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

  • October 2019
DOI:10.1007/978-3-030-33792-6_34
  • Conference: Proc. of the 22nd International Conference on Principles and Practice of Multi-Agent Systems, PRIMA’19. Lecture Notes in Artificial Intelligence, Springer Publishers.
Authors:
Nimat Ullah at Vrije Universiteit Amsterdam
Nimat Ullah
Jan Treur at Vrije Universiteit Amsterdam
Jan Treur
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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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RESEARCH POSTER PRESENTATION DESIGN © 2019
www.PosterPresentations.com
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.
Abstract
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.
Objective
Computational Model
Y(t
+∆
t )= Y(t)
+ƞ
Y
[c
Y
(ω
X
1,
YX
1
(t), ...,
ω
Xk
,
YXk(t)
)-
Y(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)

The choice between bad and worse.docx
Data
August 2022
Nimat Ullah · Jan Treur
... 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. ...
Food Desires, Negative Emotions and Behaviour Change Techniques: A Computational Analysis
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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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Apart from various other neural and hormonal changes caused by stress, frequent and long-term activation of the hypothalamus–pituitary–adrenal (HPA) axis in response to stress leads in an adaptive manner to the inadequacy of the stress response system. This leads to a cognitive dysfunction where the subject is no more able to downregulate his or her stress due to the atrophy in the hippocampus and hypertrophy in the amygdala. These atrophies can be dealt with by antidepressant treatment or psychological treatments like cognitive and behavioural therapies. In this paper, an adaptive neuroscience-based computational network model is introduced which demonstrates such a cognitive dysfunction due to a long-term stressor and regaining of the cognitive abilities through a cognitive behavioural therapy: Mindfulness-Based Cognitive Therapy (MBCT). Simulation results are reported for the model which demonstrates the adaptivity as well as the dynamic interaction of the involved brain areas in the phenomenon.
... 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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To effectively regulate their emotions, people have to continually adjust their emotion regulation strategies to changes in internal and external demands. Flexibility and adaptivity are thus vital to emotion regulation. Flexibility refers to the context-sensitive deployment of emotion regulation strategies while regulating one's own emotions. By contrast, adaptivity refers to the learning taking place while regulating one's own emotions over time, and the control of this learning. Flexibility is increased by having a larger repertoire of strategies as this increases the odds that an appropriate strategy is available. On the other hand, having more emotion regulation strategies to choose from creates the need for a decision. Because this decision-making process occurs in real-time, it requires emotional stability and cognitive analysis. Over time, different experiences in choosing emotion regulation strategies give rise to learning which is one form of adaptivity. Flexibility in emotion regulation is provoked by the fluctuating contexts, whereas adaptations are induced by the frequency and intensity of emotion-regulatory activities. These adaptations are grounded in changes at a cellular and molecular level. The latter adaptations are often referred to by the term plasticity or first-order adaptation. Often some form of control is applied to such adaptation processes, determining when and under which circumstances the adaptations should take place; this is often referred to by the term meta-plasticity or second-order adaptation. The above concepts are illustrated by simulated example scenarios based on different computational network models. In the first simulated scenario, a varying context shows the flexibility in the choice of emotion regulation strategies. In the second and third scenarios, plasticity and metaplasticity are illustrated based on first- and second-order adaptive network models.
... 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). ...
The Older the Better: A Fourth-Order Adaptive Network Model for Reward-Driven Choices of Emotion Regulation Strategies Over Time
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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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