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et al., 2019 ), fueling the persistence of addictive behaviors like gambling despite the possibility of recurring negative consequences (e.g., financial losses; Everitt & Robbins, 2005 ). According to reinforcement learning (RL) theory, such habitual
addictive behaviors by altering the lower-level reinforcement learning mechanisms responsible for instilling these behaviors in the first place ( Brewer, 2019 ). Indeed, from the perspective of reinforcement learning (RL) theory ( Skinner, 1963
, Braverman, & Comings, 1996 ; Everitt & Robbins, 2005 , 2016 ; Koob & Volkow, 2010 ; Robinson & Berridge, 1993 ) in a cyclic dynamic. It emphasizes the role of reinforcement-learning and the formation of use expectancies in the development and maintenance
. , Coles , M. G. ( 2002 ): The neural basis of human error processing: Reinforcement learning, dopamine, and the error-related negativity . Psychol. Rev ., 109 , 679 – 709 . 16. Holroyd
Abstract
Background and aims
Cybersex addiction is discussed controversially, while empirical evidence is widely missing. With respect to its mechanisms of development and maintenance Brand et al. (2011) assume that reinforcement due to cybersex should lead to the development of cue-reactivity and craving explaining recurrent cybersex use in the face of growing but neglected negative consequences. To support this hypothesis, two experimental studies were conducted.
Methods
In a cue-reactivity paradigm 100 pornographic cues were presented to participants and indicators of sexual arousal and craving were assessed. The first study aimed at identifying predictors of cybersex addiction in a freely recruited sample of 171 heterosexual males. The aim of the second study was to verify the findings of the first study by comparing healthy (n = 25) and problematic (n = 25) cybersex users.
Results
The results show that indicators of sexual arousal and craving to Internet pornographic cues predicted tendencies towards cybersex addiction in the first study. Moreover, it was shown that problematic cybersex users report greater sexual arousal and craving reactions resulting from pornographic cue presentation. In both studies, the number and subjective quality of real-life sexual contacts were not associated to cybersex addiction.
Discussion
The results support the gratification hypothesis, which assumes reinforcement, learning mechanisms, and craving to be relevant processes in the development and maintenance of cybersex addiction. Poor or unsatisfying sexual real-life contacts cannot sufficiently explain cybersex addiction.
Conclusions
Positive reinforcement in terms of gratification plays a major role in cybersex addiction
required to feed the algorithm with a large number of images of tumorous organs and a large number of healthy organs. Based on the learned pattern, the algorithm will be able to determine if a tumor is present in a new image. Reinforcement learning is
rarely used in literature; however, significant tasks have been done in regression-based solutions, Artificial Neural Networks (ANNs), and reinforcement learning (RL). 1.1 Virtualization The technique of virtualization has the aim of efficient management
A mesterséges intelligencia néhány biztonsági vetülete
On some security aspects of AI systems
. Adversarial Policies: Attacking Deep Reinforcement Learning International Conference on Learning Representations 2020 Hamm Lonnie
reduction, and clustering, enabling the identification of inherent patterns within the data [ 15 ]. Reinforcement learning: Reinforcement learning involves a computer or agent operating within a dynamic environment [ 16 ]. The algorithm learns to perform a
), the PVL model with decay reinforcement learning rule (PVL-DecayRI) ( Ahn, Krawitz, Kim, Busmeyer, & Brown, 2011 ). See details in SM. The fMRI data acquisition and pre-processing All MRI data were acquired using 3-T Siemens Magnetom Trio scanners in
