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Doubting What to Eat: A Computational Model for Food Choice Using Different Valuing Perspectives


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In this paper a computational model for the decision making process of food choices is presented that takes into account a number of aspects on which a decision can be based, for example, a temptation triggered by the food itself, a desire for food triggered by being hungry, valuing by the expected basic satisfaction feeling, and valuing by the expected goal satisfaction feeling.
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Doubting What to Eat: A Computational Model
for Food Choice Using Different Valuing Perspectives
Altaf H. Abro and Jan Treur
Vrije Universiteit Amsterdam, Behavioural Informatics Group
De Boelelaan 1081, 1081 HV Amsterdam, The Netherlands
{a.h.abro, j.treur}
In this paper a computational model for the decision making process of food
choices is presented that takes into account a number of aspects on which a
decision can be based, for example, a temptation triggered by the food itself, a
desire for food triggered by being hungry, valuing by the expected basic
satisfaction feeling, and valuing by the expected goal satisfaction feeling.
Keywords: Computational model, food choice, Hebbian learning, desire.
1 Introduction
The effect of overweight and obesity has been studied extensively in recent years, as
all over the world it is becoming a substantial problem that increases the risk of health
problems, such as diabetes, high blood pressure, cardiovascular diseases and other [1,
2]. Moreover, obesity was found to be associated with an increased risk of morbidity
and mortality [3, 4]. One of the recent studies, published in The Lancet [5] has
described the current trends in obesity and made future predictions about continuation
of these trends: 18% of men and 21% of women will be obese by 2025. Researchers
from different fields such as neuroscience focus on eating behaviour:
‘What we eat, when and how much, all are influenced by brain reward mechanisms
that generate ‘liking’ and ‘wanting’ for foods. As a corollary, dysfunction in reward
circuits might contribute to the recent rise of obesity and eating disorders’ [6], p. 43
Also from a computational perspective attempts have been made to get an
understanding of the decision making process on food choice; e.g., [7]. Computational
models can play a vital role in providing support to lifestyle change. To develop
smart, human-aware applications, modelling the dynamics and interaction of mental
states underlying behavioural choices is becoming more and more important.
In this paper the aspects of eating behaviour and particularly making choices of
food are explored computationally. For that purpose it has been taken into account:
How is desire for food generated in relation to a body unbalance interpreted as
being hungry and/or by any kind of sensed external stimulus. For example,
certain types of food may be sensed and considered attractive by their cues.
How can a person learn to adopt a rational behaviour on the basis of his past
experiences; for example, this can involve Hebbian learning [8, 9].
How does experienced satisfaction play a role, and how can the choice of food
be altered through the satisfaction level.
How influential is the role of goals; for example, a person wants to lose weight
(long term goal) and adopts as a short term goal to eat healthy and light food.
2 Background
In the past decade a number of theories and models have addressed different aspects
that may cause overweight and obesity. This involves different fields including,
psychology, social science, health science, and neuroscience [6, 10]. Researchers
attempt to develop interventions which help people change their lifestyle to avoid
obesity. The current paper focuses on a neurologically inspired computational model,
for which in this section some neurological background is discussed.
From a neurological perspective a large number of studies have been conducted on
food-related behaviour, and how food addiction is formed; for example, discussing
how highly palatable food activates reward pathways lead to obesity [11, 12, 13].
Studies suggest that dysregulation of brain reward pathways may contribute to
increased consumption of highly palatable foods, which leads to weight gain and
obesity [14]. Another reason for addictive behaviour formation is excessive eating
over long period, which involves reward circuitry, and persons become habitual in
that behaviour [13]. Different brain regions are involved in food reward circuitry
which leads addiction behaviour: a prefrontal region and the amygdale, and also the
limbic system integrating amygdala with hypothalamus and septal nuclei [15,16, 17].
Changing the eating behaviour is a quite challenging task, as persons often become
habitual in it. Decision making about food choices usually involves considering
various options and comparing them in order to make a reasonable choice out of them
that can satisfy hunger or taste. Every available food option is coupled with an
associated feeling related to prediction whether that option will provide satisfaction or
not. It depends on reward mechanisms, as a form of valuing of the options. In making
a choice for an option such valuing processes play a significant role.
A desire on the one hand triggers preparations for responses and on other hand
generates the associated feelings via recursive body and as-if body loops [18, 19, 20],
which can predict the consequences and satisfaction of particular options before
taking action. This is done by evaluating the options using loops involving
interaction between feeling and preparation. The connections from feelings to
preparations can be either assumed static or adaptive. Adaptive connection strengths
depend on earlier experiences [21, 22]. They can be learnt over time as a form of
neural plasticity by changing the connections strengths [9, 23].
Neuroscientific literature shows that goals guiding an individual’s behaviour are
linked with activations in the prefrontal cortex. Such activations can inhibit the
activation of subcortical structures (e.g., cerebellum and basal ganglia), associated
with habitual behaviour [24, 25] and have the ability to change habitual behaviour
[26, 27, 28]. Such control mechanisms can help persons to change their preferences or
choice of food by keeping goals in mind; for example, they may start to look for low
calories food instead of high calories food.
3 The Computational Model
In this section the proposed computational model is discussed, based on the literature
described in Section 2. The model was designed by considering a number of aspects
of eating behaviour, such as food desire generation and preparation for actions to
fulfil the desire. As causes of a food desire either internal (feeling less energy,
hungry), or external (stimuli) causes are considered. In relation to a generated desire,
particular actions are considered and the extent to which they will provide a feeling of
satisfaction. Moreover, goals are considered in this model. An overview of the model
is depicted in Fig. 1; the concepts used are explained in Table 1. The cognitive model
reflects the underlying neurological concepts at an abstracted level, and is based on a
network of temporal-causal relationships [29].
as-if-body loop
as-if-body loop
ssbi fsbi
sssf fssat
Figure 1. Conceptual representation of the computational model
The process of a desire generation is modelled in two ways, as shown in Fig.1. First, a
desire can be generated through the metabolic activity (energy level). This process is
modelled by the connections from metabolic state msub that leads to the bodily
unbalance (for being hungry) represented by body state bsub. A person senses the
bodily unbalance via the internal sensor state ssub that enables the person to form the
sensory body representation state srsub representing body unbalance in the brain. By
this body unbalance the desire to eat something to reduce the body unbalance
develops and the person prepares for the available food options. This is modelled by
the connection from the desire state dsub to preparation states psbi.
Not only the desire but also the associated feelings have impact on the preparations;
before performing any action a feeling state fsbi for the (partly prepared) action is
generated by a predictive as-if-body loop via the sensory representation state srsbi.
Table 1. Overview of the states of the proposed model (see also Fig. 1)
Informal name
World state w
This characterizes the current world situation which the person is
facing the stimulus, in this case w is the food stimulus
Sensor state for w
The person senses the world through the sensor state, providing
sensory input
Sensory representation
state for w
Internal representation of sensory world information on w
Metabolism for ub
Represents metabolic energy level: a low level while being inactive
and a high level while being active
Body state of ub
A bodily state that represents the body state underlying being hungry;
if a person is becoming hungry, the body unbalance ub increases, and
after eating there will be lower or no unbalance
Sensor state for ub
The person senses the bodily unbalance state ub, providing sensory
Sensory represent- ation
of ub
Internal sensory representation for sensed bodily unbalance
information on ub
desire for unbalance ub
Generating a desire based on sensory representation for a body
unbalance ub (e.g., a state of being hungry)
Preparation for an
action bi
Preparation for an eating response
Feeling associated to
body state bi
A feeling state fsbi for the eating action
Sensory representation
of bi
Internal sensory representation of body srsb for bi in the brain. In this
case here b represents the associated available food choices
Sensor state for bi
The person senses the external body states or environment through the
sensor state, providing sensory input.
Sensor state for
satisfaction sfbi
The person senses the external body states providing sensory input to
the feelings of satisfaction.
Sensory representation
of satisfaction sfbi
Internal representation of the body aspects of feelings of satisfaction
Feeling for satisfaction
Feeling of satisfaction; these are the feelings about the considered food
choice, how much satisfactory it is.
Effector state for action
In the considered scenarios the action bi is performed to eat food of
particular choice for which a person is prepared, to reduce the body
unbalance ub
Body state for bi
This represents external body states related to eating that particular
Long term goal
This represents the long term goal, to lose weight, for example
Short term goal
Short term goal refers to smaller incremental way of achieving long
term goals for example start to eat healthy avoid from fast food etc.
ωfsbi, psbi
Learnt connections
These are the connections which can be learnt by Hebbian learning.
This one models how the generated feeling affects the preparation for
response bi.
In the considered scenario, the desire state dsub has an effect on a number of
preparation states psbi for responses bi which lead to sensory body representation srsbi
and to the feeling states fsbi. Subsequently, the states fsbi have strengthening impacts
on the preparation state psbi, which in turn has an impact on feeling state, fsbi, through
srsbi which makes the process recursive: an as-if body loop (e.g., [30]).
A desire, generated by body unbalance or by environmental factors or both, needs
to be fulfilled by eating a particular selected food. A person takes action for a selected
food option to reduce or eliminate the body unbalance. In this model the effect of an
executed action bi (by esbi) is effectuated on the one hand through the connection from
effector states esbi to body states bsbi that are bodily representations of direct
valuations or satisfaction for the selected options. On the other hand the action effect
is effectuated through the connection from effector states esbi to body states bsub, this
action effect represents reduction of the bodily unbalance bsub after eating.
Another process involves the expected satisfaction feeling of goal fulfilment after
the particular action execution esbi (eating particular food). Expected satisfaction
feelings also have effect on making choices. For example, if a person takes fast food
initially, after eating he may not feel satisfied as it was not healthy, so next time (s)he
may prefer to go for healthy food, in order to feel more satisfaction.
The model as depicted in Fig.1 shows the connection from body state bsbi sensed
by the person via sensor state sssf and represented by sensory representation state for
goal fulfilment satisfaction srssf. This representation state srssf receives impact from
the preparation state psbi via an as-if-body loop as well. The sensory information srssf
then leads to a level of expected feelings of goal fulfilment satisfaction fssf. So on the
basis of this satisfaction a person may continue to choose the same option (eat the
same kind of food) again and again or he or she may change the option, e.g., from fast
food to healthy food. In this way it will have impact on preparation states psbi.
Setting goals in life either long term or short term is also a common practice . Goals
may relate to a healthy life style, behaviour change or other ambition. In this case
long term goals can be considered to weight loss and short term goals can be
considered in relation to taking a healthy diet. If a person does not have any goal
about his or her health he or she may eat anything based on various personal
characteristics/ preference of wanting and liking. On the other hand, if a person wants
to aim at being healthy then this may affect what is considered suitable to eat. In the
considered scenario as a long term goal ltg it is taken that a person wants to lose
weight and that leads to short term goal stg which is to take a healthy diet to achieve
long term goal. A short term goal has a direct influence on the preparation states psbi.
So by keeping goals in mind a person can change his eating behaviour and start giving
preference to healthy food.
Another factor that is included in the model, is how the effect of the feeling on the
preparation can be learnt over time based on past experiences. For this a Hebbian
learning mechanism has been included by which such connections may automatically
emerge or strengthen. The connections from feeling fsbi to preparation psbi with
weights ωfsbi, psbi have been made adaptive in this way. The strengths ωfsbi, psbi are
adapted by the Hebbian learning principle, that connected neurons that are frequently
activated simultaneously strengthen their connections over time [8, 9, 31].
In the example scenarios it has been shown that if a person starts changing eating
behaviour than the particular connection gets more strength over time due to learning.
Each time the person experiences a certain feeling for a prepared action, through
Hebbian learning this strengthens the association between feeling and preparation. So
the recursive process of predictive as-if body loops via feeling states fsbi to the
preparation states psbi, involves Hebbian learning in order to enable adaptivity.
The model description presented above (Fig.1 and Table1) represents a network
of cognitive and affective states in a conceptual manner. The following elements are
also considered to be given as part of such a conceptual representation:
For each connection from state X to state Y a weight X.,Y (a number between -1
and 1), for strength of impact; a negative weight is used for suppression
For each state Y a speed factor Y (a positive value) for timing of impact
For each state Y (a reference to) a combination function cY() used to aggregate
multiple impacts from different states on one state Y
For a numerical representation of the model the states Y get activation values
indicated by Y(t): real numbers between 0 and 1 over time points t, where the time
variable t ranges over the real numbers. The conceptual representation of the model
(as shown in Fig. 1 and in Table 1) can be transformed in a systematic or even
automated manner into a numerical representation as follows [29]:
At each time point t state X connected to state Y has an impact on Y defined as
impactX,Y(t) = X,Y X(t)
where X,Y is the weight of the connection from X to Y
The aggregated impact of multiple states Xi on Y at t is determined using a
combination function cY(..):
aggimpactY(t) = cY(impactX1,Y(t), …, impactXk,Y(t))
= cY(X1,YX1(t), …, Xk,YXk(t))
where Xi are the states with connections to state Y
The effect of aggimpactY(t) on Y is exerted over time gradually, depending on
speed factor Y:
Y(t + t) = Y(t) + Y [aggimpactY(t) - Y(t)] t
or dY(t)/dt = Y [aggimpactY(t) - Y(t)]
Thus the following difference and differential equation for Y are obtained:
Y(t + t) = Y(t) + Y [cY(X1,YX1(t), …, Xk,YXk(t)) - Y(t)] t
dY(t)/dt = Y [cY(X1,YX1(t), …, Xk,YXk(t)) - Y(t)]
In the model considered here, for all states for the standard combination function the
advanced logistic sum combination function alogistic,() is used [10]:
cY(V1, …Vk) = alogistic,(V1, …, Vk) = (
󰇛󰇜 -
 ) (  )
Here is a steepness parameter and a threshold parameter. The advanced logistic
sum combination function has the property that activation levels 0 are mapped to 0
and it keeps values below 1. For example, for the prepration state psbi the model is
numerically represented in difference equation form as
aggimpactpsbi(t) =
alogistic,(srsub, psbi srsub(t), fsbi, psbi fsbi(t), fssat, psbi fssat(t), stg, psbi stg,(t))
psbi(t+t) = psbi(t) + psbi [aggimpactpsbi(t) - psbi(t)] t
The numerical representation of Hebbian learning is:
ωfsbi,psbi(t+t) = ωfsbi,psbi(t) + [η fsbi(t) psbi(t) (1 - ωfsbi,psbi(t)) - ζ ωfsbi,psbi(t)] Δt
4 Simulation Results
The computational model has been used to conduct a number of simulation
experiments according to different scenarios performed using the Matlab
environment. In this section one is described in detail. The following scenario is used:
James is facing overweight leading to an obesity problem, and has difficulties in
everyday life routine regarding his diet (dietary pattern). To overcome this
problem, he set goals to lose weight by eating healthy food. Here food is
categorized in three categories, light food (very low calories), healthy food (low
calories), and fast food (high calories). So, he has three available food options to
choose from, each of which gives some extent of satisfaction as expected from that
food. As he wants to lose weight, he will want to go for healthy or light food. In
daily routine, when he feels hungry he looks around to have healthy food to eat.
The parameter values used are shown in Tables 2 and 3.
Table 2. Values of threshhold, steepness and update speed
Table 3. Values of parameters used: connection weights
In the scenario the strength of the weights fsbi,psbi of the connections from feelings
to the considered preparations change over time through the Hebbian learning
mechanism. Initial values of the activation levels for all states have been chosen 0
except the metabolic activity which depends on the scenario, and activation values for
goals either are 0 or higher, also depending on the scenario. The simulation is
executed for some scenarios for 180 time points and for other scenarios simulations
for 1500 time points; the time step t = 0.1. Learning rate η = 0.016 and extinction
rate ζ = 0.000015 have been used when Hebbian learning is used. Details of the
values for parameters used in the simulation are given in Table 2 (threshold and
steepness ) and in Table 3 (connection weights). In the scenario depicted in Figure 2,
the simulation shows five times hunger.
Figure 2. Simulation results of five times body unbalance (hunger), with Hebbian learning
The role of the learning mechanism is illustrated, so the person can change his eating
behaviour by learning through past experiences. When the person notices a body
unbalance or hunger that triggers the desire to eat then initially the person may have
equal tendency for all available options Figure 2(b). But through the associated
feelings valuation of the options takes place and the person can learn Figure 2(e): the
eating behaviour can be changed by adapting it slowly due to learning from his
experiences. In this simulation Hebbian learning is used on the weights fsbi,psbi of the
connections between associated feelings and preparations so these links are dynamic
in this scenario; the weights esbi,bsbi of the connections from execution states to body
state are 0.3, 0.9, 0.6 respectively, these values are only for this simulation, change in
these connections will make a change in the learning connections as well.
5 Conclusion
The model presented in this paper is a neurologically inspired computational model
for making food choices by involving a number of internal and external factors. The
focus of this paper is to formalise the dynamics and interaction of internal states
which are involved in decision of making food choice. This model will help to
understand the complex process from food desire generation to food choices that
evolve based on internal prediction in combination with associated feelings for
valuation of the available food options. The model also incorporates the role of goals
and their influence on decision making on food choices. The simulation results
suggest that this model is capable of learning of making food choices on the one hand
through associated feelings to fulfil the desire, and on the other hand through levels of
satisfaction in relation with various types of food and goals. The proposed
computational model for making food choices can be used to develop human-aware
intelligent systems that can help and support persons with overweight and obesity.
In future work, the model will be extended with food desire regulation strategies,
and further focus will be on the social and environmental factors; in addition it may
be further extended with more personal characteristics.
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... To change the eating behaviour or lifestyle of persons there is a need to develop intelligent systems which can help persons to adopt healthy eating behaviour or change their lifestyle. There is much work going on the computational modelling of human behaviour [4], [5] that can provide basis for the development of such human-aware systems. Giuliani et al. [6] have taken the emotion regulation process model of Gross [7], [8] thereby assuming that food craving is also an affective state so that its regulation involves the emotion regulation strategies to regulate the desire-associated emotions: ...
... Just following this causal chain three possibilities for regulation can be identified: (1) modifying beliefs that interpret the world (also called reinterpretation), (2) modifying the own sensing of the world, for example by changing the gaze direction (also called attention deployment) and (3) modifying the world itself (situation modification). This work extends the model of desire generation and making of choices for actions in [5] by introducing desire regulation mechanisms.The neuroscientific literature suggests that the food craving can be downregulated using cognitive regulation strategies [9]- [12]. The neurological underpinnings of such regulation processes have been studied through fMRI techniques. ...
... The proposed model extends the model of desire generation and making of choices [5], which is visible in the lower part of Fig. 1 ( that part is indicated with solid arrows and states without filling any colour and the extended part includes all doted arrows and states filled with green colour). It is assumed that a desire can be generated either from below the neck (a bodily unbalance such as being hungry) or above the neck (being attracted to cues in the environment; e.g., palatable food). ...
Conference Paper
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In this paper a neurologically inspired cognitive agent model for desire regulation is presented that describes the desire generation process and a number of desire regulation strategies. This work addresses antecedent-focused desire regulation strategies. These strategies include reinterpretation, attention deployment and situation modification. The model has been used to perform a number of simulation experiments concerning food desire and eating behaviour.
... In (Treur and Umair, 2012) it is discussed how some other types of decision and learning models can be evaluated according to the rationality measures used here. In (Abro and Treur, 2016) an adaptive temporal-causal network model is described for decision making on food choice in which different valuing perspectives are integrated, among which valuing perspectives related to satisfaction with respect to short term and long term goals. ...
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This paper presents a dynamic modelling approach that enables to design complex high level conceptual representations of models in the form of causal-temporal networks, which can be automatically transformed into executable numerical model representations. Dedicated software is available to support designing models in a graphical manner, and automatically transforming them into an executable format and performing simulation experiments. The temporal-causal network modelling format used makes it easy to take into account theories and findings about complex brain processes known from Cognitive, Affective and Social Neuroscience, which, for example, often involve dynamics based on interrelating cycles. This enables to address complex phenomena such as the integration of emotions within all kinds of cognitive processes, and of internal simulation and mirroring of mental processes of others. In this paper also the applicability has been discussed in general terms, showing for example that every process that can be modelled by first-order differential equations, also can be modeled by the presented temporal-causal network modeling apporoach. A variety of example models that can be found in other papers illustrate the applicability of the approach in more detail.
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Background: Up-to-date evidence about levels and trends in disease and injury incidence, prevalence, and years lived with disability (YLDs) is an essential input into global, regional, and national health policies. In the Global Burden of Disease Study 2013 (GBD 2013), we estimated these quantities for acute and chronic diseases and injuries for 188 countries between 1990 and 2013. Methods: Estimates were calculated for disease and injury incidence, prevalence, and YLDs using GBD 2010 methods with some important refinements. Results for incidence of acute disorders and prevalence of chronic disorders are new additions to the analysis. Key improvements include expansion to the cause and sequelae list, updated systematic reviews, use of detailed injury codes, improvements to the Bayesian meta-regression method (DisMod-MR), and use of severity splits for various causes. An index of data representativeness, showing data availability, was calculated for each cause and impairment during three periods globally and at the country level for 2013. In total, 35 620 distinct sources of data were used and documented to calculated estimates for 301 diseases and injuries and 2337 sequelae. The comorbidity simulation provides estimates for the number of sequelae, concurrently, by individuals by country, year, age, and sex. Disability weights were updated with the addition of new population-based survey data from four countries. Findings: Disease and injury were highly prevalent; only a small fraction of individuals had no sequelae. Comorbidity rose substantially with age and in absolute terms from 1990 to 2013. Incidence of acute sequelae were predominantly infectious diseases and short-term injuries, with over 2 billion cases of upper respiratory infections and diarrhoeal disease episodes in 2013, with the notable exception of tooth pain due to permanent caries with more than 200 million incident cases in 2013. Conversely, leading chronic sequelae were largely attributable to non-communicable diseases, with prevalence estimates for asymptomatic permanent caries and tension-type headache of 2·4 billion and 1·6 billion, respectively. The distribution of the number of sequelae in populations varied widely across regions, with an expected relation between age and disease prevalence. YLDs for both sexes increased from 537·6 million in 1990 to 764·8 million in 2013 due to population growth and ageing, whereas the age-standardised rate decreased little from 114·87 per 1000 people to 110·31 per 1000 people between 1990 and 2013. Leading causes of YLDs included low back pain and major depressive disorder among the top ten causes of YLDs in every country. YLD rates per person, by major cause groups, indicated the main drivers of increases were due to musculoskeletal, mental, and substance use disorders, neurological disorders, and chronic respiratory diseases; however HIV/AIDS was a notable driver of increasing YLDs in sub-Saharan Africa. Also, the proportion of disability-adjusted life years due to YLDs increased globally from 21·1% in 1990 to 31·2% in 2013. Interpretation: Ageing of the world's population is leading to a substantial increase in the numbers of individuals with sequelae of diseases and injuries. Rates of YLDs are declining much more slowly than mortality rates. The non-fatal dimensions of disease and injury will require more and more attention from health systems. The transition to non-fatal outcomes as the dominant source of burden of disease is occurring rapidly outside of sub-Saharan Africa. Our results can guide future health initiatives through examination of epidemiological trends and a better understanding of variation across countries.
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A psychobiological dimension of eating behaviour is proposed, which is anchored at the low end by energy intake that is relatively well matched to energy output and is reflected by a stable body mass index (BMI) in the healthy range. Further along the continuum are increasing degrees of overeating (and BMI) characterized by more severe and more compulsive ingestive behaviours. In light of the many similarities between chronic binge eating and drug abuse, several authorities have adopted the perspective that an apparent dependence on highly palatable food-accompanied by emotional and social distress-can be best conceptualized as an addiction disorder. Therefore, this review also considers the overlapping symptoms and characteristics of binge eating disorder (BED) and models of food addiction, both in preclinical animal studies and in human research. It also presents this work in the context of the modern and "toxic" food environment and therein the ubiquitous triggers for over-consumption. We complete the review by providing evidence that what we have come to call "food addiction" may simply be a more acute and pathologically dense form of BED.
In this article I discuss a hypothesis, known as the somatic marker hypothesis, which I believe is relevant to the understanding of processes of human reasoning and decision making. The ventromedial sector of the prefrontal cortices is critical to the operations postulated here, but the hypothesis does not necessarily apply to prefrontal cortex as a whole and should not be seen as an attempt to unify frontal lobe functions under a single mechanism. The key idea in the hypothesis is that 'marker' signals influence the processes of response to stimuli, at multiple levels of operation, some of which occur overtly (consciously, 'in mind') and some of which occur covertly (non-consciously, in a non-minded manner). The marker signals arise in bioregulatory processes, including those which express themselves in emotions and feelings, but are not necessarily confined to those alone. This is the reason why the markers are termed somatic: they relate to body-state structure and regulation even when they do not arise in the body proper but rather in the brain's representation of the body. Examples of the covert action of 'marker' signals are the undeliberated inhibition of a response learned previously; the introduction of a bias in the selection of an aversive or appetitive mode of behaviour, or in the otherwise deliberate evaluation of varied option-outcome scenarios. Examples of overt action include the conscious 'qualifying' of certain option-outcome scenarios as dangerous or advantageous. The hypothesis rejects attempts to limit human reasoning and decision making to mechanisms relying, in an exclusive and unrelated manner, on either conditioning alone or cognition alone.
The behaviourist views psychology as a purely directive experimental branch of natural science. Its theoretical goal is the prediction and control of behavior. So far, human psychology has been unsuccessful due to the mistaken notion that introspection is the only method available to psychology, and that it is the study of consciousness. Actually, psychology is the study of behavior and therefore need not take recourse to conscious phenomena. Hence, animal psychology is as valid a field of study as human psychology. The laws of behavior of animals must be determined and evaluated in and for themselves, regardless of their generalizability to other animals or humans. This suggested elimination of states of consciousness as the objects of investigation will remove the barrier that exists between psychology and other natural sciences, without neglecting the essential problems of introspective psychology.