Search Results

You are looking at 1 - 10 of 19 items for :

  • "reinforcement learning" x
  • Refine by Access: All Content x
Clear All
Journal of Behavioral Addictions
Authors:
Florent Wyckmans
,
Nilosmita Banerjee
,
Mélanie Saeremans
,
Ross Otto
,
Charles Kornreich
,
Laetitia Vanderijst
,
Damien Gruson
,
Vincenzo Carbone
,
Antoine Bechara
,
Tony Buchanan
, and
Xavier Noël

, Kirschbaum, & Stalder, 2013 ). We fitted choice behavior on the RL-task to a 7-parameter hybrid reinforcement learning algorithm ( Daw et al., 2011 ) with the hBayesDM package ( Ahn, Haines, & Zhang, 2017 ). Four MCMC chains of 6,000 samples

Open access

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

Open access

. , 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

Restricted access

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

Open access

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

Open access

A mesterséges intelligencia néhány biztonsági vetülete

On some security aspects of AI systems

Scientia et Securitas
Authors:
László Vidács
,
Márk Jelasity
,
László Tóth
,
Péter Hegedűs
, and
Rudolf Ferenc

. Adversarial Policies: Attacking Deep Reinforcement Learning International Conference on Learning Representations 2020 Hamm Lonnie

Open access
Journal of Behavioral Addictions
Authors:
Rudolf Stark
,
Charlotte Markert
,
Onno Kruse
,
Bertram Walter
,
Jana Strahler
, and
Sanja Klein

-Affect-Cognition-Execution (I-PACE; Brand, Young, Laier, Wölfling, & Potenza, 2016 ; Brand et al., 2019 ), escalated impulsive/compulsive/addictive behavior is explained by negative reinforcement along with positive reinforcement learning. Therefore, repeatedly using SEM for

Open access
Journal of Behavioral Addictions
Authors:
Giovanni Martinotti
,
Eleonora Chillemi
,
Matteo Lupi
,
Luisa De Risio
,
Mauro Pettorruso
, and
Massimo Di Giannantonio

Risio, et al., 2014 ). The latter, particularly the dorsolateral PFC (DLPFC), plays a critical role in the addictive cycle, comprising reinforcement learning, craving, and inhibitory control. Importantly, preclinical and neuroimaging studies have shown

Open access

. Another method called Refresh ( Narayan, Cohen & Lapata 2018 ) is based on the Rouge metric, which is used to rank sentences in the text using the reinforcement learning method. The goal of Latent ( Zhang et al. 2018 ) was to propose a latent variable

Open access
Journal of Behavioral Addictions
Authors:
Shan-Shan Ma
,
Chiang-Shan R. Li
,
Sheng Zhang
,
Patrick D. Worhunsky
,
Nan Zhou
,
Jin-Tao Zhang
,
Lu Liu
,
Yuan-Wei Yao
, and
Xiao-Yi Fang

study with fMRI and methylphenidate challenge . Synapse , 63 ( 5 ), 429 – 442 . 10.1002/syn.20621 Schonberg , T. , Daw , N. D. , Joel , D. , & O’Doherty , J. P. ( 2007 ). Reinforcement learning signals in the human striatum distinguish

Open access