, K. G., & Bloom, S. R. (2006, December 14). Gut hormones and

the regulation of energy homeostasis. Nature, 444, 854?859.

Nicklas, T. A., Baranowski, T., Cullen, K. W., & Berenson, G. (2001).

Eating patterns, dietary quality and obesity. Journal of the American

College of Nutrition, 20, 599?608.

Pavlov, I. (1927). Conditioned reflexes. New York: Oxford University

Press.

Powley, T. L., & Berthoud, H. R. (1985). Diet and cephalic phase insulin

responses. American Journal of Clinical Nutrition, 42(Suppl. 5), 991?1002.

Rescorla, R. A. (1969). Information variables in Pavlovian conditioning. In

G. H. Bower (Ed.), The psychology of learning and motivation (Vol. 6,

pp. 1?46). New York: Academic Press.

Rigby, N. J., Kumanyika, S., & James, W. P. (2004). Confronting the

epidemic: The need for global solutions. Journal of Public Health

Policy, 25, 418?434.

Rowland, N. E., Nasrallah, N., & Robertson, K. L. (2005). Accurate caloric

compensation in rats for electively consumed ethanol-beer or ethanolpolycose

mixtures. Pharmacology Biochemistry & Behavior, 80, 109?114.

Schiweck, H. (1999). From sugar to sweetener market. Zuckerindustrie,

124, 611?615.

Sclafani, A. (1997). Learned controls of ingestive behaviour. Appetite, 29,

153?158.

Sclafani, A. (2001). Post-ingestive positive controls of ingestive behavior.

Appetite, 36, 79?83.

Seeley, R. J., & York, D. A. (2005). Fuel sensing and the central nervous

system (CNS): Implications for the regulation of energy balance and the

treatment for obesity. Obesity Reviews, 6, 259?265.

Siegel, S. (2005). Drug tolerance, drug addiction, and drug anticipation.

Current Directions in Psychological Science, 14, 296?300.

Siegel, S., Baptista, M. A., Kim, J. A., McDonald, R. V., & Weise-Kelly,

L. (2000). Pavlovian psychopharmacology: The associative basis of

tolerance. Experimental & Clinical Psychopharmacology, 8, 276?293.

Storlien, L. H., & Bruce, D. G. (1989). Mind over metabolism: The

cephalic phase in relation to non-insulin-dependent diabetes and obesity.

Biological Psychology, 28, 3?23.

Swithers, S. E., & Davidson, T. L. (2005a). Influence of early dietary

experience on energy regulation. Physiology & Behavior, 86, 669?680.

Swithers, S. E., & Davidson, T. L. (2005b). Obesity: Outwitting the

wisdom of the body? Current Neurology and Neuroscience Reports, 5,

159?162.

Swithers, S. E., Doerflinger, A., & Davidson, T. L. (2006). Consistent

relationships between sensory properties of savory snack foods and

calories influence regulation of food intake in rats. International Journal

of Obesity, 30, 1685?1692.

Tappy, L. (1996). Thermic effect of food and sympathetic nervous system

activity in humans. Reproduction, Nutrition, Development, 36, 391?397.

Teff, K. L. (2000). Nutritional implications of the cephalic-phase reflexes:

Endocrine responses. Appetite, 34, 206?213.

Teff, K. L., Devine, J., & Engelman, K. (1995). Sweet taste: Effect on

cephalic phase insulin release in men. Physiology & Behavior, 57,

1089?1095.

Teff, K. L., Mattes, R. D., Engelman, K., & Mattern, J. (1993). Cephalicphase

insulin in obese and normal-weight men: Relation to postprandial

insulin. Metabolism: Clinical and Experimental, 42, 1600?1608.

Tordoff, M. G. (1988). How do non-nutritive sweeteners increase food

intake? Appetite, 11(Suppl. 1), 5?11.

Watanabe, T., Nomura, M., Nakayasu, K., Kawano, T., Ito, S., & Nakaya,

Y. (2006). Relationships between thermic effect of food, insulin resistance

and autonomic nervous activity. Journal of Medical Investigation,

53, 153?158.

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I refused to allow my children to have artificial sweetners growing up and I dropped them from my own diet years ago. I'm convinced that's one reason they have very few problems and mine aren't as bad as they could be.

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