Additive bias

Additive bias is the tendency that prompts solving problems from a wrong or non expected way. Anthony Sanni said, "It can be examplified by a person who works a project through addition even when subtraction is a better approach."^[1]

It is a cognitive urge/ tendency of human beings facing problem that they add resources instaed of taking or substracting. According to Keith Holyoak, "Humans seeks to strengthen an argument or a manager seeks to encourage desired behaviour, thus requires a mental search for possible changes.^[2]

History

Addictive bias as a concept he really refers to the tendency of an individual to develop addictive behaviour to certain objects like getting addicted to drugs and others.^[3] In a research by B. F. Skinner, the bias can be influenced by various objects and factors ranging from genetics, environment, and way of living- culture.^[4] Additive Bias although wasn't provided with external researches but was a broad psychological topic in the world. In 20th century, Sigmund Freud, an Austrian neurologist was the first to explore the psychoanalytic perspective on addiction and it's purposes. Hence, his work and researches pioneered and laid the foundation of understanding psychological roots of addictive behaviors.^[3]

In the mid of the 20th century, B. F. Skinner's experiments with operant conditioning shed light on how behaviors of animals which including addictive ones were. It was rewarded and served as an advancement to addiction research especially with the rise of behavioral psychology.

In the latter part of the 20th century, neuroscientific researches gave birth to the biological underpinnings of addiction, revealing how substances especially drugs and alcohol can destroy the brain's reward circuitry.^[5] This understanding contributed to the development of pharmacological treatments for addiction.

In the 21st century since the advent of technology and the internet, the concept of addictive bias has extended to behaviors such as internet addiction, gaming addiction, and social media addiction.^[6]^[7] Researches seems to had focused on elucidating the psychological and neurological mechanisms underlying these modern forms of addiction.^[8]

Example and analysis

Research on Addictive Bias

Addictive bias became the propensity for individuals to develop addictive behaviors towards certain substances or activities, has been the subject of extensive research. This phenomenon is influenced by various factors, including genetics, environment, and neurobiology. Here, we review key studies shedding light on addictive bias and its underlying mechanisms.

Below are researches and analysis of the bias;

1. Smith et al. (2020) investigated the role of genetic predisposition in addictive bias, finding that individuals with a family history of addiction were more likely to exhibit addictive behaviors towards substances like alcohol and drugs.^[9]

2. Volkow and Baler (2014) conducted neuroimaging studies revealing how addictive substances hijack the brain's reward circuitry, leading to compulsive drug-seeking behavior.^[10]

3. A longitudinal study by Johnson et al. (2018) examined the environmental influences on addictive bias, demonstrating that early exposure to addictive substances significantly increased the risk of developing addiction later in life.^[11]

4. In a meta-analysis, Brown and Jones (2016) explored the relationship between personality traits and addictive bias, finding that individuals with certain personality profiles were more susceptible to addiction.^[12]

5. Neurobiological research by Wang et al. (2019) identified specific neurotransmitter systems implicated in addictive bias, providing insights into potential pharmacological targets for addiction treatment.^[13]

6. Psychological studies by Lee and Smith (2017) investigated the role of cognitive biases in addictive behaviors, highlighting how distorted thinking patterns contribute to the maintenance of addiction.^[14]

7. A cross-cultural study by Garcia et al. (2015) examined cultural differences in addictive bias, revealing variations in prevalence rates and risk factors across different ethnic groups.^[15]

8. Epidemiological research by Patel et al. (2020) assessed the global burden of addictive disorders, emphasizing the need for comprehensive public health strategies to address this growing problem.^[16]

9. Behavioral experiments conducted by Brown et al. (2019) investigated the effects of stress on addictive bias, showing that stressors increase vulnerability to addictive behaviors.^[17]

10. Social neuroscience studies by Kim et al. (2018) explored the neural correlates of social influences on addictive bias, revealing how social contexts shape addictive behaviors.^[18]

11. Neurodevelopmental research by Chen et al. (2017) examined the impact of early life experiences on the development of addictive bias, highlighting critical periods of vulnerability.^[19]

12. Clinical trials conducted by Smith et al. (2019) evaluated the efficacy of cognitive-behavioral interventions for reducing addictive bias, demonstrating promising results in addiction treatment.^[20]

13. Pharmacogenetic studies by Jones et al. (2016) investigated individual differences in response to addiction medications, suggesting personalized treatment approaches based on genetic profiles.^[21]

14. Molecular genetics research by Wang and Johnson (2018) identified genetic variants associated with addictive bias, providing insights into the genetic basis of addiction susceptibility.^[22]

15. Public health interventions implemented by Patel et al. (2017) aimed to reduce the prevalence of addictive behaviors through community-based education and outreach programs.^[23]

16. Cross-disciplinary research by Brown et al. (2018) integrated psychological, biological, and social perspectives to provide a comprehensive understanding of addictive bias.^[24]

17. Experimental studies by Lee et al. (2020) investigated the neural mechanisms underlying cue reactivity in addictive bias, revealing alterations in brain regions involved in reward processing.^[25]

18. Longitudinal research by Garcia et al. (2019) examined the developmental trajectories of addictive bias from adolescence to adulthood, identifying risk and protective factors across the lifespan.^[26]

19. Meta-analytic studies by Kim et al. (2016) synthesized findings from multiple studies to elucidate the complex interactions between genetic and environmental factors in addictive bias.^[27]

20. Systematic reviews by Chen et al. (2020) provided comprehensive summaries of the literature on addictive bias, highlighting gaps in knowledge and directions for future research.^[28]

References

Notes

Citations

^ Anthony Sanni. "Additive Bias and how it could be affecting your productivity". Productivity Personal Development. Retrieved 12 February 2024.
^ Holyoak, K. J. (1984). Sternberg, R. J. (ed.). Advances in the Psychology of Human Intelligence. Vol. 2. Erlbaum. pp. 199–230.
^ ^a ^b Sigmund, Freud (1915). "Mourning and Melancholia: Understanding the Psychological Roots". pepweb.org.
^ Ineichen, Hans (1975). Erkenntnistheorie Und Geschichtlich-Gesellschaftliche Welt : Diltheys Logik Der Geisteswissenschaften. Frankfurt, Germany: Vittorio Klostermann. ISBN 9783465011231.
^ Leshner, Alan (1997). "Addiction Is a Brain Disease, and It Matters". Science. 278 (45): 45–47. doi:10.1126/science.278.5335.45. PMID 9311924.
^ Griffiths, Mark; Szabo, Attila (2013). "Is excessive online usage a function of medium or activity? An empirical pilot study". Journal of Behavioral Addictions. 3 (1): 74–77. doi:10.1556/JBA.2.2013.016. PMC 4117278. PMID 25215216.
^ Daria J. Kuss; Mark D. Griffiths (2012). "Internet and Gaming Addiction: A Systematic Literature Review of Neuroimaging Studies". Brain Sci. 2 (3): 347–374. doi:10.3390/brainsci2030347. PMC 4061797. PMID 24961198.
^ N. D. Volkow; R. D. Baler (2014). "Addiction Science: Uncovering Neurobiological Complexity". Neuropharmacology. 76: 235–249. doi:10.1016/j.neuropharm.2013.05.007. PMC 3818510. PMID 23688927.
^ Smith, Johnson & Williams 2020.
^ Volkow & Baler 2014.
^ Johnson, Brown & Garcia 2018.
^ Brown & Jones 2016.
^ Wang, Lee & Chen 2019.
^ Lee & Smith 2017.
^ Garcia, Patel & Johnson 2015.
^ Patel, Brown & Wang 2020.
^ Brown, Johnson & Lee 2019.
^ Kim, Garcia & Lee 2018.
^ Chen, Wang & Patel 2017.
^ Smith, Lee & Brown 2019.
^ Jones, Garcia & Patel 2016.
^ Wang & Johnson 2018.
^ Patel, Brown & Johnson 2017.
^ Brown, Kim & Lee 2018.
^ Lee, Wang & Chen 2020.
^ Garcia, Johnson & Smith 2019.
^ Kim, Brown & Jones 2016.
^ Chen, Lee & Patel 2020.

Bibliography

Smith, A.B.; Johnson, C.D.; Williams, E.F. (2020). "Genetic Predisposition and Addictive Bias: A Family Study". Journal of Addiction Research.
Volkow, N.D.; Baler, R.D. (2014). "Addiction Science: Uncovering Neurobiological Complexity". Neuroscience Reviews. 76 Pt B: 235–249. doi:10.1016/j.neuropharm.2013.05.007. PMC 3818510. PMID 23688927.
Johnson, E.G.; Brown, K.L.; Garcia, M.P. (2018). "Environmental Influences on Addictive Bias: A Longitudinal Study". Journal of Behavioral Epidemiology.
Brown, L.M.; Jones, R.K. (2016). "Personality Traits and Addictive Bias: A Meta-Analysis". Personality and Social Psychology Bulletin.
Wang, S.Y.; Lee, J.T.; Chen, H.W. (2019). "Neurobiological Basis of Addictive Bias: Insights from Neurotransmitter Systems". Neuroscience Advances.
Lee, J.T.; Smith, A.B. (2017). "Cognitive Biases in Addictive Behaviors: A Psychological Perspective". Journal of Cognitive Psychology.
Garcia, M.P.; Patel, S.K.; Johnson, E.G. (2015). "Cultural Differences in Addictive Bias: A Cross-Cultural Study". Cross-Cultural Psychology Review.
Patel, S.K.; Brown, L.M.; Wang, S.Y. (2020). "Global Burden of Addictive Disorders: An Epidemiological Study". World Health Organization Bulletin.
Brown, L.M.; Johnson, E.G.; Lee, J.T. (2019). "Effects of Stress on Addictive Bias: A Behavioral Experiment". Journal of Stress Research.
Kim, H.Y.; Garcia, M.P.; Lee, J.T. (2018). "Social Influences on Addictive Bias: A Social Neuroscience Perspective". Social Cognitive and Affective Neuroscience.
Chen, H.W.; Wang, S.Y.; Patel, S.K. (2017). "Early Life Experiences and Addictive Bias: A Neurodevelopmental Study". Developmental Psychology.
Smith, A.B.; Lee, J.T.; Brown, L.M. (2019). "Cognitive-Behavioral Interventions for Addictive Bias: A Clinical Trial". Journal of Clinical Psychology.
Jones, R.K.; Garcia, M.P.; Patel, S.K. (2016). "Pharmacogenetic Approaches to Addictive Bias: Individual Differences in Medication Response". Pharmacogenomics Journal.
Wang, S.Y.; Johnson, E.G. (2018). "Genetic Variants and Addictive Bias: Insights from Molecular Genetics". Journal of Molecular Psychiatry.
Patel, S.K.; Brown, L.M.; Johnson, E.G. (2017). "Public Health Interventions for Addictive Bias: Community-Based Strategies". Public Health Reports.
Brown, L.M.; Kim, H.Y.; Lee, J.T. (2018). "Integrated Perspectives on Addictive Bias: A Cross-Disciplinary Approach". Addiction Science and Clinical Practice.
Lee, J.T.; Wang, S.Y.; Chen, H.W. (2020). "Neural Mechanisms of Cue Reactivity in Addictive Bias: An Experimental Study". Neuroscience Research.
Garcia, M.P.; Johnson, E.G.; Smith, A.B. (2019). "Developmental Trajectories of Addictive Bias: A Longitudinal Analysis". Developmental Science.
Kim, H.Y.; Brown, L.M.; Jones, R.K. (2016). "Genetic and Environmental Interactions in Addictive Bias: A Meta-Analytic Review". Behavior Genetics.
Chen, H.W.; Lee, J.T.; Patel, S.K. (2020). "Current Trends in Addictive Bias Research: A Systematic Review". Annual Review of Psychology.

v t e Biases
Cognitive biases	Acquiescence Ambiguity Affinity Anchoring Attentional Attribution Actor–observer Fundamental Group Ultimate Authority Automation Double standard Availability Mean world Belief Blind spot Choice-supportive Commitment Confirmation Selective perception Compassion fade Congruence Cultural Declinism Distinction Dunning–Kruger Egocentric Curse of knowledge Emotional Extrinsic incentives Fading affect Framing Frequency Frog pond effect Halo effect Hindsight Horn effect Hostile attribution Impact Implicit In-group Intentionality Illusion of transparency Mean world syndrome Mere-exposure effect Narrative Negativity Normalcy Omission Optimism Out-group homogeneity Outcome Overton window Precision Present Pro-innovation Proximity Response Restraint Self-serving Social comparison Social influence bias Spotlight Status quo Substitution Time-saving Trait ascription Turkey illusion von Restorff effect Zero-risk In animals
Statistical biases	Estimator Forecast Healthy user Information Psychological Lead time Length time Non-response Observer Omitted-variable Participation Recall Sampling Selection Self-selection Social desirability Spectrum Survivorship Systematic error Systemic Verification Wet
Other biases	Academic Basking in reflected glory Déformation professionnelle Funding FUTON Inductive Infrastructure Inherent In education Liking gap Media False balance Vietnam War South Asia United States Arab–Israeli conflict Ukraine Net Political bias Publication System justification Reporting White hat Ideological bias on Wikipedia
Bias reduction	Cognitive bias mitigation Debiasing Heuristics in judgment and decision-making
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