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The Choice Between Bad and Worse: A Cognitive Agent Model for Desire Regulation Under Stress


Abstract and Figures

Desire for food intake often arises to get rid of negative emotions. On the other hand, negative emotion like anxiety, also brings along psychological health issues. In such a situation it’s quite a feasible option to get rid of the worse before the bad. In this paper a cognitive agent model for food desire regulation is presented wherein Hebbian learning helps in breaking the bond between anxiety or stress and desire for food intake as a result. Simulation results of the model illustrate food desire and its regulation.
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It is aproven fact in social sciences that desires for food intake can occur as a result of
negative emotions. On the other hand, a negative emotion like anxiety also brings
along psychological health issues. In such a situation it’s quite a feasible option to get
rid of the worse before the bad.In this paper, a cognitive agent model for food desire
regulation is presented wherein Hebbian learning helps in breaking the bond between
anxiety or stress and desire for food intake as a result. Simulation results of the model
illustrate the food desire and its regulation.
Scenario for the simulation
Anna wants to lose her weight to look attractive, so she undergoes a dietary plan.
Every time her coworker brings some pastries, her desire for food arises but she
efficiently controls her food desires by reappraising her belief about food and putting
her dietary goals in front of her. However, it becomes difficult for her when she has a
particularly anxious week. She tries to suppress her desire for food but it proves
maladaptive and she ends up in eating. After eating she feels stressed because she was
on diet, she neither enjoyed the food nor complied with her dietary plans.
Social AI Group, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
Nimat Ullah and Jan Treur
The Choice Between Bad and Worse:
A Cognitive Agent Model for Desire Regulation under Stress
Highlighting the role of anxiety in emotional eating and hence obesity.
Breaking cycle of anxiety and binging by
Elimination of anxiety by moderate level of stress.
Computational Model
t )= Y(t)
(t), ...,
Temporal-Causal Model for Desire Regulation
Simulation results
Expressive suppression without Hebbian learning
Expressive suppression with Hebbian learning
Hebbian learning while using expressive suppression
Reappraisal to handle desire for food intake

Supplementary resource (1)

... This interplay between emotions and desires [12] can have very negative psychological as well as physical health [13,14] consequences as both can prove triggers for one another [10,12,15] and form a continuous cycle. To avoid such a cycle [12,16], a person must have the ability to regulate their emotions and desires and do not let emotions or desires influence each other. ...
... The model introduced in this section is developed using the concepts discussed above using the network-oriented temporal-causal modelling technique discussed in the preceding section. Motivation for this model comes from [16], in which a person has to choose a bad situation in order to get rid of a worse situation. In this model, the BCTs listed in Table 1 have been used as interventions to help the person change their behaviour and hence adopt a healthy lifestyle. ...
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Behaviour change techniques are considered as effective means for changing behaviour, but with the increase of its use we also see interest in their exact working principles. This is required to make informed choices about when to apply which technique. Computational models that describe human behavior can be helpful for this. In this paper a few behaviour change techniques have been connected with a computational model of emotion and desire regulation. Simulations have been done to illustrate the effect of the techniques. The results demonstrate the working mechanism as well as the feasibility of the techniques used in the model.
... Moreover, to combine these concepts into a single model, this study considers an adaptive network modeling approach [12] because of its efficacy and suitability for the adaptive and cyclic processes, as demonstrated in [13,14]. This modeling approach come under the umbrella of causal modeling which has a long history in Artificial Intelligence, e.g., [15,16]. ...
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... Moreover, reward-driven and prediction-driven synaptic plasticity and hence learning has been explained in . In terms of computational modeling, various examples of adaptive computational models can also be found, for instance in (Ullah & Treur, 2019) reward based learning has been demonstrated based on a Hebbian learning process. Similarly, (Zegerius & Treur, 2020) models the working of Eye Movement Desensitization and Reprocessing (EMDR) therapy for persons affected by a Post-Traumatic Stress Disorder (PTSD) by a therapy-induced Hebbian learning process. ...
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... The multi-level adaptive network model in this paper has been developed and simulated using adaptive casual network-oriented modeling approach and its supportive environment from (Treur 2019(Treur , 2020a(Treur , 2020b. This approach has already proven well-applicable for various adaptive as well as non-adaptive network models, for instance (Ullah et al. 2018(Ullah et al. , 2020Gao et al. 2019;Ullah and Treur 2019). Moreover, this paper extends a conference paper in COMPLEX NETWORKS 2019 (Ullah and Treur 2019a). ...
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... Moreover, this study considers an adaptive causal network modeling approach (Treur 2020) to model the above mentioned phenomena because stressful emotions and their effects form an adaptive and cyclic process which this approach particularly handles quite effectively as demonstrated, for example, in (Ullah and Treur 2020;Ullah and Treur 2019). This modeling approach can be considered as a branch in the causal modeling area which has a long tradition in AI; e.g., see (Kuipers and Kassirer, 1983;Kuipers, 1984;Pearl, 2000). ...
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