Brain pathways for cognitive-emotional decision making in the human animal

Levine, Daniel S.

doi:10.1016/j.neunet.2009.03.003

Cited by 43 publications

(25 citation statements)

References 47 publications

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“…One area for further work is to examine the impact of strong attractors in the Boltzmann machine developed in [29] to model neuroses. A more challenging task is to integrate these conceptual tools with the current work on modelling cognitive-emotional decision making using attractor neural networks as in [30].…”

Section: Discussionmentioning

confidence: 99%

Strong attractors of Hopfield neural networks to model attachment types and behavioural patterns

Edalat

Mancinelli

2013

The 2013 International Joint Conference on Neural Networks (IJCNN)

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Abstract-We study the notion of a strong attractor of a Hopfield neural model as a pattern that has been stored multiple times in the network, and examine its properties using basic mathematical techniques as well as a variety of simulations. It is proposed that strong attractors can be used to model attachment types in developmental psychology as well as behavioural patterns in psychology and psychotherapy. We study the stability and basins of attraction of strong attractors in the presence of other simple attractors and show that they are indeed more stable with a larger basin of attraction compared with simple attractors. We also show that the perturbation of a strong attractor by random noise results in a cluster of attractors near the original strong attractor measured by the Hamming distance. We investigate the stability and basins of attraction of such clusters as the noise increases and establish that the unfolding of the strong attractor, leading to its breakup, goes through three different stages. Finally the relation between strong attractors of different multiplicity and their influence on each other are studied and we show how the impact of a strong attractor can be replaced with that of a new strong attractor. This retraining of the network is proposed as a model of how attachment types and behavioural patterns can undergo change.

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Section: Discussionmentioning

confidence: 99%

Strong attractors of Hopfield neural networks to model attachment types and behavioural patterns

Edalat

Mancinelli

2013

The 2013 International Joint Conference on Neural Networks (IJCNN)

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“…The framework in [34] is based on Levine's pathways for emotional-cognitive decision making [54], which contains two networks: An energy based competitive needs network that is inspired by Maslow's hierarchy of needs and includes physiological as well as higher needs and a network of decision rules with four connected areas: the amygdala, the OFC, ACC and DLPFC, which account for various decision rules on specific tasks. These four regions comprise a three-layer network, in which the vigilance threshold of the individual determines the status of activation of each layer.…”

Section: A Basic Neural Model Of Self-attachmentmentioning

confidence: 99%

Introduction to self-attachment and its neural basis

Edalat

2015

2015 International Joint Conference on Neural Networks (IJCNN)

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Abstract-We introduce the notion of self-attachment which, based on an interdisciplinary set of concepts, proposes a new psychotherapeutic technique. The underlying ideas include findings and paradigms in developmental psychology and neuroscience, neuroplasticity and long term term potentiation, fMRI studies on human bond making, ethology and psychology of religion and experiments in energy based artificial neural networks. The proposed self-attachment therapeutic technique is distinguished by its intervention to create an internal and passionate affectional bond within the individual between the "adult self", representing the logical and cognitive faculty, and the "inner child", representing the unregulated and undeveloped emotional circuits. The aim is to create more optimal circuits for emotional regulation. The proposed self-attachment protocols internally emulate within the individual the interactions of a good enough primary care-giver and child in order to moderate the child's arousal level, minimise its negative affects and maximize its positive affects. These interactions are assumed, in developmental neuroscience and in developmental psychology, to be the basis of secure attachment of children with their parents, which leads to an optimal regulation of neurotransmitters, hormones, and the emotional dynamics of the individual. We report on several case studies of this technique in recent years. Finally, we propose a simple mathematical model to capture the impact of self-attachment protocols using the notion of strong patterns in energy based neural networks and employ a recently developed mathematical model to examine the impact of self-attachment using emotional and cognitive neural pathways for decision making.

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“…On the other hand, deliberative rules allow decision makers to evaluate complex situations in order to extract relevant information. An example of this category of models is DECIDER (Levine, 2009 state of the needs module determines which rule must be applied through the orienting module. Depending on the pattern identified by the network contained in each rule module, the model applies the corresponding rule.…”

Section: Rule-based Modelsmentioning

confidence: 99%