Identifying Perceptual Errors and Cognitive Biases in Observing and Describing Intelligence and Providing Mitigating Solutions

Message:
Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:
This study aims to identify perceptual errors and cognitive biases in observing and describing intelligence. Accordingly, the question was raised: what are the common perceptual errors and cognitive biases in observing and describing intelligence, and how can they be mitigated? The statistical population includes all experts and practitioners in the field of observation and description working in field operations in one of the intelligence-security organizations, who in addition to practical experience in observation and description, have at least a bachelor's degree with at least 15 years of specialized work in the field. The survey included 100 individuals. To test the research questions and measure perceptual errors and cognitive biases, a researcher-made questionnaire with 48 questions on a Likert scale was used. The validity of the questionnaire was assessed through content validity, and its reliability was measured using Cronbach's alpha, showing that the tool has acceptable validity and reliability for measuring the intended variables. The analysis results showed that for 11 questions in the questionnaire about perceptual errors in observing and describing intelligence, all respondents agreed significantly with the types of errors. This result was repeated for the 37 questions related to cognitive biases. The study findings indicate that although respondents confirmed the types of errors and cognitive biases, there was no significant difference in their responses to the statements. The third finding is that respondents expressed that the presence of perceptual errors and most biases in observing and describing intelligence is inevitable and can only be minimized through training, practice, the use of modern technologies, and alternative methodologies.
Language:
Persian
Published:
Journal of Security Research, Volume:23 Issue: 85, 2025
Pages:
73 to 92
https://www.magiran.com/p2834802  
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