NBR 427
NEUROSCIENCE AND
BIOBEHAVIORAL
REVIEWS
PERGAMON
Neuroscience and Biobehavioral Reviews 24 (2000) 249–259
www.elsevier.com/locate/neubiorev
Review
Rating changes over the course of meals: what do they tell us about
motivation to eat?
M.R. Yeomans*
Laboratory of Experimental Psychology, University of Sussex, Brighton BN1 9QG, UK
Abstract
Detailed analysis of the pattern of change in rated appetite within a meal have proved a useful technique through which to explore appetite
control. Variability in individual ratings, and technical difficulties in achieving ratings at equivalent stages of a meal, have lead to the use of
curve-fitting techniques to model changes in rated appetite across a meal. These changes could best be described by a quadratic function, in
which the three parameters (intercept, linear and quadratic coefficients) represented distinct influences on meal size. In normal subjects,
manipulations of palatability and opioid receptor blockade and preloads of alcohol all modified the linear component of this function only,
while preloading with maltodextrin reduced appetite at the start of eating (the intercept) but not the pattern of change in ratings within that
meal. Thus the linear coefficient appears to measure the degree of stimulation of appetite by the sensory characteristics of the food, while the
intercept reflects baseline appetite at the start of a meal. These results suggest that microstructural analyses of rating changes allow some
dissociation of the factors underlying motivation to eat, and provide a novel methodology for future experimentation. q 2000 Elsevier
Science Ltd. All rights reserved.
Keywords: Hunger; Microstructure; Appetite; Satiety; Motivation
Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. Theoretical perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3. Basic methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4. Flavour manipulation, palatability and appetite control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5. Physiological manipulations and the control of appetite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6. Alcohol and the control of appetite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. Introduction
Given the long history of the use of microstructural
analyses of feeding as a tool to explore appetite control in
animals (see reviews by Clifton and Berridge, this issue), it
is surprising that the microstructural approach has only been
introduced relatively recently in studies of appetite control
in humans. Early studies (e.g. [2,3,9,30,56], see reviews by
Guss et al. this issue) concentrated on the same dependent
variables of eating rate, bout size, etc. that had been used in
animal studies, and with useful results. However, one of the
* Tel.: 144-1273-678617; fax: 144-1273-678611.
E-mail address: [email protected] (M.R. Yeomans).
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major differences between human and animal research is the
possibility to make use of introspection as a measure of
behaviour in humans.
In appetite research, the major use of data from introspective measures has been in the form of appetite ratings.
Ratings of hunger and fullness, along with sensory and
hedonic evaluations of the foods being eaten, are regularly
used in studies of appetite, and have yielded some useful
and informative data. For example, changes in ratings of
food pleasantness have been used independently to develop
the ideas of negative gustatory alliesthesia (e.g. [14]) and
sensory-specific satiety (e.g. [45]). Similarly, changes in
actual ratings of hunger in the period immediately after a
meal have also proved valuable in dissociating the relative
0149-7634/00/$ - see front matter q 2000 Elsevier Science Ltd. All rights reserved.
PII: S0149-763 4(99)00078-0
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M.R. Yeomans / Neuroscience and Biobehavioral Reviews 24 (2000) 249–259
Fig. 1. How rated hunger might be predicted to change during a meal as a function of: (A) total food consumed; (B) accelerated satiation; and (C) as an
integration of orosensory stimulation and satiation.
satiating efficiency of different macronutrients (e.g. [42]).
Only recently has the potential use of appetite ratings as a
microstructural measure, where the ratings are made repeatedly within a meal, been explored. Some of these studies
have concentrated on comparisons of the microstructure of
changes in rated appetite between clinical and normal populations (e.g. [22,24,25,31,50,57], see also reviews by Guss
and Westerterp, this issue). However, other studies have
used this technique to explore basic processes underlying
the motivation to eat in normal subjects, and it is this
approach which forms the basis of the present review.
2. Theoretical perspective
As with any methodological approach, the use of extensive data collection is only of real value where the results
can be tied into an overall theoretical framework, which
allows meaningful evaluation of the significance of the
data. The wealth of existing microstructural data from
previous animal and human studies has allowed just such
a framework to be developed, and this has greatly facilitated
the application of a microstructural approach based on
appetite ratings.
The key question to be addressed from a theoretical
standpoint is what does a particular appetite rating signify?
While the answer is still subject to debate in terms of the
exact processes involved, there is a general consensus that
appetite ratings result from the integration of internal cues
relating to the current nutrient requirements of the body,
orosensory cues relating to the acceptability of the food
being eaten and a variety of contextual cues (e.g. [11]).
Thus when subjects are asked “How hungry are you at the
present time?”, their answer cannot be assumed to be a
measure of a single motivational construct such as hunger
or satiety in its physiological sense. Rather, we should
conceive rated hunger as a reflection of a subject’s assessment of their desire to eat.
If we accept that a hunger rating is more than a simple
reflection of a single underlying physiological process, how
then might we dissociate the motivational components
underlying this rating? We argue that detailed analysis of
how ratings of hunger and fullness change across a meal
following various appetite manipulations allow us to do just
that. However, in order to do so, first it is important to
establish a motivational framework within which to analyse
these data. The approach we have taken has built on many
years of research by other scientists. Briefly, the simplest
models of meal-taking, such as the classic model of appetite
control based on hypothalamic function by Stellar [47] were
based primarily around the concept of two negative-feedback processes: one based on stimuli arising from the loss of
nutrients through metabolism between meals and the other
based on feedback about food intake during ingestion (see
Ref. [52] for a description of the development of this
systems-based approach). A further major influence on our
own approach to understanding appetite control was the
suggestion that orosensory reward (i.e. the stimulation of
appetite by the sensory properties of foods) could also
influence meal-size. Here, the model of licking in rats
developed by Davis and Levine [18] was instrumental in
allowing us to consider different ways in which human
hunger may change as a consequence of ingestion. The
idea of food-based reward has since been developed further
(e.g. [4]), and our modelling of behaviour attempts to integrate these different ideas.
Integration of the traditional negative-feedback models
with the concept of orosensory reward implies meal-size
should be the consequence of three motivational influences.
Firstly, there is a baseline motivation at the onset of feeding.
This reflects the current desire to eat, which itself has been
described as an integration of internal cues relating to
nutrient deficit along with contextual cues (e.g. [13]).
Secondly, there is a positive feedback mechanism associated
with orosensory feedback. Thirdly, a negative feedback
system acts to produce an increasing signal whose effect
is ultimately to terminate eating (satiation). These ideas fit
well with the observation that the initial rate at which a rat
licks at a solution is dependent primarily on the stimulating
effectiveness of the ingestant (i.e. positive feedback control)
M.R. Yeomans / Neuroscience and Biobehavioral Reviews 24 (2000) 249–259
whereas the subsequent decline in lick rate appears to reflect
more the process of satiation (negative feedback control
[18]). Similar dissociation of these two feedback processes
is evident in the analysis of eating rate in foods varying in
palatability in humans (e.g. [9]). Thus the appetite model
used here to interpret changes in rated appetite is consistent
with the current systems-based approach to appetite control
in general.
If we take this motivational framework as a starting point
through which to analyse changes in rated appetite, it should
be possible firstly to see whether the predictions from this
model fit the data, and then secondly to test the validity of
the model using a variety of appetite manipulations. In order
to do this, we extrapolated from the descriptive model of
licking in rats described by Davis and Levine [18] so that it
could be used to predict how rated appetite might change
across a meal for a normal person eating a standard, bland
food. The prediction is that rated hunger should decline
progressively across the meal, while rated fullness should
increase, both as a consequence of negative feedback. If this
feedback is constant, then the decline in rated appetite
would be predicted to be linear (Fig. 1A). However, it is
widely believed that as intake progresses, negative feedback
increases probably through the progressive stimulation of
multiple satiety mechanisms (the satiety cascade, e.g. [8]).
The prediction that follows from this is that rated hunger
should decrease faster as the meal progresses (Fig. 1B).
What then would be predicted to happen if the food
consumed was highly palatable? Under these circumstances, the positive feedback arising from the orosensory
cues should counteract satiation, and consequently more
food will be eaten. Thus rated appetite should reflect the
integration of these separate positive and negative feedback
processes (Fig. 1C). If the food is sufficiently palatable, it is
conceivable that the effect of the positive feedback will be
sufficient to increase rated hunger in the early stages of the
meal (the appetiser effect, [58]). If rated hunger and fullness
are both approximations of desire to eat [11], then we would
predict changes in fullness to mirror those for hunger.
However, if fullness ratings simply reflect some form of
metering of the amount of food consumed, fullness ratings
would not be predicted to be dependent on palatability. The
following sections detail a series of studies conducted to
explore these various predictions.
3. Basic methodology
At first glance, asking subjects to make repeated ratings
of appetite within a meal should be a trivial problem
methodologically. However, a number of important issues
have to be addressed. Firstly, how often can (and should)
ratings be made? Researchers vary enormously in this
respect. Some have used time-based interruptions in order
to generate ratings at set time intervals (e.g. [58]). The
disadvantage of this approach is that the amount consumed
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in each time period will differ both within and between
subjects. Indeed, variation in normal eating rates can result
in fourfold differences in amount consumed in a set time
(e.g. [58]). The alternative approach, and one which greatly
reduces the problem of ensuring comparable ratings across
and within subjects, is to base the interval on the amount
consumed. This in turn is only possible when food intake is
being constantly monitored, and requires some form of
automatic measuring device. In our laboratory, we have
modified the Universal Eating Monitor first described by
Kissileff [29], and subsequently modified by Bellisle [2]
to allow analysis of chewing, to allow subjects to be interrupted at regular intervals, typically after each 50 g
consumed. This approach has several practical advantages.
Firstly, it regulates interval size so that variability in amount
consumed between each set of ratings can be minimised
(note that variability in bite size still means that the amount
consumed in each bout is not constant, but that the range is
considerably more limited than is seen when intervals are
time-based). Secondly, it removes any need for the experimenter to be present, removing a potential external
influence on eating. Thirdly, both intake and rating data
can be logged automatically, facilitating analysis.
A second important design feature is in the precise details
of the rating scales used. While most research using appetite
ratings has tended to use simple pen and paper measures, the
need for repeat ratings in microstructural work is more
suited to computerised rating scales integrated into a set
of automated instructions detailing subjects when to start
and stop eating. Computerised rating scales have several
advantages here. Firstly, the time at which they are
presented can be controlled precisely. Secondly, the order
of multiple ratings can be randomised, as can the polarity of
the rating scales, thereby reducing expectancy and order
effects. Thirdly, subjects make each rating independently,
without the use of previous ratings as a cross-reference. In
contrast, pen and paper ratings are usually administered in
the form of a booklet where subjects have access to all
previous ratings with which to compare present state.
Thus, the computerised approach ensures ratings are made
on the absolute scale, rather than as a relative judgement.
The precise wording of the rating is also important. Indeed,
some researchers have claimed that when we ask subjects
“How hungry are you?”, “How full are you?” and “How
pleasant is the food?”, these questions may all be interpreted
as “How much do you want to eat?” [11]. One aim of this
review is to critically assess this claim, and it will become
apparent that when changes in hunger and fullness ratings
within a meal are analysed in detail, important but subtle
differences emerge which imply that subjects can distinguish between these questions in a meaningful way. Moreover, we have found subtle differences in the individual use
of “pleasantness” and “palatability” ratings [66]. Thus
careful analysis can allow us to determine whether apparent
similarities and differences between different ratings are
meaningful or not.
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Fig. 2. (A) Total food consumed, (B) best-fit quadratic functions relating hunger, and (C) fullness against intake during the test meal for men and women eating
a bland (—), palatable (– – –) and an unpleasantly strong food (· ··) (adapted from Ref. [58]).
A third problem area in the use of appetite ratings as
microstructural variables is how best to analyse multiple
ratings. The main problem is how to contrast ratings
which may be spaced unevenly across a meal between
subjects and/or experimental conditions? Even when ratings
are evenly spaced across a meal, a rating after a set amount
consumed is at different stages of intake for different
subjects. Thus 500 g may represent the end of a meal for
one subject, but the mid-point of the meal for someone who
consumes 1000 g overall. To make direct comparison
between ratings at set intervals is thus problematic. Two
approaches have emerged to try and get around these
problems. The first, and that used extensively in this
laboratory, makes use of curve-fitting techniques to reduce
each set of ratings down to a mathematical relationship
between actual intake and rated appetite (e.g. [58,62–65]).
This approach has a number of advantages. Firstly, it
reduces the problem of rating accuracy by taking a weighted
average across data points as a function of intake. To
explain this, consider the situation of someone assessing
M.R. Yeomans / Neuroscience and Biobehavioral Reviews 24 (2000) 249–259
the sweetness of a solution using a standardised rating scale.
The accuracy of their response will depend on a number of
factors, including the sensitivity and ease of use of the scale.
If the same subject made the same rating twice under the
same conditions, slight variations will be seen. Researchers
into taste psychophysics reduce this problem by averaging
across repeat ratings. This is possible since we would expect
sweetness to reflect sugar content, which should be a
constant. However, this is more difficult with appetite
ratings during a meal since each rating is made under
slightly different conditions. By treating each rating as
some form of function of the amount ingested, we can use
curve-fitting techniques to reduce the problem of potential
inaccuracy in each individual rating.
The second advantage of the curve-fitting technique is
that the parameters from these fitted functions can be
contrasted between experimental conditions, greatly reducing
the number of comparisons to be made and so protecting
against Type 2 statistical errors. Put simply, if hypothetically
we had seven hunger ratings at equidistant points in a meal
and tried to contrast these between three experimental conditions, the number of data points generated would be 21.
Even allowing for the use of analysis of variance to make
some overall comparisons between conditions, any
significant interaction would require some form of contrasts
to be made between conditions at each rating time. The
number of contrasts could lead to statistical errors being
made. If however the same data had been summarised as a
quadratic function relating hunger against intake, each condition could be described in terms of the three parameters
from the best-fit functions, and separate analyses could then
be used to contrast each fitted parameter between conditions.
The third advantage of the curve-fitting technique is that
it allows a variety of mathematical models to be tested and
so allows some evaluation of the number of processes
needed to best describe the observed data. For example,
the prediction from the theoretical model illustrated in
Fig. 1C is that changes in rated appetite may best be
described by a quadratic function which combines appetite
at the start of eating (the intercept), orosensory reward (the
linear coefficient) and satiation (the quadratic function), and
this prediction can be tested by contrasting the goodness of
fit of the linear, quadratic and higher-level models. In
practice, each analysis starts by calculating the linear
equation which best describes the change in the rated
variable (e.g. hunger) with increasing intake, followed by
the equivalent quadratic and higher functions. This process
is repeated separately for each subject. The average increase
in goodness of fit as the model increases in complexity can
then be used to assess whether the addition of each new
factor improves significantly the mathematical description
of the data. However, the success of this approach as a
measure of appetite control does depend on the assumption
that the best-fit function is an accurate description of the
number of processes underlying an appetite rating, and
although the studies described later in this review suggest
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this may well be true, the fact that a mathematical equation
accounts for most of the variation is not a guarantee that the
model is correct.
The second approach makes no such assumptions about
the data, but gets around the problem of controlling for the
stage of a meal by artificially dividing meals into quarters,
and then using a simple linear algorithm to predict the rating
at each quartile (see Ref. [31]). These quartile ratings can
then be contrasted between groups or experimental conditions. The problem with this approach is that it assumes
that appetite varies proportionally across a meal, which may
not be the case if (as is widely believed) gastric distension
and chemoreceptors are key physiological determinants in
the cessation of feeding [39]. However, until there is clear
evidence in favour of one of these approaches, it makes
most sense for future studies to be analysed using both
techniques in order to gain the most possible information
for the data.
4. Flavour manipulation, palatability and appetite
control
The model illustrated in Fig. 1C makes clear predictions
about the influence of relative palatability on the microstructure of rated appetite. However, the role of palatability
in appetite control in general is still the subject of debate
(e.g. [38]), particularly in relation to how palatability integrates with motivation based on need states. The model
explored here is in line with the idea that the increase in
intake observed when the palatability of a food is increased
is a consequence of orosensory reward pathways, which
increase appetite and consequent intake. Microstructural
analyses of behaviour based on feeding rate both in animals
and humans tend to support this viewpoint (e.g. [9,17]). We
have tested these ideas further in a series of experiments in
which the flavour of a simple test food (pasta in a tomato
sauce) was manipulated by the addition of either a common
herb (oregano, [58,64]) or graded levels of sodium chloride
to alter palatability [59]. These studies were stimulated in
part by the pioneering work of Hill et al. [26], who reported
higher rated hunger at the end of a fixed-size meal for
subjects eating a preferred relative to a non-preferred food
and an initial increase in rated appetite when eating the
preferred food.
Our first study used a test meal divided into 2-min eating
episodes (bursts) with appetite ratings made in each interburst interval [58]. Overall, both men and women ate most
in the preferred (PALATABLE) condition (Fig. 2A). When
the change in rated hunger across the meal was modelled
mathematically, the resulting best-fit quadratic functions
also varied with palatability (Fig. 2B). However, only the
linear coefficient of these best-fit functions was affected,
with a higher positive value (i.e. representing a greater
tendency for hunger to increase) in the most palatable
condition. This effect was seen both with men and
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Fig. 3. (A) Total food consumed and (B) best-fit quadratic functions relating rated hunger against food consumed within a test meal for men eating food with
food with too little, their ideal and too much salt.
women, although it was more evident in women in that
study. When the strength of the food flavour was increased
further (the STRONG condition) the increased appetite
disappeared, and appetite now decreased linearly as a function of intake. We have since replicated the effect of increasing palatability in this way using meals divided into 50 g
bouts using a computerised universal pattern monitor [64].
The effect of the palatability manipulation was still evident,
and was independent of the effects of enforced pausing
during the meal. These results are consistent with a causal
sequence of behaviour in which the manipulation of palatability leads to a stimulation of appetite (which we have
called the appetiser effect, [58]) which in turn increases
food consumption.
While analyses of rated hunger provided clear evidence
for sensory stimulation of appetite in the early stages of
eating, no such effects were seen with fullness ratings
(Fig. 2C). Even though hunger tended to increase in the
early stages of eating a highly palatable food, rated fullness
did not show a concomitant decrease and the rate of
satiation was similar with all foods. Thus fullness ratings
seemed to reflect the volume of food ingested rather than a
more abstract desire to eat. This implies that when subjects
are asked to rate both hunger and fullness, they can make a
meaningful distinction between these two aspects of
appetite.
Further illustration of the appetiser effect came from a
recent study where we manipulated the salt content of the
ingested food (see Ref. [59]). Although salty tastes are
generally disliked when experienced in isolation (e.g.
[14]), salty tastes are liked in the context of actual food
stimuli (see Ref. [1]). We identified for each individual
subject samples of pasta in a tomato sauce containing
their preferred salt level, and then a level of salt above
and below this which was near to neutral in terms of liking.
This was possible since subjects generally like salty tastes
experienced in a savoury food context (e.g. [1]). We
subsequently assessed intake and changes in rated appetite
during lunch with these three foods. Overall intake varied
with rated pleasantness, with highest intake in the preferred
Fig. 4. (A) Total food consumed, (B) best-fit quadratic functions relating hunger, and (C) fullness against intake for men eating a test meal following either a
maltodextrin-rich (– – –) or control (—) soup preload (see Ref. [63] for further details).
M.R. Yeomans / Neuroscience and Biobehavioral Reviews 24 (2000) 249–259
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Fig. 5. (A) Total food consumed, (B) best-fit quadratic functions relating hunger, and (C) fullness against intake for men eating a test meal consisting of either
pasta in a cheese or a tomato sauce on a baseline day, and following naltrexone or placebo (see Ref. [62] for further details).
salt condition (Fig. 3A). Microstructural analysis of hunger
ratings demonstrated a clear relationship between the
relative palatability of the three foods and the appetiser
effect indexed as the linear coefficient of the quadratic
function relating hunger against intake (Fig. 3B). The linear
coefficient did not differ from neutral (zero) when subjects
ate food with a salt content which was rated as hedonically
neutral above or below their preferred salt content, but was
positive when subjects ate their preferred food. Consequently, rated hunger only increased in the early stages of
eating the food with the preferred level of salt. As with the
earlier studies, changes in rated fullness did not differ
between conditions.
5. Physiological manipulations and the control of
appetite
A simple method for testing sensitivity of a particular
eating situation to internal cues resulting from current nutritional status is to use controlled preloads consumed at a
known time before the test meal. This preload design has
been used extensively in appetite research, with varied
results (see Ref. [41] for recent review). However, when
preloads are based on carbohydrate and occur close to the
test meal (ideally 30 min [44]), subsequent intake has
usually been found to decrease as the energy content of
the preload increases (e.g. [6,7,43,55], but see Refs.
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Fig. 6. (A) Total food consumed; (B) best-fit quadratic functions relating hunger; (C) and fullness against intake during the test meal for men eating a pasta
lunch 20 min after drinking water (—), apple-juice (···) or an alcoholic apple drink (– – –) (adapted from Ref. [65]).
[23,48]). As a preliminary test of the influence of satiety on
the evolution of rated appetite during a test meal [63], we
presented subjects with a soup preload with either a lowenergy soup (CONTROL) or a soup with added maltodextrin (MALT). Subjects ate less overall (Fig. 4A) and
had lower hunger ratings (Fig. 4B) and higher fullness
ratings (Fig. 4C) at the onset of the test meal following
the MALT preload. However, the pattern of change in
ratings of both hunger and fullness within the test meal
were unaffected by the preload manipulation. The MALT
preload also failed to alter the rated pleasantness of the
lunch food in that study, although other studies using similar
preloads have reported reduced food pleasantness (e.g.
[12,27,28]).
Opioid peptides are heavily implicated in the mechanisms
underlying the influence of palatability on appetite (see
Refs. [10,16,40]). For example, administration of drugs
such as naloxone and naltrexone which selectively block
opioid receptors reduce intake of preferred foods in animals
(e.g. [15,33–35,46,60]) and humans [19,20,67,68] and also
reduce the rated pleasantness of sweet tastes [21,36] and
preferred foods [5,61,67]. Thus it would be predicted that
M.R. Yeomans / Neuroscience and Biobehavioral Reviews 24 (2000) 249–259
opioid antagonism should reduce food palatability and
thereby attenuate the appetiser effect, and this was demonstrated using naltrexone for subjects eating two different
foods [62]. Subjects ate less overall after 50 mg naltrexone
than after placebo or on a baseline pre-drug test day (Fig.
5A), and this reduction in intake was accompanied by a
decrease in the linear coefficient from the best-fit quadratic
functions relating rated hunger against food intake following naltrexone (Fig. 5B). Rated hunger at the onset of eating
was unaffected by naltrexone, in line with other studies (e.g.
[5,54]). Changes in fullness ratings did not differ significantly between conditions (Fig. 5C), but the test foods
were rated as tasting less pleasant following naltrexone.
Thus, as with manipulations of flavour alone, opioid
antagonism appears to alter the motivation to eat by modulation of orosensory reward mechanisms whose effects are
most evident at the start of a meal.
Even though the effects on overall intake of naltrexone
and the maltodextrin preload were similar, the effects on
rated appetite are strikingly different, maltodextrin reducing
hunger at the start of eating but not altering the pattern of
change within a meal, and naltrexone having no effect on
hunger at the start of eating but decreasing the appetiser
effect. This suggests a clear dissociation between the
mechanisms underlying satiation and palatability, but
since no study to date has combined these two manipulations, whether these are truly separate or interactive effects
remains to be explored. It is also evident that hunger and
fullness ratings can be dissociated under these conditions,
with hunger being sensitive to manipulations of palatability
but fullness appearing to reflect volume ingested regardless
of palatability.
6. Alcohol and the control of appetite
Another area where study of the microstructure of feeding
has generated interesting data relates to the effects of
alcohol. Moderate alcohol intake is increasingly recognised
as a risk factor for the development of obesity [51], and
laboratory studies have confirmed a relative insensitivity
to preloads based on alcohol. There are numerous potential
explanations for this effect, ranging from metabolic theories
(e.g. [32]) and the idea that alcohol fails to generate the
relevant satiety cues (e.g. [53]) to more psychological
ideas relating to alcohol acting as a disinhibitor of restrained
eating (e.g. [37]). A further idea, and one which fits with the
use of appetite in cuisine, is that alcohol may itself have
stimulatory effects on appetite. These ideas were explored in
a recent study in which we contrasted the effects of preloads
consisting of water (zero energy control), an alcoholic fruit
juice (ALCOHOL) and an iso-energetic non-alcoholic fruit
juice (JUICE) on the microstructure of eating at a test meal
20 min later [65]. The ALCOHOL and JUICE preloads
were closely matched for sensory properties, and although
subjects were aware that the study involved alcohol,
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responses in debriefing suggested our attempt to disguise
alcohol administration was largely successful. The study
used both men who scored high and low on the restraint
scale from the Three Factor Eating Questionnaire [49].
Intake at the test meal varied depending on the preload
and restraint classification (Fig. 6A), with no significant
effects of preload in the restrained men but a significant
reduction in intake after JUICE but not ALCOHOL in the
unrestrained men. Moreover, hunger at the start of eating
was reduced in both groups after the ALCOHOL and JUICE
preloads relative to WATER (Fig. 6B). The pattern of
change in rated hunger within the test meal was unaffected
by preload in the restrained men, but there was a significant
increase in the linear coefficient from the best-fit quadratic
function following ALCOHOL in the unrestrained men
(Fig. 6C). Thus, the failure to eat less by the unrestrained
subjects seems to result from stimulation of appetite once
eating started even though rated appetite had been
suppressed beforehand. The mechanism for this effect
remains unclear, and needs further substantiation. The
failure to find disinhibited eating by the restrained subjects
corresponds with an earlier study where the same pattern of
data were seen when subjects were unaware of the presence
of alcohol [37].
7. Discussion
The studies described in this review illustrate that the
analysis of changes in ratings of hunger and fullness within
a meal is a useful method for assessing the complex integration of the processes underlying the motivation to eat.
Most importantly, this method has been able to dissociate
the relative importance of orosensory and post-ingestive
cues. Manipulations of palatability have the greatest effect
on appetite ratings early in a meal, before the effects of
satiation become evident. The observation that blockage
of opioid receptors abolishes this effect fits well with current
theories of the role of opioid peptides in orosensory feedback control (e.g. [16]). The idea that alcohol may have the
opposite effect, that is to stimulate appetite early in a meal,
is a novel finding which is worthy of further investigation. In
contrast manipulating satiety through the careful use of oral
preloads failed to alter the pattern of change in rated appetite
within a meal, suggesting that the processes which underlie
motivation to eat at the start of a meal and the processes
which modify appetite while eating act independently, at
least under these test conditions. What is missing in these
analyses to date is a manipulation which enhances satiation
(the rate of decline in rated appetite within a meal) independently of other effects on appetite, and until such studies
have been completed, the true nature of the interaction of
the different motivational influences explored in this review
will remain unclear.
The study of rating changes within a meal also makes it
evident that subjects can and do discriminate between
258
M.R. Yeomans / Neuroscience and Biobehavioral Reviews 24 (2000) 249–259
“hunger” and “fullness” when asked to make these two
ratings repeatedly. None of the manipulations which altered
the pattern of change in rated hunger within a meal (palatability, opioid antagonism, alcohol) caused a significant
change in the pattern of change in fullness ratings. While
we should interpret this with caution since it may be that
fullness ratings are harder to make, and so are more
variable, at face value these data do suggest that the contention that these ratings all approximate to the same underlying question (e.g. [11]) is incorrect.
Overall, the analysis of changes in rated appetite within a
meal offers a novel and useful alternative to traditional
microstructural variables, and the appetite models which
have been developed from this work may prove useful in
future studies which are designed to elucidate more clearly
the physiological processes involved in the experience of
motivation to eat.
[16]
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