联系我们
Isaac Scientific Publishing
Psychology Research and Applications
PRA > Volume 1, Number 1, March 2019

An Attentional Bias for Occasional Cellphone Users Assessed with the Emotional Stroop Test

Download PDF  (591.8 KB)PP. 13-21,  Pub. Date:March 26, 2019
DOI: 10.22606/pra.2019.11003

Author(s)
Antonio A. Álvarez* and Lucía Otero
Affiliation(s)
Departamento de Psicología Social, Básica y Metodología, Universidad de Santiago de Compostela, Santiago de Compostela, Spain
Abstract
The use of the cellphone has drastically increased in the last few years, which entails a risk for owners that their excessive use may produce an addiction. When someone develops this dependence, they tend to show an attentional bias to information related to it. The abovementioned hypothesis has been investigated in this study using an addiction Stroop test. In light of this, 43 undergraduates, classified as high or low message senders according to their daily average, were requested to perform a Stroop task including cellphone-related, toothache-related (control condition) and neutral words. No cellphone-related attentional bias was found, but the less frequent users were faster with toothache-related words than with neutral words. Analyzing the whole sample, this Stroop facilitation effect significantly and negatively correlated with cellphone usage frequency. No evidence of a cellphone-related addiction was found, but the results indicate that cellphone use may be associated with attentional biases.
Keywords
Cellphone use frequency, addiction stroop test, attentional bias, toothache-related stroop facilitation effect.
References
  • [1]  M. Aggarwal, S. Grover, and D. Basu (2012), “Mobile phone use by resident doctors: tendency to addiction-like behavior,” German journal of psychiatry, vol. 15, no. 2, pp. 50-55.
  • [2]  G. Andersson, R. Bakhsh, L. Johansson, V. Kaldo, and P. Carlbring (2005), “Stroop facilitation in tinnitus patients: an experiment conducted via the World Wide Web,” CyberPsychology & behavior, vol. 8, no. 1, pp. 32-38.
  • [3]  Bianchi and J. G. Phillips (2005), “Psychological predictors of mobile use,” CyberPsychology & behavior, vol. 8, no. 1, pp. 39-51. DOI: 10.1089/cpb.2005.8.39.
  • [4]  J. Billieux, P. Maurage, O. López-Fernández, D. J. Kuss, and M. D. Griffiths (2015), “Can disordered mobile phone use be considered a behavioral addiction? An update on current evidence and a comprehensive model for future research,” Current addiction reports, DOI 10.1007/s40429-015-0054-y
  • [5]  J. Billieux, M. Van der Linden, and L. Rochat (2008), “The role of impulsivity in actual and problematic use of the mobile phone,” Applied cognitive psychology, vol. 22, pp. 1195-1210. DOI: 10.1002/acp.1429
  • [6]  M. Boyer and M. Dickerson (2003), “Attentional bias and addictive behavior: automaticity in a gambling-specific modified Stroop task,” Addiction, vol. 98, pp. 61-70.
  • [7]  E. Castano, B. Leidner, A. Bonacosa, J. Nikkah, R. Perrulli, B. Spencer, and N. Humphrey (2011), “Ideology, fear of death, and death anxiety,” Political psychology, vol. 32, no. 4. Doi: 10.1111/j.1467-9221.2011.00822.x
  • [8]  B. Cha, S. Najmi, J. M. Park, C. T. Finn, and M. K. Nock (2010), “Attentional bias toward suicide-related stimuli predicts suicidal behavior,” Journal of abnormal psychology, vol. 119, no. 3, pp. 616-622. DOI: 10.1037/a0019710
  • [9]  K. K. S. Chan and W. W. S. Mak (2015), “Attentional bias associated with habitual self-stigma in people with mental illness,” PLoS ONE, vol. 10, no. 7, e0125545. doi:10.1371/journal.pone.0125545
  • [10]  S. Channon and A. Hayward (1990), “The effect of short-term fasting on processing of food cues in normal subjects,” International journal of eating disorders, vol. 9, no. 4, pp. 447-452.
  • [11]  Chen, F. Liu, S. Ding, X. Ying, L. Wang, and Y. Wen (2017), “Gender differences in factors associated with smartphone addiction: a cross-sectional study among medical college students,” BMC psychiatry, vol. 17, no. 341. DOI: 10.1186/s12888-017-1503-z
  • [12]  S. I. Chiu, F. Y. Hong, and S. L. Chiu (2013), “An analysis on the correlation and gender difference between college students’ Internet addiction and mobile phone addiction in Taiwan,” ISRN Addiction, pp. 1-10. DOI: 10.1155/2013/360607
  • [13]  J. Cousijn, P. Watson, L. Koenders, W. A. M. Vingerhoets, A. E. Goudriaan, and R. W. Wiers (2013), “Cannabis dependence, cognitive control and attentional bias for cannabis words,” Addictive behavior, vol. 38, pp. 2825-2832. http://dx.doi.org/10.1016/j.addbeh.2013.08.011
  • [14]  W. M. Cox, J. S. Fadardi, and E. M. Pothos (2006), “The Addiction-Stroop Test: Theoretical considerations and procedural recommendations,” Psychological bulletin, vol. 132, no. 3, pp. 443-476. DOI: 10.1037/0033-2909.132.3.443
  • [15]  W. M. Cox and E. Klinger (2004), “A motivational model of alcohol use: Determinants of use and change,” in Handbook of motivational counseling: Concepts, approaches, and assessment. Wiley, pp. 121-138.
  • [16]  CREA. Corpus de referencia del español actual, Real Academia Española, Available: http://www.rae.es
  • [17]  R. J. Croft, D. L. Hamblin, J. Spong, A. W. Wood, R. J. McKenzie, and C. Stough (2008), “The effect of mobile phone electromagnetic fields on the alpha rhythm of human electroencephalogram,” Bioelectromagnetics, vol. 29, pp. 1-10. doi: 10.1002/bem.20352
  • [18]  J. De Sola-Gutiérrez, F. Rodríguez de Fonseca, and G. Rubio (2016), “Cell-phone addiction: a review,” Frontiers in psychiatry, 7, doi: 10.3389/fpsyt.2016.00175
  • [19]  J. S. Fadardi and W. M. Cox (2006), “Alcohol attentional bias: drinking salience or cognitive impairment?,” Psychopharmacology, vol. 185, pp. 169-178. DOI 10.1007/s00213-005-0268-0
  • [20]  I. Fernández-León and A. A. álvarez (July 2015), “A Stroop facilitation effect for death cues”. Poster presented at the 14th European Congress of Psychology, Available: www.ecp2015.it/wp-content/uploads/2015/06/ PostersECP2015.pdf (P609)
  • [21]  F. Ferreri, G. Curcio, P. Pasqualetti, L. De Gennaro, R. Fini, and P. M. Rossini (2006), “Mobile phone emissions and human brain excitability,” Annals of neurology, vol. 60, pp. 188-196.
  • [22]  G. Fortune, H. L. Richards, A. Corrin, R. J. Taylor, C. E. M. Griffiths, and C. J. Main (2003), “Attentional bias for psoriasis-specific and psychosocial threat in patients with psoriasis,” Journal of behavioral medicine, vol. 26, no. 3, pp. 211-224.
  • [23]  V. Goswami and D. R. Singh (2016), “Impact of mobile phone addiction on adolescent’s life: A literature review,” International journal of home science, vol. 2, no. 1, pp. 69-74.
  • [24]  F.-Y. Hong, S.-I. Chiu, and D.-H. Huang (2012), “A model of the relationship between psychological characteristics, mobile phone addiction and use of mobile phones by Taiwanese university female students,” Computers in human behavior, http://dx.doi.org/10.1016/j.chb.2012.06.020
  • [25]  Z. Jiang, X. Zhao, and C. Li (2017), “Self-control predicts attentional bias assessed by online shopping-related Stroop in high online shopping addiction tendency college students,” Comprehensive psychiatry, vol. 75, pp. 14-21. http://dx.doi.org/10.1016/j.comppsych.2017.02.007
  • [26]  R. Klein and B. Knäuper (2009), “Predicting attention and avoidance: When do avoiders attend?,” Psychology and health, vol. 24, no. 7, pp. 729-747. DOI: 10.1080/08870440801947779
  • [27]  S. Liu, S. D. Lane, J. M. Schmitz, A. J. Waters, K. A. Cunningham, and F. G. Moeller (2011), “Relationship between attentional bias to cocaine-related stimuli and impulsivity in cocaine-dependent subjects,” The american journal of drug and alcohol abuse, vol. 37, pp. 117-122. DOI: 10.3109/00952990.2010.543204
  • [28]  M. A. E. Marissen, I. H. A. Franken, A. J. Waters, P. Blanken, W. van den Brink, and V. M. Hendriks (2006), “Attentional bias predicts heroin relapse following treatment,” Addiction, vol. 101, pp. 1306-1312. doi: 10.1111/j.1360-0443.2006.01498.x
  • [29]  C. F. Matta and S. Burkhardt (2003), “Health risks of cellular telephones: the myth and the reality,” Ontario Public Health Association (OPHA), pp. 1-20. http://www.opha.on.ca
  • [30]  M. R. Munafò and J. Stevenson (2003), “Selective processing of threat-related cues in day surgery patients and prediction of post-operative pain,” British journal of health psychology, vol. 8, pp. 439-449.
  • [31]  M. Pothos and K. Tapper (2010), “Inducing a Stroop effect,” Applied cognitive psychology, vol. 24, no. 7, pp. 1021-1033. doi:10.1002/acp.1603
  • [32]  T. E. Robinson and K. C. Berridge (1993), “The neural basis of craving: An incentive-sensitization theory of addiction,” Brain research reviews, vol. 18, pp. 247-291.
  • [33]  J. Roelofs, M. L. Peters, and J. W. S. Vlaeyen (2002), “Selective attention for pain-related information in healthy individuals: the role of pain and fear,” Journal of pain, vol. 6, pp. 331-339. doi:10.1016/S1090-3801(02)00021-6
  • [34]  W. Schneider, A. Eschman, and A. Zuccolotto (2002). E-Prime User’s Guide. Psychology Software Tools Inc.
  • [35]  P. Smith and M. Waterman (2005), “Sex differences in processing aggression words using the Emotional Stroop task,” Aggressive behavior, vol. 31, pp. 271-282. DOI: 10.1002/ab.20071
  • [36]  S. T. Tiffany (1990), “A cognitive model of drug urges and drug-use behavior: Role of automatic and nonautomatic processes,” Psychological review, vol. 97, pp. 147-168.
  • [37]  S. P. Walsh, K. M. White, S. Cox, and R. M. Young (2011), “Keeping in constant touch: The predictors of young Australians’ mobile phone involvement,” Computers in human behavior, vol. 27, pp. 333-342.
  • [38]  A. S. Yadav and M. K. Sharma (2008), “Increased frequency in micronucleated exfoliated cells among humans exposed in vivo to mobile telephone radiations,” Mutation research, vol. 650, pp. 175-180. doi:10.1016/ j.mrgentox.2007.11.005
Copyright © 2019 Isaac Scientific Publishing Co. All rights reserved.