The Representation of Information About Taste and Odor in the Orbitofrontal Cortex

Abstract

Complementary neurophysiological recordings in macaques and functional neuroimaging in humans show that the primary taste
cortex in the rostral insula and adjoining frontal operculum provides separate and combined representations of the taste,
temperature, and texture (including viscosity and fat texture) of food in the mouth independently of hunger and thus of reward
value and pleasantness. One synapse on, in the orbitofrontal cortex, these sensory inputs are for some neurons combined by
learning with olfactory and visual inputs, and these neurons encode food reward in that they only respond to food when hungry
and in that activations here correlate with subjective pleasantness and with individual differences in and cognitive modulation
of the hedonic value of food. Information theory analysis shows a robust representation of taste in the orbitofrontal cortex,
with an average mutual information of 0.45bits for each neuron about which of six tastants (glucose, NaCl, HCl, quinine-HCl,
monosodium glutamate, and water) was present, averaged across 135 gustatory neurons. The information increased with the number
of neurons in the ensemble, but less than linearly, reflecting some redundancy. There was less information per neuron about
which of six odors was present from orbitofrontal olfactory neurons, but the code was robust in that the information increased
linearly with the number of neurons, reflecting independent information encoded by different neurons. Although some neurons
were sharply tuned to individual tastants, the average encoding was quite distributed.

KeywordsOrbitofrontal Cortex-Taste-Gustatory Cortex-Information Theory-Sparseness-Smell-Information-Neuronal Responses-fMRI

Full text

The Representation of Information About Taste and Odor
in the Orbitofrontal Cortex
Edmund T. Rolls &Hugo D. Critchley &
Justus V. Verhagen &Mikiko Kadohisa
Received: 31 May 2009 / Accepted: 1 September 2009 /Published online: 24 September 2009
#2009 Springer Science + Business Media, LLC
Abstract Complementary neurophysiological recordings
in macaques and functional neuroimaging in humans show
that the primary taste cortex in the rostral insula and
adjoining frontal operculum provides separate and com-
bined representations of the taste, temperature, and texture
(including viscosity and fat texture) of food in the mouth
independently of hunger and thus of reward value and
pleasantness. One synapse on, in the orbitofrontal cortex,
these sensory inputs are for some neurons combined by
learning with olfactory and visual inputs, and these neurons
encode food reward in that they only respond to food when
hungry and in that activations here correlate with subjective
pleasantness and with individual differences in and cognitive
modulation of the hedonic value of food. Information theory
analysis shows a robust representation of taste in the
orbitofrontal cortex, with an average mutual information of
0.45 bits for each neuron about which of six tastants (glucose,
NaCl, HCl, quinine-HCl, monosodium glutamate, and water)
was present, averaged across 135 gustatory neurons. The
information increased with the number of neurons in the
ensemble, but less than linearly, reflecting some redundancy.
There was less information per neuron about which of six
odors was present from orbitofrontal olfactory neurons, but
the code was robust in that the information increased linearly
with the number of neurons, reflecting independent informa-
tion encoded by different neurons. Although some neurons
were sharply tuned to individual tastants, the average
encoding was quite distributed.
Keywords Orbitofrontal Cortex .Taste .Gustatory Cortex .
Information Theory .Sparseness .Smell .Information .
Neuronal Responses .fMRI
Introduction
The aims of this paper are to describe the rules of the
cortical processing of taste and smell, how the pleasantness
or affective value of taste and smell are represented in the
brain, how cognitive factors modulate these affective
representations, and how these affective representations
play an important role in the control of appetite and food
intake based on a series of studies we have performed. To
make the results relevant to understanding the control of
human food intake, complementary evidence is provided by
neurophysiological studies in non-human primates and by
functional neuroimaging studies in humans. We describe
E. T. Rolls :H. D. Critchley :J. V. Verhagen :M. Kadohisa
Department of Experimental Psychology, University of Oxford,
South Parks Road,
Oxford OX1 3UD, UK
H. D. Critchley
e-mail: H.Critchley@bsms.ac.uk
J. V. Verhagen
e-mail: jverhagen@jbpierce.org
E. T. Rolls (*)
Oxford Centre for Computational Neuroscience,
Oxford, UK
e-mail: Edmund.Rolls@oxcns.org
URL: www.oxcns.org
J. V. Verhagen
The John B. Pierce Laboratory,
290 Congress Avenue,
New Haven, CT 06519, USA
Present Address:
H. D. Critchley
Department of Psychiatry, Brighton and Sussex Medical School,
University of Sussex,
Brighton BN1 9PX, UK
Chem. Percept. (2010) 3:16–33
DOI 10.1007/s12078-009-9054-4

new information theoretic analyses of the nature of the taste
and olfactory representations provided by orbitofrontal
cortex neurons.
Taste Processing in the Primate Brain
Pathways
A diagram of the taste and related olfactory, somatosen-
sory, and visual pathways in macaques is shown in
Fig. 1. The multimodal convergence that enables single
neurons to respond to different combinations of taste,
olfactory, texture, temperature, and visual inputs to
represent different flavors produced often by new combi-
nations of sensory input is a theme of recent research that
will be described.
The Primary Taste Cortex
The primary taste cortex in the primate anterior insula and
adjoining frontal operculum contains not only taste neurons
tuned to sweet, salt, bitter, sour (Scott et al. 1986; Yaxley et
al. 1990; Rolls and Scott 2003), and umami as exemplified
by monosodium glutamate (Baylis and Rolls 1991; Rolls et
al. 1996c), but also other neurons that encode oral
somatosensory stimuli including viscosity, fat texture,
temperature, and capsaicin (Verhagen et al. 2004). Some
neurons in the primary taste cortex respond to particular
combinations of taste and oral texture stimuli, but do not
respond to olfactory stimuli or visual stimuli such as the
sight of food (Verhagen et al. 2004). Neurons in the primary
taste cortex do not represent the reward value of taste,
that is the appetite for a food, in that their firing is not
decreased to zero by feeding the taste to satiety (Rolls et al.
1988; Yaxley et al. 1988).
The Secondary Taste Cortex
A secondary cortical taste area in primates was discovered
by Rolls et al. (1990) in the caudolateral orbitofrontal
cortex, extending several millimeters in front of the primary
taste cortex. This was shown to be a secondary taste cortical
area in a neuroanatomical study using horseradish peroxi-
dase in which it was shown that the area of the caudolateral
orbitofrontal cortex functionally identified as containing
taste responsive neurons receives projections from the
primary taste cortex in the insula and frontal operculum,
and projects on to other regions of the orbitofrontal cortex
(Baylis et al. 1995), throughout which taste neurons are
Behavior
Autonomic
responses
Cingulate Cortex
Behavior
V1 V2 V4
Thalamus
Receptors solitary tract VPMpc nucleus
VISION
Taste
TASTE
Bulb
Frontal operculum/Insula
visual cortex
Inferior temporal
(Primary Taste Cortex)
Nucleus of the
Amygdala
Gate
Lateral
function
by e.g. glucose utilization,
stomach distension or body
weight
Gate
Orbitofrontal
Cortex
Hypothalamus
Hunger neuron controlled
TOUCH
OLFACTION
Thalamus VPL
Olfactory
Primary somatosensory cortex (1.2.3)
Olfactory (Pyriform)
Cortex
Insula
Striatum
Fig. 1 Schematic diagram of
the taste and olfactory pathways
in primates including humans
showing how they converge
with each other and with visual
pathways. Hunger modulates the
responsiveness of the represen-
tations in the orbitofrontal cor-
tex of the taste, smell, texture,
and sight of food (indicated by
the gate function), and the orbi-
tofrontal cortex is where the
palatability and pleasantness of
food is represented. VPMpc
ventral posteromedial thalamic
nucleus. V1, V2, V4—visual
cortical areas
Chem. Percept. (2010) 3:16–33 17

found (Rolls and Baylis 1994; Critchley and Rolls 1996a;
Rolls et al. 1996c; Pritchard et al. 2005; Rolls 2008b).
Neurons in this region respond not only to each of the four
classical prototypical tastes sweet, salt, bitter, and sour
(Rolls 1997; Rolls and Scott 2003), but also there are many
neurons that respond best to umami tastants such as
glutamate (which is present in many natural foods such as
tomatoes, mushrooms, and milk; Baylis and Rolls 1991)
and inosine monophosphate (which is present in meat and
some fish such as tuna; Rolls et al. 1996c). This evidence,
taken together with the identification of glutamate taste
receptors (Zhao et al. 2003; Maruyama et al. 2006), leads to
the view that there are five prototypical types of taste
information channel, with umami contributing, often in
combination with corresponding olfactory inputs (Rolls et
al. 1998; McCabe and Rolls 2007; Rolls 2009a), to the
flavor of protein. In addition, other neurons respond to
water, and others to somatosensory stimuli including
astringency as exemplified by tannic acid (Critchley and
Rolls 1996a), and capsaicin (Rolls et al. 2003a; Kadohisa et
al. 2004). Taste responses are found in a large mediolateral
extent of the orbitofrontal cortex (Rolls and Baylis 1994;
Critchley and Rolls 1996a; Rolls et al. 1996c; Pritchard et
al. 2005; Rolls 2008b; Rolls and Grabenhorst 2008), as is
well illustrated in Fig. 8which shows the recording sites of
the neurons in the studies of Critchley and Rolls (1996a)
and Rolls et al. (1996c,1999).
The Pleasantness of the Taste of Food, Sensory-Specific
Satiety, and the Effects of Variety on Food Intake
The modulation of the reward value of a sensory stimulus
such as the taste of food by motivational state, for example
hunger, is one important way in which motivational
behavior is controlled (Rolls 2005,2007a). The subjective
correlate of this modulation is that food tastes pleasant
when hungry and tastes hedonically neutral when it has
been eaten to satiety. Following the discovery of sensory-
specific satiety revealed by the selective reduction in the
responses of lateral hypothalamic neurons to a food eaten to
satiety (Rolls 1981; Rolls et al. 1986), it has been shown
that this is implemented in a region that projects to the
hypothalamus, the orbitofrontal (secondary taste) cortex, for
the taste, odor, and sight of food (Rolls et al. 1989;
Critchley and Rolls 1996b).
This evidence shows that the reduced acceptance of food
that occurs when food is eaten to satiety, the reduction in
the pleasantness of its taste and flavor, and the effects of
variety to increase food intake (Cabanac 1971; Rolls and
Rolls 1977,1982,1997; Rolls et al. 1981a,b,1982,
1983a,b,1984; Rolls and Hetherington 1989; Hetherington
2007) are produced in the orbitofrontal cortex, but not at
earlier stages of processing including the primary taste cortex
where the responses reflect factors such as the intensity of
the taste, which is little affected by satiety (Rolls et al. 1983c;
Rolls and Grabenhorst 2008). In addition to providing an
implementation of sensory-specific satiety (probably by
habituation of the synaptic afferents to orbitofrontal neurons
with a time course of the order of the length of a course of a
meal), it is likely that visceral and other satiety-related
signals reach the orbitofrontal cortex (as indicated in Fig. 1)
(from the nucleus of the solitary tract, via the thalamus and
insula (Cechetto and Saper 1987;Craig2002; Critchley
2005), and possibly hypothalamic nuclei) and there modulate
the representation of food, resulting in an output that reflects
the reward (or appetitive) value of each food (Rolls 2005).
The Representation of Flavor: Convergence
of Olfactory, Taste, and Visual Inputs
in the Orbitofrontal Cortex
Taste and olfactory pathways are brought together in the
orbitofrontal cortex where flavor is formed by learned
associations at the neuronal level between these inputs (see
Fig. 1; Rolls and Baylis 1994; Critchley and Rolls 1996c;
Rolls et al. 1996a; Verhagen et al. 2004). Visual inputs also
become associated by learning in the orbitofrontal cortex
with the taste of food to represent the sight of food and
contribute to flavor (Thorpe et al. 1983; Rolls 1996). The
visual and olfactory as well as the taste inputs represent the
reward value of the food, as shown by sensory-specific
satiety effects (Critchley and Rolls 1996b). Most neurons in
the taste insula did not respond to odor. Of 24 neurons in
the insular taste cortex tested fully during experiments
described by Verhagen et al. (2004) with a range of odors,
two had marginally significantly different responses be-
tween odors (close to p< 0.05 with no correction for the
number of tests applied), and the evoked changes in firing
rate were on average even for these two neurons a small
proportion (27%) of the changes elicited by taste in the
same neurons. Consistent with this, activations in the
human insular cortex can sometimes be found to odor
(Verhagen and Engelen 2006; Grabenhorst et al. 2007;
Grabenhorst and Rolls 2009; Rolls et al. 2009a), and these
may reflect backprojections (Grabenhorst et al. 2007; Rolls
2008a), which are implicated in memory recall (Treves and
Rolls 1994; Rolls 2008a), for when an odor or flavor
retrieves a representation of a sweet taste, the insula is
activated (Veldhuizen and Small, personal communication).
The agranular insula, anterior to the primary taste cortex, is
activated by both taste and odor (de Araujo et al. 2003c; see
also Small et al. (2004)). The mid-insula is activated by oral
texture (de Araujo and Rolls 2004). More posterior regions
of the insula contain a representation of one’s own body
(McCabe et al. 2008).
18 Chem. Percept. (2010) 3:16–33

The Texture of Food, Including Fat Texture
Some orbitofrontal cortex neurons have oral texture-related
responses that encode parametrically the viscosity of food
in the mouth (shown using a methyl cellulose series in the
range 1–10,000 cP), others independently encode the
particulate quality (gritty texture) of food in the mouth,
produced quantitatively for example by adding 20- to 100-µm
microspheres to methyl cellulose (Rolls et al. 2003a), and
others encode the oral texture of fat (Rolls et al. 1999;
Verhagen et al. 2003), as illustrated in Fig. 2. The fat-
responsive neurons respond to naturally fatty foods such as
dairy cream, vegetable oil, triolein, and chocolate and also to
non-fat oils including silicone oil and mineral oil and do not
respond to the fatty acid linoleic acid (Rolls et al. 1999;
Verhagen et al. 2003). The basis of oral fat sensation is thus
largely by oral texture. Moreover, the pleasantness or reward
value of fat in the mouth is mediated by this system in that
feeding to satiety reduces the responses of these fat-
responsive neurons to zero (Rolls et al. 1999). A few cortical
neurons do respond to fatty acids in the mouth (Verhagen et
al. 2003), consistent with a peripheral fatty acid sensing
mechanism (Gilbertson 1998), but these cortical neurons do
not respond to the fats just described in the mouth (Verhagen
et al. 2003,2004). It may be that free fatty acids in foods act
as a warning signal and are unpleasant (Mattes 2009), and
consistent with this, food manufacturers aim to keep free
fatty acids to a minimum.
In addition, recent findings (Kadohisa et al. 2004,2005)
have revealed that some neurons in the orbitofrontal cortex
reflect the temperature of substances in the mouth and that
this temperature information is represented independently
of other sensory inputs by some neurons and in combina-
tion with taste or texture by other neurons.
Imaging Studies in Humans
Taste
In humans, it has been shown in neuroimaging studies
using functional magnetic resonance imaging (fMRI) that
taste activates an area of the anterior insula/frontal
operculum, which is probably the primary taste cortex,
and part of the orbitofrontal cortex, which is probably the
secondary taste cortex (Francis et al. 1999;O’Doherty et al.
2001;deAraujoetal.2003a). Activation in more
widespread brain areas has been reported by others (Small
et al. 2003). Within individual subjects, separate areas of
the orbitofrontal cortex are activated by sweet (pleasant)
and by salt (unpleasant) tastes (O’Doherty et al. 2001).
Francis et al. (1999) also found activation of the human
amygdala by the taste of glucose. Extending this study,
O’Doherty et al. (2001) showed that the human amygdala
was as much activated by the affectively pleasant taste of
glucose as by the affectively negative taste of NaCl and
thus provided evidence that the human amygdala is not
especially involved in processing aversive as compared to
rewarding stimuli. Zald et al. (1998) had shown earlier that
the amygdala, as well as the orbitofrontal cortex, responds
to aversive (saline) taste stimuli.
Umami taste stimuli, of which an exemplar is mono-
sodium glutamate (MSG) and which capture what is
described as the taste of protein, activate the insular
(primary), orbitofrontal (secondary), and anterior cingulate
(tertiary; Rolls 2008b) taste cortical areas (de Araujo et al.
2003b). When the nucleotide 0.005 M inosine 5′-mono-
phosphate (IMP) was added to MSG (0.05 M), the blood
oxygenation-level dependent (BOLD) signal in an anterior
part of the orbitofrontal cortex showed supralinear additiv-
Fat responsive neurons respond independently of viscosity e.g.
bk265
280
50
55
40
25
0
5
10
15
20
1 10 100 1000 10000
Viscosity (cP)
Firing rate (spikes/sec;
mean+/-sem)
silicone oil
CMC series
mineral oil
coconut oil
vegetable oil
safflower oil
Fig. 2 A neuron in the primate orbitofrontal cortex responding to the
texture of fat in the mouth independently of viscosity. The cell
(bk265) increased its firing rate to a range of fats and oils (the
viscosity of which is shown in centipoise). The information that
reaches this type of neuron is independent of a viscosity sensing
channel in that the neuron did not respond to the methyl cellulose
(CMC) viscosity series. The neuron responded to the texture rather
than the chemical structure of the fat in that it also responded to
silicone oil (Si(CH
3
)
2
O)
n
) and paraffin (mineral) oil (hydrocarbon).
Some of these neurons have taste inputs. After Verhagen et al. (2003)
Chem. Percept. (2010) 3:16–33 19

ity, and this may reflect the subjective enhancement of
umami taste that has been described when IMP is added to
MSG (Rolls 2009a). Overall, these results illustrate that the
responses of the brain can reflect inputs produced by
particular combinations of sensory stimuli with supralinear
activations and that the combination of sensory stimuli may
be especially represented in particular brain regions and
may help to make the food pleasant.
Odor
In humans, in addition to activation of the pyriform
(olfactory) cortex (Zald and Pardo 1997; Sobel et al.
2000; Poellinger et al. 2001), there is a strong and
consistent activation of the orbitofrontal cortex by olfactory
stimuli (Zatorre et al. 1992; Francis et al. 1999), and this
region appears to represent the pleasantness of odor, as shown
by a sensory-specific satiety experiment with banana vs
vanilla odor (O’Doherty et al. 2000). Furthermore, pleasant
odors tend to activate the medial and unpleasant odors the
more lateral, orbitofrontal cortex (Rolls et al. 2003b), adding
to the evidence that it is a principle that there is a hedonic
map in the orbitofrontal cortex and also in the anterior
cingulate cortex, which receives inputs from the orbitofrontal
cortex (Rolls and Grabenhorst 2008).
Olfactory–Taste Convergence to Represent Flavor
and the Influence of Satiety
Convergence for taste (e.g., sucrose) and odor (e.g.,
strawberry), and in some cases supralinearity reflecting
interactions, were found in the orbitofrontal cortex and the
adjoining agranular insula and anterior cingulate cortex (de
Araujo et al. 2003c; Small et al. 2004; Small and Prescott
2005; Verhagen and Engelen 2006; Verhagen 2007). These
activations in the orbitofrontal and anterior cingulate cortex
were correlated with the pleasantness ratings given by the
participants (de Araujo et al. 2003c). These results provide
evidence on the neural substrate for the convergence of
taste and olfactory stimuli to produce flavor in humans and
where the pleasantness of flavor is represented in the
human brain.
McCabe and Rolls (2007) have shown that the conver-
gence of taste and olfactory information appears to be
important for the delicious flavor of umami. They showed
that when glutamate is given in combination with a
consonant, savory, odor (vegetable), the resulting flavor
can be much more pleasant than the glutamate taste or
vegetable odor alone and that this reflected activations in
the pregenual cingulate cortex and medial orbitofrontal
cortex. The principle is that certain sensory combinations
can produce very pleasant food stimuli, which may of
course be important in driving food intake.
To assess how satiety influences the brain activations to
a whole food which produces taste, olfactory, and texture
stimulation, we measured brain activation by whole foods
before and after the food is eaten to satiety. The foods eaten
to satiety were either chocolate milk or tomato juice. A
decrease in activation by the food eaten to satiety relative to
the other food was found in the orbitofrontal cortex
(Kringelbach et al. 2003), but not in the primary taste
cortex. This study provided evidence that the subjective
pleasantness of the flavor of food and sensory-specific
satiety are represented in the orbitofrontal cortex. Further
evidence that the reward value of food is represented in the
orbitofrontal cortex is that activations to taste in the
orbitofrontal cortex (OFC) but not in the insula are
enhanced by paying attention to pleasantness (Grabenhorst
and Rolls 2008). Furthermore, activations related to the
affective value of umami taste and flavor (as shown by
correlations with pleasantness ratings) in the orbitofrontal
cortex were modulated by cognitive word-level descriptors
(such as “rich delicious taste”) that enhanced the pleasant-
ness of the taste and flavor. Affect-related cognitive
modulations were not found in the insular taste cortex
where the intensity but not the pleasantness of the taste was
represented (and it would have been interesting to check for
a dissociation in a study in which expectancy reduced the
aversiveness of a bitter taste (Nitschke et al. 2006), as we
have done between correlations of activations in different
brain regions with intensity vs affective value.
In our studies, we have been careful to identify the taste
insula as the region that responds in a contrast of a pure
taste stimulus—a tasteless control (O’Doherty et al. 2001;
de Araujo et al. 2003b; Grabenhorst and Rolls 2008;
Grabenhorst et al. 2008), and this region is at the anterior
end of the human insula (with Ycoordinates; Collins et al.
1994) in the approximate range of 14 to 3 mm (de Araujo et
al. 2003c; Grabenhorst et al. 2008). This is a region where
we have found that activations correlate with the intensity
but not pleasantness ratings of taste and are enhanced to a
taste when paying attention to its intensity but not to its
pleasantness (Grabenhorst and Rolls 2008; Grabenhorst et
al. 2008). In a more mid or posterior part of the insula
(Y=−14), activations to oral texture are found (de Araujo
and Rolls 2004), there is a reduction in activations to
chocolate when it is eaten to satiety (Kringelbach et al.
2003), and water (which refreshes the dry sensation in the
mouth) produces a larger activation when thirsty than when
satiated (de Araujo et al. 2003a). There may therefore be a
representation of the pleasantness of oral texture in the mid
(somatosensory) insula. In front of the insular taste cortex is
agranular insula (close to Y=15), and this is a multimodal
region at the posterior boundary of the orbitofrontal cortex
in which taste and olfactory convergence occur (de Araujo
et al. 2003c), and this could be a region continuous with the
20 Chem. Percept. (2010) 3:16–33

orbitofrontal cortex in which the pleasantness of flavor is
represented. When performing satiety experiments and
finding little change of activation in the insular taste cortex,
we use normal physiological hunger with just a few hours
(e.g., 3–4) of deprivation, we allow participants to feed
themselves to normal satiety rather than give a predeter-
mined load that may not fully satiate or may oversatiate,
and we measure responses to the particular food eaten to
satiety with responses to a food that has not been eaten to
satiety in a sensory-specific satiety design (Kringelbach et
al. 2003; Grabenhorst et al. 2009). Possible differences
between studies with respect to whether the taste insula of
humans is affected by internal state to represent the reward
value of taste (Smeets et al. 2006; Uher et al. 2006; Haase
et al. 2009) may reflect different ways in which the
experiments were performed or not separating the taste
insula from other parts of the insula. An effect in both the
orbitofrontal cortex and the insula of reinforcer devaluation
by satiety in a visual to olfactory association task has been
reported (Gottfried et al. 2003). Also, effects of the
appetite-increasing hormone ghrelin on activations to the
sight of food were found in the orbitofrontal cortex, insula,
and many other areas including the pulvinar (Malik et al.
2008). We note that other parts of the insula may map
visceral/interoceptive activity (Craig 2002,2009) and play
a role in autonomic activity (Critchley 2005), which may be
related to state-dependent effects of for example satiety
(Gautier et al. 2001).
Oral Viscosity and Fat Texture
The viscosity of food in the mouth is represented in the
human primary taste cortex (in the anterior insula) and also
in a mid-insular area that is not taste cortex but which
represents oral somatosensory stimuli (de Araujo and Rolls
2004). Oral viscosity is also represented in the human
orbitofrontal and perigenual cingulate cortices, and it is
notable that the perigenual cingulate cortex, an area in
which many pleasant stimuli are represented, is strongly
activated by the texture of fat in the mouth and also by oral
sucrose (de Araujo and Rolls 2004; Grabenhorst et al.
2009).
The Sight of Food
O’Doherty et al. (2002) showed that visual stimuli
associated with the taste of glucose activated the orbito-
frontal cortex and some connected areas, consistent with the
primate neurophysiology. Simmons et al. (2005) found that
showing pictures of foods, compared to pictures of
locations, can also activate the orbitofrontal cortex. Simi-
larly, the orbitofrontal cortex and connected areas were also
found to be activated after presentation of food stimuli to
food-deprived subjects (Wang et al. 2004). Backprojections
from these multimodal areas in the orbitofrontal cortex that
receive visual inputs from the inferior temporal visual cortex
(Rolls and Baylis 1994; Rolls 2000,2008a) may produce
some activations to the sight of food in earlier cortical areas.
Cognitive Effects on Representations of Food
To what extent does cognition influence the hedonics of
food-related stimuli and how far down into the sensory
system does the cognitive influence reach? To address this,
we performed an fMRI investigation in which the delivery
of a standard test odor (isovaleric acid combined with
cheddar cheese flavor, presented orthonasally using an
olfactometer) was paired with a descriptor word on a
screen, which on different trials was “cheddar cheese”or
“body odor.”Participants rated the affective value of the
test odor as significantly more pleasant when labeled
“cheddar cheese”than when labeled “body odor,”and
these effects reflected activations in the medial OFC/rostral
anterior cingulate cortex that had correlations with the
pleasantness ratings (de Araujo et al. 2005; see Fig. 3). The
implication is that cognitive factors can have profound
effects on our responses to the hedonic and sensory
properties of food, in that these effects are manifest quite
far down into sensory processing, so that hedonic represen-
tations of odors are affected (de Araujo et al. 2005). Similar
cognitive effects and mechanisms have now been found for
the taste and flavor of food (Grabenhorst et al. 2008).
In addition, it has been found that with taste, flavor, and
olfactory food-related stimuli, attention to pleasantness
modulates representations in the orbitofrontal cortex,
whereas attention to intensity modulates activations in
areas such as the primary taste cortex (Grabenhorst and
Rolls 2008; Rolls et al. 2008; cf. Veldhuizen et al. 2007).
When one reward is delivered, it can influence the
pleasantness of the next reward. Using fMRI, we investi-
gated how the subjective pleasantness of an odor is
influenced by whether the odor is presented in the context
of a relatively more pleasant or less pleasant odor
(Grabenhorst and Rolls 2009). We delivered two of a set
of four odors separated by a delay of 6 s, with the instruction
to rate the pleasantness of the second odor, and searched for
brain regions where the activations were correlated with the
absolute pleasantness rating of the second odor and for brain
regions where the activations were correlated with the
difference in pleasantness of the second from the first odor,
that is, with relative pleasantness. Activations in the antero-
lateral orbitofrontal cortex tracked the relative subjective
pleasantness, whereas activations in the anterior insula
tracked the relative subjective unpleasantness. In contrast,
in the medial and mid-orbitofrontal cortex, activations
Chem. Percept. (2010) 3:16–33 21

tracked the absolute pleasantness of the stimuli. Thus, both
relative and absolute subjective value signals which provide
important inputs to decision-making processes about which
stimulus to choose are separately and simultaneously
represented in the human brain (Grabenhorst and Rolls
2009). Relative reward value is important for the choice
between a set of available rewards, and absolute reward
value for stable and consistent economic choice.
These findings have important implications for sensory
testing and for ways in which the palatability and accept-
ability of foods can be influenced.
Information Theoretic Analysis of the Representation
of Taste and Odor in the Orbitofrontal Cortex
Taste
Two issues about the nature of the gustatory representation
in the orbitofrontal cortex that have not been addressed
previously are considered here. The first issue is how robust
or reliable are the responses of primate orbitofrontal cortex
gustatory neurons. To answer this, we apply an information
theoretic approach and analyze how much information we
0
0
4
2
AB
CD
R
z
Y = 0
Y = 0
Y = 15
Y = 15
Y = 42
Y = 42
X = 13
X = 13
16
8
0-2 -1 012
0.4
0.0
-0.4
PST (sec)
-2
-1
0
1
2
0
8
16
0
0.2
0.5
0.7
EF
BOLD (% change)
PST (sec) Pleasantness
Ratings
Pleasantness
Ratings
BOLD (% change)
Fig. 3 Cognitive influences on
olfactory representations in the
human brain. Group (random)
effects analysis showing the
brain regions where the BOLD
signal was correlated with
pleasantness ratings given to the
test odor. The pleasantness rat-
ings were being modulated by
the word labels. aActivations in
the rostral anterior cingulate
cortex, in the region adjoining
the medial OFC, shown in a
sagittal slice. bThe same acti-
vation shown coronally. cBilat-
eral activations in the amygdala.
dThese activations extended
anteriorly to the primary olfac-
tory cortex. The image was
thresholded at p<0.0001 uncor-
rected in order to show the
extent of the activation. ePara-
metric plots of the data averaged
across all subjects showing that
the percentage BOLD change
(fitted) correlates with the
pleasantness ratings in the re-
gion shown in aand b. The
parametric plots were very
similar for the primary olfactory
region shown in d.PST post-
stimulus time (s). fParametric
plots for the amygdala region
shown in c. After de Araujo
et al. (2005)
22 Chem. Percept. (2010) 3:16–33

obtain on average on a single trial from the responses of
one of these neurons. If they were noisy, then we would
obtain little information from a single neuron on a single
trial. If the neuron responded to only one stimulus in a large
set of stimuli and had little response to all the other stimuli
in the set, then again on average we would learn only little
on a given trial from the responses of the neuron (assuming
equiprobable stimuli). The second issue is how finely tuned
the neurons are to the stimuli, that is whether a neuron
responds to only one stimulus in a set or whether it
responds to some but not other stimuli in the set. We
analyzed this by using information theory to measure how
much information was obtained from a cell for each
stimulus in the set. If the neuron responded to one stimulus
only in the set, then considerable information might be
available from the neuron when that stimulus was presented
(subject to noise), and little information would be available
about each of the other stimuli in the set.
Methods
Neuronal Recordings
The information theoretic approach and its application to
the analysis of neuronal data are described in detail
elsewhere (Rolls and Treves 1998; Rolls and Deco 2002;
Rolls 2008a). By way of introduction, information theory
(Shannon 1948) provides the means for quantifying how
much information neurons communicate to other neurons
and thus provides a quantitative approach to fundamental
questions about information processing in the brain. To
investigate what in neuronal activity carries information,
one must compare the amounts of information carried by
different codes, that is, different descriptions of the same
activity, to provide the answer. To investigate the speed of
information transmission, one must define and measure
information rates from neuronal responses. To investigate to
what extent the information provided by different cells is
redundant or instead independent, again one must measure
amounts of information in order to provide quantitative
evidence. To compare the information carried by the
number of spikes, by the timing of the spikes within the
response of a single neuron, and by the relative time of
firing of different neurons reflecting for example stimulus-
dependent neuronal synchronization, information theory
again provides a quantitative and well-founded basis for
the necessary comparisons. To compare the information
carried by a single neuron or a group of neurons with that
reflected in the behavior of the human or animal, one must
again use information theory, as it provides a single
measure which can be applied to the measurement of the
performance of all these different cases. To compare the
information encoded by neurons with that which can be
read from brain activations obtained with functional neuro-
imaging, information theory again provides a common
metric (Rolls et al. 2009b). In all these situations, there is
no quantitative and well-founded alternative to information
theory (Rolls 2008a).
The gustatory stimuli used were 1.0 M glucose (G),
0.1 M NaCl (N), 0.01 M HCl (H), 001 M QHCl (Q), and
0.1 M monosodium glutamate (M); and 0.001 M tannic
acid was used as an astringent stimulus, and the recordings
were from the macaque orbitofrontal cortex. The recordings
are part of a series of investigations in which the functions
of the orbitofrontal cortex are being analyzed to provide
evidence on feeding, taste and olfaction, and their disorders
(Rolls 2007a,b,2009b) and on the causes of the emotional
and motivational problems that can occur in patients with
damage to this brain region (Rolls et al. 1994; Rolls 1999,
2005; Hornak et al. 2003; Berlin et al. 2004). It is important
that such neurophysiological studies directed towards
understanding the function of the orbitofrontal cortex in
humans be performed on primates for even the anatomical
connections of the taste and olfactory systems are very
different in primates from those in rodents (Norgren 1984,
1988; Rolls 2008b), and in addition, the orbitofrontal cortex
is considerably less developed in rodents compared to its
great development in primates (Zald and Rauch 2006).
Single Cell Information Analysis
The single cell information analysis methods used have
been described in detail (Rolls et al. 1996a,1997b). A
novel aspect of the data analysis methods is that we
investigated how much information was available about
each stimulus in the set. Because we have found that most
of the cortical information about which stimulus was
presented is made evident by measuring the firing rate of
the neuron and not variations in its time course (Tovee et al.
1993; Rolls et al. 1996a; Rolls 2008a), the information
theoretic analyses described here were based on the
information available from the firing rate. The period in
which this was measured was the post-stimulus period 100–
600 ms with respect to the onset of the taste stimulus.
Although there are some differences in the time courses of
the neuronal responses to different tastes in the nucleus of
the solitary tract (Scott et al. 1985; Hallock and Di Lorenzo
2006), we note that there are no strong indications that the
time courses of the neuronal responses are very different for
different tastants in the cortex, as shown by published
examples (Scott et al. 1986; Yaxley et al. 1988,1990; Rolls
et al. 1990,1996c,1999,2003a; Critchley and Rolls 1996a;
Verhagen et al. 2003,2004; Kadohisa et al. 2004) and in
that our analyses provide similar results and similar tuning
for 1-, 3-, or 5-s analyses, and in any case, the information
theoretic analysis focused on the first 500 ms of cortical
Chem. Percept. (2010) 3:16–33 23

neuronal responses where any possible differences are
minor. Of course, as expected, oral texture stimuli may
have different time courses, with very viscous stimuli
producing longer responses as it takes longer to clear a
thick stimulus from the mouth (Rolls et al. 2003a; Verhagen
et al. 2004).
The measure was the stimulus-specific information or
surprise, I(s,R), which is the amount of information the set
of responses, R, has about a specific stimulus, s. The mutual
information between the whole set of stimuli Sand of
responses Ris the average across stimuli of this stimulus-
specific information (note that ris an individual response
from the set of responses R).
Is;RðÞ¼
X
r2R
Prs
j
ðÞlog2
Prs
j
ðÞ
PðrÞð1Þ
One hundred thirty-five neurons, representing 5.7% of the
2,374 orbitofrontal cortex and related neurons tested in
three macaques, had gustatory responses (using a criterion
of a significant difference, p<0.001 for most cells,
between the responses to the different tastants in an
ANOVA; Critchley and Rolls 1996a; Rolls et al. 1996c,
1999). As described in those papers, the well-isolated
single neurons were recorded with glass-insulated tungsten
microelectrodes (Merrill and Ainsworth 1972). The liquid
taste stimuli were delivered manually in aliquots of 0.2 ml
via a 1-ml syringe. The monkey’s mouth was rinsed with
distilled water during the intertrial interval, which lasted a
minimum of 30 s or until activity returned to baseline
levels. The manual presentation of taste stimuli was
chosen to allow the repeated stimulation of a large
receptive field despite changing mouth and tongue
positions of the monkey (Scott et al. 1986; Rolls et al.
1990). The response properties of these 135 neurons,
examples of their responses, and the criteria for identifi-
cation have been described previously (Critchley and
Rolls 1996a;Rollsetal.1996c,1999). The numbers of
the 135 neurons with best responses to each of the tastants
1MglucoseG,0.1MNaClN,0.01MHClH,0.001M
quinine-HCl Q, 0.1 M monosodium glutamate M, and
distilledwaterWareshowninFig.5c. The responses of
the neurons were measured in a 0.5-s period starting
100 ms after taste delivery. A further 110 neurons (4.8%)
showed significant responses to the delivery of the
tastants, but were either not tested fully or did not
discriminate between the tastants (and thus were probably
responding to somatosensory input associated with the
delivery of the tastants into the mouth). Of the other
neurons in the sample of 2,374 neurons, some responded
to oral astringency as exemplified by tannic acid (Critchley
and Rolls 1996a), some to oral fat texture (Rolls et al. 1999),
some to olfactory stimuli (Critchley and Rolls 1996b,c; Rolls
et al. 1996b,1996a), some to food-related visual stimuli
(Critchley and Rolls 1996b), and some visual neurons to face
expression or face identity (Rolls et al. 2006).
Tastants
QNWHMG
Firing rate spikes/sec
20
15
10
5
0
Information about each tastant [I(si)] (bits)
2.01.51.00.50.0
Firing rate spikes/sec
20
15
10
5
0
W
M
Q
H
N
G
Information about each tastant [I(si)] (bits)
2.01.51.00.50.0
z-score of firing rates
10
5
0
-5
-10
W
M
Q
H
N
G
a
c
b
Fig. 4 a Response profile of a typical glucose-responsive neuron
(aq103a) showing the mean evoked firing rate of the neuron to the
tastants glucose, G, (1.0 M); NaCl, N, (0.1 M); HCl, H, (0.01 M);
quinine-HCl, Q, (0.001 M), monosodium glutamate, M, (0.1 M), and
distilled water, W. The evoked firing rates are plotted as changes from
the spontaneous firing rate of the neuron. bRelationship between the
firing rate of the neuron (ordinate) plotted as a function of the
information (I(s
i
) in the responses of the neuron about each tastant i.c
Relationship between the zscore of the responses to each tastant
(ordinate) plotted as a function of the information in the response to
each tastant
24 Chem. Percept. (2010) 3:16–33

Results
Single Cell Stimulus-Specific Information About Taste
Figure 4a illustrates the response profile of a typical taste
neuron (aq103a). In Fig. 4b, the amount of information
reflected in the response to each tastant is plotted against
the mean evoked firing rates. The neuron responded to the
taste of glucose with a mean evoked firing rate of 16.5
spikes per second. The remaining stimuli evoked firing
rates of between 0.5 and 8 spikes per second, showing this
cell to be a glucose-best neuron. Figure 4b shows I(s
i
), the
information about each tastant when that tastant was
presented, as a function of the firing rate of the neuron to
the tastant being applied. The information from the
responses to glucose (I(s
i
)) approached 2.0 bits, but when
quinine was delivered, more than 1 bit of information was
available from the neuronal response, even though the
neuron fired very little to the quinine. The explanation for
this is clarified by Fig. 4c. In this figure, the information to
each tastant is plotted against the number of standard
deviations the neuronal response was away from the average
response to all tastants (termed the zscore in Fig. 4c). The z
score was calculated from the difference between the mean
firing rate to the tastant and the average evoked firing rate to
all tastants divided by the standard deviation of the response
to the tastant. The absolute magnitude of the zscore thus
reflects the probability that such a response will occur. It is
shown in Fig. 4c (cell aq103a) that the considerable
information provided when quinine was the stimulus was
related to the fact that such a low neuronal response was
improbable, and thus, much was learned when that response
occurred. Similar types of graph were found for other
neurons responding best to each of the other tastants.
Tastants
WMQHNG
Average information about
each stimulus (bits)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
Tastants
WMQHNG
Average response (spikes/sec)
0
2
4
6
8
10
12
Tastants
WMQHNG
Number of neurons with best
0
10
20
30
40
50
other
c
-2 -1 0 1 2
-2
-1
0
1
2
G
BJ
T42
N
T23/V1
LaA
LiA
Q
T10
T37
H
V10
V10000
MO
OVO
Gr
FVO
CO
SiO
SaO
V1000
SC
V100
MSG
Cap
X
Y
d
a
b
response
Fig. 5 a Information (I(s
i
)) about the set of six tastants (glucose
1.0 M, NaCl, 0.1 M, HCl, 0.01 M, quinine-HCl 0.001 M, mono-
sodium glutamate 0.1 M, and distilled water) averaged across the
population of 135 gustatory neurons. bAverage response evoked by
each of the tastants averaged across the population of 135 neurons. c
Number of neurons with optimal responses to each of the tastants in
the population of 135 gustatory neurons (a–cnew analyses of data of
Critchley and Rolls 1996a; Rolls et al. 1996b,1999). dMultidimen-
sional space from 53 orbitofrontal cortex neurons’responses to taste,
oral texture, and oral temperature stimuli from Kadohisa et al. (2005).
The taste stimuli were 1 M glucose (G), 0.1 M NaCl (N), 0.1 M MSG
(M), 0.01 M HCl (H), and 0.001 M quinine-HCl (Q); the temperature
stimuli were T10, T23, T37, and T42 where the number indicates the
temperature in °C; the viscosity stimuli were V1, V10, V100, V1000,
and V10000 where the numeral indicates the viscosity in centipoise;
fat texture stimuli were SiO10, SiO100, SiO1000 (silicone oil with the
viscosity indicated),vegetable oil (VO), coconut oil (CO), and
safflower oil (SaO). BJ fruit juice, Cap 10 μM capsaicin, LaA
0.1 mM lauric acid, LiA 0.1 mM linoleic acid, Gr gritty stimulus. The
solid line joins the members of the viscosity series. Different line
styles join the members of the taste, temperature, and oil stimuli
Chem. Percept. (2010) 3:16–33 25

Figure 5a shows the stimulus-specific information (I(s
i
))
about each of the six tastants (glucose, NaCl, HCl, quinine-
HCl, monosodium glutamate, and distilled water) averaged
across the population of 135 taste-responsive cells. There
was most information about the sweet taste of glucose. The
data support and quantify what has been noted in previous
studies (Rolls et al. 1990; Baylis and Rolls 1991; Rolls and
Baylis 1994; Kadohisa et al. 2005) that the orbitofrontal
cortical taste neurons tend to represent sweet tastes much
more than other tastes. In Fig. 5b, the average responses
(firing rate—spontaneous) evoked by the different tastants
across the 135 cells is shown. This graph does not clearly
illustrate the differential way in which these taste qualities
are reflected by the neuronal responses across the popula-
tion. In Fig. 5c, the number of neurons responding
preferentially to each of the tastants is shown. In the latter
graph, the proportion of cells responding preferentially to
glucose is clearly much larger than the other tastants, yet
this difference is not particularly evident from the average
responses shown in Fig. 5b. However, Fig. 5c does not
reflect the degree to which the optimal stimulus of a taste
neuron, for example glucose, is able to be differentiated
from other (suboptimal) stimuli. This is, however, reflected
in the information about individual stimuli shown in Fig. 5a
which illustrates an advantage of the information theoretic
approach to neural representation. Consistent with this, but
in a visual and less quantitative way, glucose is well
separated from other taste and oral stimuli in a multidi-
mensional scale space based on a later sample of 53
orbitofrontal cortex neurons, as shown in Fig. 5d (Kadohisa
et al. 2005).
Single Cell Average Information About the Set of Taste
Stimuli
In all the data above, the information was calculated for
the responses of cells to the individual tastants (I(s
i
)).
Another approach is to calculate the average information
reflected in the responses of each neuron about a stimulus
set (I(S,R)). For the set of six tastants, the average
information about which tastant was present, I(S,R), was
0.45 bits (SD=0.26), averaged across neurons (see Fig. 6).
This is quite a high value, indicating a robust representa-
tion of taste in the orbitofrontal cortex. Robust here
30
20
10
0
Std. Dev = .26
Mean = .45
N = 135.00
0.05 0.25 0.45 0.65 0.85 1.05 1.25 1.45
Number of neurons
Average information [I(S,R)] about set of tastants (bits)
Fig. 6 Histogram showing the average information, I(S,R), about the
set of prototypical tastants, glucose, NaCl, HCl, and quinine-HCl
0.950.850.750.650.550.450.350.250.150.05
60
50
40
30
20
10
0
Std. Dev = .14
Mean = .84
Number of cells
Sparseness from firing rate
0.950.850.750.650.550.450.350.250.150.05
40
30
20
10
0
Std. Dev = .17
Mean = .70
Number of cells
Sparseness from response
b
a
Fig. 7 a Histogram of the sparseness values calculated from the
evoked firing rates of the neurons to the set of prototypical tastants
(glucose, NaCl, HCl, and quinine-HCl). bHistogram of the sparseness
values calculated from the responses (evoked firing rate minus
spontaneous firing rate) of the neurons to the set of prototypical
tastants
26 Chem. Percept. (2010) 3:16–33

signifies relatively low variability and relatively large
differences in firing rate to the different stimuli (so that
information can be easily read out). For comparison, the
mutual information about a set of 20 faces encoded by
inferior temporal cortex neuronswas0.36bits(Rollsetal.
1997b) and of nine odors by orbitofrontal cortex neurons
was0.09bits(Rollsetal.1996a). It was found that if a
neuron had a high average information, it was often
responsive to several of the taste stimuli, but with clearly
different rates to each stimulus. Neurons with a high
stimulus-specific information, but to only one stimulus,
i.e., neurons with fine tuning as described below, tended
to have intermediate values of the average information,
as expected. Neurons with rather broad tuning, i.e.,
rather similar responses to the different stimuli, tended
to have low values of the mutual information (see further
below).
Breadth of Tuning
A measure of the breadth of tuning of a single neuron to a
set of stimuli can be calculated (Smith and Travers 1979)as
a coefficient of entropy (H) derived from the proportion of
a neuron’s total response that is devoted to each of the basic
tastants (p
i
). A scaling constant (k) is applied such that were
the neuron to respond equally to all stimuli, then H= 1.0.
The coefficient of entropy, H, hence the measure of breadth
of tuning is as follows:
H¼kΣni¼1pilog pi:ð2Þ
Total specificity to only one stimulus would result in a
coefficient of entropy of 0.
The mean breadth of tuning (H) for the prototypical
tastants (glucose 1.0 M, NaCl, 0.1 M, HCl, 0.01 M, and
quinine-HCl 0.001 M) of the population of 135 neurons was
0.77 (SD= 0.20). There was a small negative correlation
(Pearson correlation coefficient=−0.32) between the average
information and the breadth of tuning measure. It is clear that
the breadth of tuning measure cannot be confidently used to
predict the amount of information in the responses of the
population of cells about a stimulus set, as the breadth of tuning
does not reflect the reliability/variability of neuronal responses.
Sparseness of the Representation of the Prototypical
Tastants
The sparseness, a, of the representation of a set of (taste)
stimuli provided by the neurons can be defined and
calculated as:
a¼Xi¼1;nri=nðÞ

2=Xi¼1;nr2
i=n
 ð3Þ
Fig. 8 Location of the orbito-
frontal cortex gustatory neurons
in the single cell taste informa-
tion analysis. These neurons
were recorded in the studies of
Critchley and Rolls (1996a) and
Rolls et al. (1996c,1999)
Chem. Percept. (2010) 3:16–33 27

where r
i
is the firing rate to the ith stimulus in the set of n
stimuli. The sparseness has a maximal value of 1.0. This is
a measure of the extent of the tail of the distribution, in this
case of the firing rates of the neuron to each stimulus. A
low value indicates that there is a long tail to the
distribution, equivalent in this case to only a few neurons
with high firing rates. If these neurons were binary (either
responding with a high firing rate or not responding), then a
value of 0.2 would indicate that 20% of the neurons had
high firing rates and 80% had no response. In the more
general case of a continuous distribution of firing rates, the
sparseness measure, a, still provides a quantitative measure
of the length of the tail of the firing rate distribution (Treves
and Rolls 1991). This measure of the sparseness of the
representation of a set of stimuli by a single neuron has a
number of advantages. One is that it is the same measure of
sparseness which has proven to be useful and tractable in
formal analyses of the capacity of neural networks that use an
approach derived from theoretical physics (see Treves 1990;
Treves and Rolls 1991; Rolls and Treves 1990). A second is
that it can be applied to neurons which have continuously
variable (graded) firing rates and not just to firing rates with
a binary distribution (e.g., 0 or 100 spikes per second; Treves
and Rolls 1991). A third is that it makes no assumption
about the form of the firing rate distribution (e.g., binary,
ternary, exponential etc.) and can be applied to different
firing rate distributions (Treves and Rolls 1991). Fourth, it
makes no assumption about the mean and the variance of the
firing rate. Fifth, the measure does not make any assumption
about the number of stimuli in the set and can be used with
different numbers of test stimuli. Its maximal value is always
1.0, corresponding to the situation when a neuron responds
equally to all the stimuli in a set of stimuli. The use of this
measure of sparseness in neurophysiological investigations
has the advantage that the neurophysiological findings then
provide one set of the parameters useful in understanding
theoretically (Treves and Rolls 1991; Rolls and Treves 1990;
Franco et al. 2007) how the system operates.
The sparseness values for the population of neurons are
shown in Fig. 7a. In addition, a second sparseness measure,
calculated from the responses and not the evoked firing
rates of the neurons, is illustrated in Fig. 7b. The sparseness
values from both the firing rates and the responses were
high (0.84 and 0.70, respectively). This is indicative of a
distributed representation of the stimuli. A distributed
encoding of tastes enables fine discriminations to be made
of the tastants while at the same time being conservative
and resistant to degradations of the neural code. Interest-
ingly, the sparseness of the representation provided by
inferior temporal cortex neurons about faces and objects is
approximately 0.7 (Rolls et al. 1997b; Franco et al. 2007)
and by orbitofrontal cortex neurons of odors is 0.93
(Critchley and Rolls 1996c).
The locations of the orbitofrontal cortex neurons in the
single cell taste information study just described are shown
in Fig. 8.
Multiple Cell Information Analysis for Taste: Methods
A multiple cell information measure, the average amount of
information that is obtained about which stimulus was
shown from a single presentation of a stimulus from the
responses of all the cells, enabled measurement of how the
information increases as a function of the number of
neurons considered. If the information increases linearly
with the number of cells, then the information encoded by
each cell is independent of that encoded by the other cells.
For at least small numbers of neurons, and with relatively
large stimulus sets, this is the case for the inferior temporal
visual cortex and is a very powerful type of encoding in
that the number of stimuli represented increases exponen-
tially with the number of neurons in the set (Abbott et al.
0
20
40
60
80
100
0 2 4 6 8 10 12 14
Percent correct
Number of Cells
inform, OFC, 6 tastes, Bayesian Poisson
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
0 2 4 6 8 10 12 14
Information (bits)
Number of Cells
inform, OFC, 6 tastes, Bayesian Poisson
a
b
Fig. 9 Multiple cell taste information analysis for orbitofrontal cortex
for six taste stimuli
28 Chem. Percept. (2010) 3:16–33

1996; Rolls et al. 1997a; Rolls 2008a). If the information
saturates at one cell, then the information encoded by the
set of cells is redundant with respect to each other (Rolls
2008a).
Procedures for calculating the multiple cell information
measure are given by Rolls et al. (1997a). The multiple cell
information measure is the mutual information I(S,R), that
is, the average amount of information that is obtained from
a single presentation of a stimulus about the set of stimuli S
from the responses of all the cells. For multiple cell
analysis, the set of responses, R, consists of response
vectors comprising the responses from each cell.
Ideally, we would like to calculate
IS;RðÞ¼
X
s2S
PðsÞIs;RðÞ:ð4Þ
However, the information cannot be measured directly
from the probability table P(r,s) embodying the relationship
between a stimulus sand the response rate vector rprovided
by the firing of the set of neurons to a presentation of that
stimulus. This is because the dimensionality of the response
vectors is too large to be adequately sampled by trials.
Therefore, a decoding procedure is used in which the
stimulus s′that gave rise to the particular firing rate response
vector on each trial is estimated. This involves for example
maximum likelihood estimation or dot product decoding. For
example, given a response vector rto a single presentation
of a stimulus, its similarity to the average response vector of
each neuron to each stimulus is used to estimate using a dot
product comparison which stimulus was presented. The
probabilities of it being each of the stimuli can be estimated
in this way. Details are provided by Rolls et al. (1997a). A
probability table is then constructed of the real stimuli sand
the decoded stimuli s′. From this probability table, the
mutual information is calculated as:
IS;S'ðÞ¼
X
s;s'
Ps;s'ðÞlog2
Ps;s'ðÞ
PðsÞPs'ðÞ
:ð5Þ
Multiple Cell Information Analysis for Taste: Results
Figure 9shows the multiple cell information analysis for 13
taste neurons from the orbitofrontal cortex in macaque bk
(the multiple cell information analysis can only be performed
within a single animal for the effects of correlations in
neuronal responses would be obscured if the neurons were
from different individuals). Six taste stimuli were used: 0.1 M
NaCl, 0.01 M HCl, 1 M glucose, 0.001 M quinine-HCl, 0.1 M
MSG, and water. Figure 9a shows the multiple cell
information as a function of the number of neurons. The
dashed line shows what would be predicted if the neuronal
responses were independent. Adding neurons clearly pro-
vides more information, but the information does not
increase to the 2.58 bits that would be necessary to
discriminate the stimuli perfectly, and consistent with this,
the percent correct discrimination as a function of the
number of neurons shown in Fig. 9b does not reach 100%.
Furthermore, the neurons do not provide independent
information, that is, there is some redundancy. This is shown
by the finding that the information plot lies below what
would be expected for independent information (the dashed
0
0.2
0.4
0.6
0.8
1
1.2
1.4
0 5 10 15 20 25
Information (bits)
Number of Cells
6 odor stimuli, 24 neurons
0
20
40
60
80
100
0 5 10 15 20 25
Percent correct
Number of Cells
6 odor stimuli, 24 neurons
ab
Fig. 10 Multiple cell olfactory
information
Chem. Percept. (2010) 3:16–33 29

line in Fig. 9a). The less than perfect discrimination between
this set of taste stimuli is consistent with the evidence that
the orbitofrontal cortex specializes in the representation of
the affective quality of tastes rather than their identity
(Kadohisa et al. 2005;Rolls2005). Consistent with this, in
analyses in progress, we are finding that the multiple cell
information for the primary taste cortex, where the identity
of tastes is represented (Rolls 2008b; Rolls and Grabenhorst
2008), does rise further as the number of neurons in the
sample increases. Another factor in the less than independent
encoding is that the taste space is inherently limited by the
relatively small number of taste receptor channels (which
include sweet, salt, bitter, sour, and umami) so that greater
redundancy in the representation may be expected than in
some other sensory modalities (Rolls 2008a).
Odor
Multiple Cell Information Analysis for Odor
A single cells analysis of the representation of information
about a set of nine odor stimuli (eugenol, hexylamine,
phenylethanol, butyric acid, naphthalene, caprylic acid,
citral, amy1 acetate, and vanillin) has shown that the
average information about the stimulus set provided by
each of the 38 neurons was 0.09 bits (Rolls et al. 1996a).
This is low when compared with the information values for
the responses of temporal lobe face-selective neurons but
may reflect the nature of olfactory processing and variabil-
ity in the olfactory responses.
We now describe a new, multiple cell information
analysis of this data set which aims to show how the
information increases when more neurons are considered
and whether the neurons encode information independently.
The neurophysiological methods have been described
previously (Rolls et al. 1996a), and the multiple cell
information theoretic analysis methods used were as de-
scribed above for the taste multiple cell information analyses.
Figure 10 shows the multiple cell odor information analysis
for the orbitofrontal cortex (calculated over the six odors for
which there were sufficient trials and excluding two odors for
which taste associations had been established; Rolls et al.
1996b). It is clear that the information increases approximate-
ly linearly with the number of neurons. The indication thus is
that although the information encoded by each neuron is
relatively small, the total information from the population
increases in a way that enables a population of such neurons
to discriminate the set of stimuli, though more than the 24
neurons in this sample of neurons would be needed.
This principle of independent encoding is useful given that
the stimulus space is large, with hundreds of olfactory
receptor genes, and nonlinear combinations of the effects of
these expanding the space even further (Zou and Buck 2006),
as is usual in sensory systems and as can be implemented by
competitive learning (Rolls 2008a). The independent encod-
ing allows the number of stimuli that can be encoded to
increase exponentially with the number of neurons, given
that information is a logarithmic measure (Abbott et al.
1996; Rolls et al. 1997a; Rolls 2008a).
Synthesis
These investigations show that a principle of brain function
is that representations of the reward/hedonic value and
pleasantness of sensory including food-related stimuli are
formed separately from representations of what the stimuli
are and their intensity. The pleasantness/reward value is
represented in areas such as the orbitofrontal cortex and
pregenual cingulate cortex, and it is here that satiety signals
modulate the representations of food to make them implement
reward in that they only occur when hunger is present. The
satiety signals that help in this modulation may reach the
orbitofrontal cortex from the visceral insula and/or hypothal-
amus, and in turn, the orbitofrontal cortex projects to the
hypothalamus where neurons are found that respond to the
sight, smell, and taste of food if hunger is present (Rolls
2007a; Rolls and Grabenhorst 2008). The insula itself has a
number of partially segregated and partially overlapping
representations, including for taste and odor in the agranular
insula, for taste in the anterior insula, for oral somatosensory
responses to example texture in the midinsula, and a visceral
representation, and a body somatosensory representation
more posteriorly. We have seen above some of the principles
that help make the food pleasant, including particular
combinations of taste, olfactory, texture, visual, and cogni-
tive inputs. In addition, we have gained insight into how
information is encoded by neurons, and by populations of
neurons, in the taste and olfactory systems.
Acknowledgments This research was supported by the Medical
Research Council. The participation of many colleagues in the studies
cited it sincerely acknowledged. Helpful discussions with Fabian
Grabenhorst are appreciated.
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