Implicit Measurement Approaches in the Study of Addictive Behaviors Fanni Putri Diantina, Endah Nawangsih, Rizka Hadian Permana, Yasya Citra Shafana, Zalfa Ginnadhia Fauziah
Faculty Of Psychology Universitas Islam Bandung
Abstract
Conventional assessments of addictive behaviors have predominantly relied on self-report instruments, which, despite their practicality and ease of administration, are vulnerable to several methodological biases. These include social desirability effects, limited self-awareness of one^s own behaviors, and intentional misreporting-factors that can significantly distort the accuracy of findings. In light of these limitations, implicit measures such as the Implicit Association Test (IAT), Stroop Task, and Go/NoGo Task have garnered increasing scholarly attention for their ability to tap into automatic, unconscious cognitive processes that shape addictive behaviors. These tasks bypass conscious control, thereby offering unique insights into the attentional biases, inhibitory control deficits, and associative learning mechanisms implicated in addictions, including Internet Gaming Disorder (IGD). Empirical evidence suggests that implicit measures can predict addiction-related outcome such as craving intensity, relapse likelihood, and treatment responsiveness- often independently of, or in conjunction with, self-reported data. This independence is particularly valuable, as it reveals aspects of addictive behavior that individuals may be unwilling or unable to disclose. However, concerns remain about the psychometric robustness of these tools, including issues of test-retest reliability, ecological validity, and their translation into practical clinical protocols. This review emphasizes the value of integrating implicit assessments with explicit self-reports to produce a more nuanced, multidimensional diagnostic framework. Such an approach could enhance both theoretical understanding and therapeutic targeting of addictive behaviors. Future research should prioritize refining these instruments, validating them across diverse populations, and developing protocols for their seamless incorporation into existing diagnostic and intervention models.
Keywords: implicit measure, cognitive process, internet gaming disorder
