scholarly journals Assessing attentional bias and inhibitory control in cannabis use disorder using an eye-tracking paradigm with personalized stimuli.

2019 ◽  
Vol 27 (6) ◽  
pp. 578-587 ◽  
Author(s):  
Jin H. Yoon ◽  
Guadalupe G. San Miguel ◽  
Jessica N. Vincent ◽  
Robert Suchting ◽  
Ilana Haliwa ◽  
...  
2014 ◽  
Vol 28 (7) ◽  
pp. 633-642 ◽  
Author(s):  
Marise WJ Machielsen ◽  
Dick J Veltman ◽  
Wim van den Brink ◽  
Lieuwe de Haan

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252494
Author(s):  
Janika Heitmann ◽  
Madelon E. van Hemel-Ruiter ◽  
Mark Huisman ◽  
Brian D. Ostafin ◽  
Reinout W. Wiers ◽  
...  

Background Attentional bias for substance-relevant cues has been found to contribute to the persistence of addiction. Attentional bias modification (ABM) interventions might, therefore, increase positive treatment outcome and reduce relapse rates. The current study investigated the effectiveness of a newly developed home-delivered, multi-session, internet-based ABM intervention, the Bouncing Image Training Task (BITT), as an add-on to treatment as usual (TAU). Methods Participants (N = 169), diagnosed with alcohol or cannabis use disorder, were randomly assigned to one of two conditions: the experimental ABM group (50%; TAU+ABM); or the control group (50%; split in two subgroups the TAU+placebo group and TAU-only group, 25% each). Participants completed baseline, post-test, and 6 and 12 months follow-up measures of substance use and craving allowing to assess long-term treatment success and relapse rates. In addition, attentional bias (both engagement and disengagement), as well as secondary physical and psychological complaints (depression, anxiety, and stress) were assessed. Results No significant differences were found between conditions with regard to substance use, craving, relapse rates, attentional bias, or physical and psychological complaints. Conclusions The findings may reflect unsuccessful modification of attentional bias, the BITT not targeting the relevant process (engagement vs. disengagement bias), or may relate to the diverse treatment goals of the current sample (i.e., moderation or abstinence). The current findings provide no support for the efficacy of this ABM approach as an add-on to TAU in alcohol or cannabis use disorder. Future studies need to delineate the role of engagement and disengagement bias in the persistence of addiction, and the role of treatment goal in the effectiveness of ABM interventions.


2021 ◽  
Vol 12 ◽  
Author(s):  
Janika Heitmann ◽  
Peter J. de Jong

Current cognitive models of addiction imply that speeded detection and increased distraction from substance cues might both independently contribute to the persistence of addictive behavior. Speeded detection might lower the threshold for experiencing craving, whereas increased distraction might further increase the probability of entering a bias-craving-bias cycle, thereby lowering the threshold for repeated substance use. This study was designed to examine whether indeed both attentional processes are involved in substance use disorders. Both attentional processes were indexed by an Odd-One-Out visual search task in individuals diagnosed with alcohol use disorder (AUD; n = 63) and cannabis use disorder (CUD; n = 28). To test whether the detection and/or the distraction component are characteristic for AUD and CUD, their indices were compared with matched individuals without these diagnoses (respectively, n = 63 and n = 28). Individuals with CUD showed speeded detection of cannabis cues; the difference in detection between AUD and the comparison group remained inconclusive. Neither the AUD nor the CUD group showed more distraction than the comparison groups. The sample size of the CUD group was relatively small. In addition, participants made relatively many errors in the attentional bias (AB) task, which might have lowered its sensitivity to detect ABs. The current study provided no support for the proposed role of increased distraction in CUD and AUD. The findings did, however, provide support for the view that speeded detection might be involved in CUD. Although a similar trend was evident for AUD, the evidence was weak and remained therefore inconclusive.


2015 ◽  
Vol 23 (9) ◽  
pp. 1508
Author(s):  
Qiandong WANG ◽  
Qinggong LI ◽  
Kaikai CHEN ◽  
Genyue FU

2017 ◽  
Vol 22 (42) ◽  
pp. 6392-6396 ◽  
Author(s):  
Amine Benyamina ◽  
Laurent Karila ◽  
Geneviève Lafaye ◽  
Lisa Blecha

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