The effectiveness of modification of cognitive bias towards smoking signs, on cognitive bias and addiction potential of smokers

Document Type : Research Paper

Authors

• Asghar Sohrabi shekafti ¹
• Ali pakizeh ²
• mojtaba jangkhiz ³

¹ Department of Psychology, Persian Gulf University, Bushehr, Bushehr, Iran

² Associate Professor Persian Gulf University, Bushehr - - Department of Psychology

³ Senior student of Clinical Psychology, Islamic Azad University, Bushehr Branch , Iran

10.30495/jpmm.2024.32311.3834

Abstract

Objectives:The aim of this study was to investigate the modification of cognitive bias towards the symptoms of smoking on cognitive bias and susceptibility to addiction in smoking employees.

Materials and Methods: The experimental research was conducted using a pre-test and post-test design with a control group. The statistical population of the research included the smoking employees of Bushehr city in 2022. The research sample included 40 people who were randomly divided in tow groups of 20 people, experimental and control. The members of the experimental group participated in an 8-week software to cognitive bias modification towards smoking symptoms. The data was collected by the addiction susceptibility questionnaire and the dot exploration task software in two stages before and after the intervention, and covariance analysis was used to analyze the data.

Results: The results of the research showed that the intervention aimed at modifying the cognitive bias towards the symptoms of smoking lead to a significant reduction in the cognitive bias (f=8/93 , p<0.05)as well as the susceptibility to addiction of the experimental group (f=39/38 ,p<0.01)

Discussion and Conclusion: Considering the effectiveness of interventions aimed at modifying cognitive bias on reducing cognitive bias and reducing the tendency to use drugs, the implementation of such interventions in populations susceptible to addiction will have an effective and low-cost role in preventing addiction.

Main Subjects

• Psychological Models and Methods

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