Journal of Abnormal Child Psychology

, Volume 45, Issue 8, pp 1477–1490 | Cite as

Is Poor Working Memory a Transdiagnostic Risk Factor for Psychopathology?

  • Cynthia Huang-Pollock
  • Zvi Shapiro
  • Hilary Galloway-Long
  • Alex Weigard
Article

Abstract

In contrast to historical conceptualizations that framed psychological disorders as distinct, categorical conditions, it is now widely understood that co- and multi-morbidities between disorders are extensive. As a result, there has been a call to better understand the dimensional liabilities that are common to and influence the development of multiple psychopathologies, as supported and exemplified by the National Institutes of Mental Health (NIMH) Research Domain Criteria (RDoC) framework. We use a latent variable SEM approach to examine the degree to which working memory deficits represent a cognitive liability associated with the development of common and discrete dimensions of psychopathology. In a sample of 415 community recruited children aged 8–12 (n = 170 girls), we fit a bi-factor model to parent reports of behavior from the DISC-4 and BASC-2, and included a latent working memory factor as a predictor of the internalizing, externalizing, and general “p-factor.” We found that both the general “p-factor” and externalizing (but not internalizing) latent factor were significantly associated with working memory. When a bi-factor model of externalizing symptomology was fit to further explore this relationship, working memory was only correlated with the general externalizing dimension; correlation with specific inattention, hyperactive/impulsive, and oppositional factors did not survive once the general externalizing dimension was taken into consideration. These findings held regardless of the sex of the child. Our results suggest that working memory deficits represent both a common cognitive liability for mental health disorders, and a specific liability for externalizing disorders.

Keywords

Executive function Working memory Bifactor P-factor Externalizing 

Notes

Compliance with Ethical Standards

Funding

This work was supported in part by National Institute of Mental Health Grant R01 MH084947 to Cynthia Huang-Pollock. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Mental Health or the National Institutes of Health.

Conflicts of Interest

The authors declare that there are no conflicts of interest.

Ethical Approval

All procedures were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent (assent for children) was obtained from all individual participants included in the study.

References

  1. Abikoff, H., Courtney, M., Pelham, W. E., & Koplewicz, H. S. (1993). Teachers ratings of disruptive behaviors: the influence of halo effects. Journal of Abnormal Child Psychology, 21, 519–533. doi: 10.1007/bf00916317.PubMedCrossRefGoogle Scholar
  2. Achenbach, T. M., & Edelbrock, C. S. (1978). Classification of child psychopathology: review and analysis of empirical efforts. Psychological Bulletin, 85, 1275–1301. doi: 10.1037//0033-2909.85.6.1275.PubMedCrossRefGoogle Scholar
  3. Achenbach, T. M., McConaughy, S. H., & Howell, C. T. (1987). Child adolescent behavioral and emotional problems: implications of cross-informant correlations for situational specificity. Psychological Bulletin, 101, 213–232. doi: 10.1037/0033-2909.101.2.213.PubMedCrossRefGoogle Scholar
  4. Anderson, J. R. (1982). Acquisition of cognitive skill. Psychological Review, 89, 369–406.CrossRefGoogle Scholar
  5. Angold, A., Costello, E. J., & Erkanli, A. (1999). Comorbidity. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 40, 57–87. doi: 10.1017/s0021963098003448.PubMedCrossRefGoogle Scholar
  6. Arias, V. B., Ponce, F. P., Martínez-Molina, A., Arias, B., & Núñez, D. (2016). General and specific attention-deficit/hyperactivity disorder factors of children 4 to 6 years of age: an exploratory structural equation modeling approach to assessing symptom multidimensionality. Journal of Abnormal Psychology, 125, 125–137. doi: 10.1037/abn0000115.PubMedCrossRefGoogle Scholar
  7. Baddeley, A. D. (1986). Working memory. Oxford: Oxford University Press.Google Scholar
  8. Barkley, R. A. (1997). Behavioral inhibition, sustained attention, and executive functions: constructing a unifying theory of ADHD. Psychological Bulletin, 121, 65–94.PubMedCrossRefGoogle Scholar
  9. Barkley, R. A., Edwards, G., Laneri, M., Fletcher, K., & Metevia, L. (2001). Executive functioning, temporal discounting, and sense of time in adolescents with attention deficit hyperactivity disorder (ADHD) and oppositional defiant disorder (ODD). Journal of Abnormal Child Psychology, 29, 541–556.PubMedCrossRefGoogle Scholar
  10. Barnett, R., Maruff, P., & Vance, A. (2009). Neurocognitive function in attention-deficit-hyperactivity disorder with and without comorbid disruptive behaviour disorders. Australian and New Zealand Journal of Psychiatry, 43, 722–730. doi: 10.1080/00048670903001927.PubMedCrossRefGoogle Scholar
  11. Barrouillet, P., Bernardin, S., & Camos, V. (2004). Time constraints and resource sharing in adults' working memory spans. Journal of Experimental Psychology. General, 133, 83–100. doi: 10.1037/0096-3445.133.1.83.PubMedCrossRefGoogle Scholar
  12. Benitez, A., Horner, M. D., & Bachman, D. (2011). Intact cognition in depressed elderly veterans providing adequate effort. Archives of Clinical Neuropsychology, 26, 184–193. doi: 10.1093/arclin/acr001.PubMedCrossRefGoogle Scholar
  13. Bentler, P. M. (1990). Comparative fit indices in structural models. Psychological Bulletin, 107, 238–246. doi: 10.1037/0033-2909.107.2.238.PubMedCrossRefGoogle Scholar
  14. Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88, 588–606. doi: 10.1037/0033-2909.88.3.588.CrossRefGoogle Scholar
  15. Berggren, N., & Derakshan, N. (2013). Attentional control deficits in trait anxiety: why you see them and why you don't. Biological Psychology, 92, 440–446. doi: 10.1016/j.biopsycho.2012.03.007.PubMedCrossRefGoogle Scholar
  16. Biederman, J., Newcorn, J., & Sprich, S. (1991). Comorbidity of attention deficit hyperactivity disorder with conduct, depressive, anxiety, and other disorders. American Journal of Psychiatry, 148, 564–577.PubMedCrossRefGoogle Scholar
  17. Bishop, S. J. (2009). Trait anxiety and impoverished prefrontal control of attention. Nature Neuroscience, 12, 92–98. doi: 10.1038/nn.2242.PubMedCrossRefGoogle Scholar
  18. Botvinick, M., & Braver, T. (2015). Motivation and cognitive control: from behavior to neural mechanism. Annual Review of Psychology, 66, 83–113. doi: 10.1146/annurev-psych-010814-015044.PubMedCrossRefGoogle Scholar
  19. Braver, T. S., Krug, M. K., Chiew, K. S., Kool, W., Westbrook, J. A., Clement, N. J., & Somerville, L. H. (2014). Mechanisms of motivation-cognition interaction: challenges and opportunities. Cognitive, Affective, & Behavioral Neuroscience, 14, 443–472. doi: 10.3758/s13415-014-0300-0.CrossRefGoogle Scholar
  20. Brown, T. A. (2015). Confirmatory factor analysis for applied research (2nd ed.). New York: Guilford Publications.Google Scholar
  21. Browne, M. W., & Cudeck, R. (1992). Alternative ways of assessing model fit. Sociological Methods & Research, 21, 230–258. doi: 10.1177/0049124192021002005.CrossRefGoogle Scholar
  22. Bull, R., Espy, K. A., & Wiebe, S. A. (2008). Short-term memory, working memory, and executive functioning in preschoolers: longitudinal predictors of mathematical achievement at age 7 years. Developmental Neuropsychology, 33, 205–228. doi: 10.1080/87565640801982312.PubMedPubMedCentralCrossRefGoogle Scholar
  23. Case, R., Kurland, D. M., & Goldberg, J. (1982). Operational efficiency and the growth of short-term-memory span. Journal of Experimental Child Psychology, 33, 386–404.CrossRefGoogle Scholar
  24. Caspi, A., Houts, R. M., Belsky, D. W., Goldman-Mellor, S. J., Harrington, H., Israel, S., & Moffitt, T. E. (2014). The p factor: one general psychopathology factor in the structure of psychiatric disorders? Clinical Psychological Science, 2, 119–137. doi: 10.1177/2167702613497473.PubMedPubMedCentralCrossRefGoogle Scholar
  25. Cicchetti, D., & Rogosch, F. A. (1996). Equifinality and multifinality in developmental psychopathology. Development and Psychopathology, 8, 597–600.CrossRefGoogle Scholar
  26. Cohen-Gilbert, J. E., Killgore, W. D. S., White, C. N., Schwab, Z. J., Crowley, D. J., Covell, M. J., et al. (2014). Differential influence of safe versus threatening facial expressions on decision-making during an inhibitory control task in adolescence and adulthood. Developmental Science, 17, 212–223. doi: 10.1111/desc.12123.PubMedPubMedCentralCrossRefGoogle Scholar
  27. Collishaw, S., Goodman, R., Ford, T., Rabe-Hesketh, S., & Pickles, A. (2009). How far are associations between child, family and community factors and child psychopathology informant-specific and informant-general? Journal of Child Psychology and Psychiatry, 50, 571–580. doi: 10.1111/j.1469-7610.2008.02026.x.PubMedCrossRefGoogle Scholar
  28. Conners, C. K. (2008). Conners’ rating scales—3 technical manual. New York: Multi-Health Systems Inc..Google Scholar
  29. Costello, E. J., Mustillo, S., Erkanli, A., Keeler, G., & Angold, A. (2003). Prevalence and development of psychiatric disorders in childhood and adolescence. Archives of General Psychiatry, 60, 837–844. doi: 10.1001/archpsyc.60.8.837.PubMedCrossRefGoogle Scholar
  30. Cramer, A. O. J., Waldorp, L. J., van der Maas, H. L. J., & Borsboom, D. (2010). Comorbidity: a network perspective. Behavioral and Brain Sciences, 33, 137–150. doi: 10.1017/s0140525x09991567.PubMedCrossRefGoogle Scholar
  31. Crick, N. R., & Zahn-Waxler, C. (2003). The development of psychopathology in females and males: current progress and future challenges. Development and Psychopathology, 15, 719–742. doi: 10.1017/s095457940300035x.PubMedCrossRefGoogle Scholar
  32. Daneman, M., & Carpenter, P. A. (1980). Individual-differences in working memory and reading. Journal of Verbal Learning and Verbal Behavior, 19, 450–466.CrossRefGoogle Scholar
  33. De Los Reyes, A., & Kazdin, A. E. (2005). Informant discrepancies in the assessment of childhood psychopathology: a critical review, theoretical framework, and recommendations for further study. Psychological Bulletin, 131, 483–509. doi: 10.1037/0033-2909.131.4.483.PubMedCrossRefGoogle Scholar
  34. Edwards, E. J., Edwards, M. S., & Lyvers, M. (2015). Cognitive trait anxiety, situational stress, and mental effort predict shifting efficiency: implications for attentional control theory. Emotion, 15, 350–359. doi: 10.1037/emo0000051.PubMedCrossRefGoogle Scholar
  35. Enders, C. K. (2010). Applied missing data analysis. New York: Guilford Press.Google Scholar
  36. Engle, R., Kane, M., & Tuholski, S. (1999). Individual differences in working memory capacity and what they tell us about controlled attention, general fluid intelligence, and functions of the prefrontal cortex. In A. Miyake & P. Shah (Eds.), Models of working memory: mechanisms of active maintenance and executive control (pp. 102–134). New York: Cambridge University Press.CrossRefGoogle Scholar
  37. Essex, M. J., Kraemer, H. C., Armstrong, J. M., Boyce, T., Goldsmith, H. H., Klein, M. H., & Kupfer, D. J. (2006). Exploring risk factors for the emergence of children's mental health problems. Archives of General Psychiatry, 63, 1246–1256. doi: 10.1001/archpsyc.63.11.1246.PubMedCrossRefGoogle Scholar
  38. Eysenck, M. W., & Derakshan, N. (2011). New perspectives in attentional control theory. Personality and Individual Differences, 50, 955–960. doi: 10.1016/j.paid.2010.08.019.CrossRefGoogle Scholar
  39. Ford, T., Goodman, R., & Meltzer, H. (2003). The British child and adolescent mental health survey 1999: the prevalence of DSM-IV disorders. Journal of the American Academy of Child and Adolescent Psychiatry, 42, 1203–1211. doi: 10.1097/01.chi.0000081820.25107.ae.PubMedCrossRefGoogle Scholar
  40. Fry, A. F., & Hale, S. (1996). Processing speed, working memory, and fluid intelligence: evidence for a developmental cascade. Psychological Science, 7, 237–241.CrossRefGoogle Scholar
  41. Fry, A. F., & Hale, S. (2000). Relationships among processing speed, working memory, and fluid intelligence in children. Biological Psychology, 54, 1–34.PubMedCrossRefGoogle Scholar
  42. Geary, D. C. (2011). Cognitive predictors of achievement growth in mathematics: a 5-year longitudinal study. Developmental Psychology, 47, 1539–1552. doi: 10.1037/a0025510.PubMedPubMedCentralCrossRefGoogle Scholar
  43. Gottesman, I. I., & Gould, T. D. (2003). The endophenotype concept in psychiatry: etymology and strategic intentions. American Journal of Psychiatry, 160, 636–645.PubMedCrossRefGoogle Scholar
  44. Granic, I. (2014). The role of anxiety in the development, maintenance, and treatment of childhood aggression. Development and Psychopathology, 26, 1515–1530. doi: 10.1017/s0954579414001175.PubMedCrossRefGoogle Scholar
  45. Hill, E. L. (2004). Executive dysfunction in autism. Trends in Cognitive Sciences, 8, 26–32. doi: 10.1016/j.tics.2003.11.003.PubMedCrossRefGoogle Scholar
  46. Hu, L.-T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6, 1–55. doi: 10.1080/10705519909540118.CrossRefGoogle Scholar
  47. Huang-Pollock, C. L., Mikami, A. Y., Pfiffner, L., & McBurnett, K. (2007). ADHD subtype differences in motivational responsivity but not inhibitory control: evidence from a reward-based variation of the stop signal paradigm. Journal of Clinical Child and Adolescent Psychology, 36, 127–136.PubMedCrossRefGoogle Scholar
  48. Huang-Pollock, C. L., Karalunas, S. L., Tam, H., & Moore, A. N. (2012). Evaluating vigilance deficits in ADHD: a meta-analysis of CPT performance. Journal of Abnormal Psychology, 121, 360–371. doi: 10.1037/a0027205.PubMedPubMedCentralCrossRefGoogle Scholar
  49. Huang-Pollock, C., Ratcliff, R., McKoon, G., Shapiro, Z., Weigard, A., & Galloway-Long, H. (2016). Using the diffusion model to explain cognitive deficits in attention deficit hyperactivity disorder. Journal of Abnormal Child Psychology, 1–12. doi: 10.1007/s10802-016-0151-y.
  50. Hughes, C., Russell, J., & Robbins, T. W. (1994). Evidence for executive dysfunction in autism. Neuropsychologia, 32, 477–492. doi: 10.1016/0028-3932(94)90092-2.PubMedCrossRefGoogle Scholar
  51. Jensen, P. S., Martin, D., & Cantwell, D. P. (1997). Comorbidity in ADHD: implications for research, practice, and DSM-V. Journal of the American Academy of Child and Adolescent Psychiatry, 36, 1065–1079.PubMedCrossRefGoogle Scholar
  52. Jöreskog, K. G. (1971). Simultaneous factor analysis in several populations. Psychometrika, 36, 409–426. doi: 10.1007/BF02291366.CrossRefGoogle Scholar
  53. Kail, R. (1992). Processing speed, speech rate, and memory. Developmental Psychology, 28, 899–904.CrossRefGoogle Scholar
  54. Kail, R. (2007). Longitudinal evidence that increases in processing speed and working memory enhance children's reasoning. Psychological Science, 18, 312–313.PubMedCrossRefGoogle Scholar
  55. Kail, R., & Salthouse, T. A. (1994). Processing speed as a mental-capacity. Acta Psychologica, 86, 199–225.PubMedCrossRefGoogle Scholar
  56. Kane, M. J., Conway, A., Hambrick, D. Z., & Engle, R. W. (2007). Variation in working memory capacity as variation in executive attention and control. In A. R. A. Conway, C. Jarrold, M. J. Kane, A. Miyake, & J. N. Towse (Eds.), Variation in working memory. New York: Oxford University Press.Google Scholar
  57. Karalunas, S. L., & Huang-Pollock, C. L. (2013). Integrating impairments in reaction time and executive function using a diffusion model framework. Journal of Abnormal Child Psychology, 41, 837–850. doi: 10.1007/s10802-013-9715-2.PubMedPubMedCentralCrossRefGoogle Scholar
  58. Karalunas, S. L., Huang-Pollock, C. L., & Nigg, J. T. (2012). Decomposing attention-deficit/hyperactivity disorder (ADHD)-related effects in response speed and variability. Neuropsychology, 26, 684–694. doi: 10.1037/a0029936.PubMedPubMedCentralCrossRefGoogle Scholar
  59. Kendler, K. S., Walters, E. E., Neale, M. C., Kessler, R. C., Heath, A. C., & Eaves, L. J. (1995). The structure of the genetic and environmental risk factors for 6 major psychiatric disorders in women: phobia, generalized anxiety DIsorder, panic disorder, bulimia, major depression, and alcoholism. Archives of General Psychiatry, 52, 374–383.PubMedCrossRefGoogle Scholar
  60. Kendler, K. S., Prescott, C. A., Myers, J., & Neale, M. C. (2003). The structure of genetic and environmental risk factors for common psychiatric and substance use disorders in men and women. Archives of General Psychiatry, 60, 929–937. doi: 10.1001/archpsyc.60.9.929.PubMedCrossRefGoogle Scholar
  61. Kessler, R. C., McGonagle, K. A., Zhao, S. Y., Nelson, C. B., Hughes, M., Eshleman, S., & Kendler, K. S. (1994). Lifetime and 12-month prevalence of DSM-III psychiatric disorders in the United States: results from the National Comorbidity Survey. Archives of General Psychiatry, 51, 8–19.PubMedCrossRefGoogle Scholar
  62. Kessler, R. C., Chiu, W. T., Demler, O., & Walters, E. E. (2005). Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication. Archives of General Psychiatry, 62, 617–627. doi: 10.1001/archpsyc.62.6.617.PubMedPubMedCentralCrossRefGoogle Scholar
  63. Krueger, R. F. (1999). The structure of common mental disorders. Archives of General Psychiatry, 56, 921–926. doi: 10.1001/archpsyc.56.10.921.PubMedCrossRefGoogle Scholar
  64. Krueger, R. F., Caspi, A., Moffitt, T. E., & Silva, P. A. (1998). The structure and stability of common mental disorders (DSM-III-R): a longitudinal-epidemiological study. Journal of Abnormal Psychology, 107, 216–227. doi: 10.1037/0021-843x.107.2.216.PubMedCrossRefGoogle Scholar
  65. Laceulle, O. M., Vollebergh, W. A. M., & Ormel, J. (2015). The structure of psychopathology in adolescence: replication of a general psychopathology factor in the TRAILS study. Clinical Psychological Science, 3, 850–860. doi: 10.1177/2167702614560750.CrossRefGoogle Scholar
  66. Lahey, B. B., Applegate, B., McBurnett, K., Biederman, J., Greenhill, L., Hynd, G. W., & Shaffer, D. (1994). DSM-IV field trials for attention-deficit hyperactivity disorder in children and adolescents. American Journal of Psychiatry, 151, 1673–1685.PubMedCrossRefGoogle Scholar
  67. Lahey, B. B., Rathouz, P. J., Van Hulle, C., Urbano, R. C., Krueger, R. F., Applegate, B., et al. (2008). Testing structural models of DSM-IV symptoms of common forms of child and adolescent psychopathology. Journal of Abnormal Child Psychology, 36, 187–206. doi: 10.1007/s10802-007-9169-5.PubMedCrossRefGoogle Scholar
  68. Lahey, B. B., Applegate, B., Hakes, J. K., Zald, D. H., Hariri, A. R., & Rathouz, P. J. (2012). Is there a general factor of prevalent psychopathology during adulthood? Journal of Abnormal Psychology, 121, 971–977. doi: 10.1037/a0028355.PubMedPubMedCentralCrossRefGoogle Scholar
  69. Lahey, B. B., Rathouz, P. J., Keenan, K., Stepp, S. D., Loeber, R., & Hipwell, A. E. (2015). Criterion validity of the general factor of psychopathology in a prospective study of girls. Journal of Child Psychology and Psychiatry, 56, 415–422. doi: 10.1111/jcpp.12300.PubMedCrossRefGoogle Scholar
  70. Logan, G. (1992). Attention and preattention in theories of automaticity. American Journal of Psychology, 105, 317–339.PubMedCrossRefGoogle Scholar
  71. Luman, M., Oosterlaan, J., & Sergeant, J. A. (2005). The impact of reinforcement contingencies on AD/HD: a review and theoretical appraisal. Clinical Psychology Review, 25, 183–213. doi: 10.1016/j.cpr.2004.11.001.PubMedCrossRefGoogle Scholar
  72. Malhotra, D., & Sebat, J. (2012). CNVs: harbingers of a rare variant revolution in psychiatric genetics. Cell, 148, 1223–1241. doi: 10.1016/j.cell.2012.02.039.PubMedPubMedCentralCrossRefGoogle Scholar
  73. Marsh, H. W., Byrne, B. M., & Craven, R. (1992). Overcoming problems in confirmatory factor analysis of MTMM data: the correlated uniqueness model and factorial invariance. Multivariate Behavioral Research, 27, 489–507. doi: 10.1207/s15327906mbr2704_1.PubMedCrossRefGoogle Scholar
  74. Martel, M. M., Gremillion, M., Roberts, B., von Eye, A., & Nigg, J. T. (2010a). The structure of childhood disruptive behaviors. Psychological Assessment, 22, 816–826. doi: 10.1037/a0020975.PubMedPubMedCentralCrossRefGoogle Scholar
  75. Martel, M. M., von Eye, A., & Nigg, J. T. (2010b). Revisiting the latent structure of ADHD: is there a 'g' factor? Journal of Child Psychology and Psychiatry, 51, 905–914. doi: 10.1111/j.1469-7610.2010.02232.x.PubMedPubMedCentralCrossRefGoogle Scholar
  76. Martel, M. M., Roberts, B., Gremillion, M., von Eye, A., & Nigg, J. T. (2011). External validation of bifactor model of ADHD: explaining heterogeneity in psychiatric comorbidity, cognitive control, and personality trait profiles within DSM-IV ADHD. Journal of Abnormal Child Psychology, 39, 1111–1123. doi: 10.1007/s10802-011-9538-y.PubMedPubMedCentralCrossRefGoogle Scholar
  77. Martel, M. M., von Eye, A., & Nigg, J. (2012). Developmental differences in structure of attention-deficit/hyperactivity disorder (ADHD) between childhood and adulthood. International Journal of Behavioral Development, 36, 279–292. doi: 10.1177/0165025412444077.PubMedPubMedCentralCrossRefGoogle Scholar
  78. McAlonan, G. M., Cheung, V., Cheung, C., Chua, S. E., Murphy, D. G. M., Suckling, J., et al. (2007). Mapping brain structure in attention deficit-hyperactivity disorder: a voxel-based MRI study of regional grey and white matter volume. Psychiatry Research: Neuroimaging, 154, 171–180. doi: 10.1016/j.pscychresns.2006.09.006.PubMedCrossRefGoogle Scholar
  79. McClintock, S. A., Husain, M. M., Greer, T. L., & Cullum, C. M. (2010). Association between depression severity and neurocognitive function in major depressive disorder: a review and synthesis. Neuropsychology, 24, 9–34. doi: 10.1037/a0017336.PubMedCrossRefGoogle Scholar
  80. McLean, J. F., & Hitch, G. J. (1999). Working memory impairments in children with specific arithmetic learning difficulties. Journal of Experimental Child Psychology, 74, 240–260. doi: 10.1006/jecp.1999.2516.PubMedCrossRefGoogle Scholar
  81. Meinzer, M. C., Pettit, J. W., & Viswesvaran, C. (2014). The co-occurrence of attention-deficit/hyperactivity disorder and unipolar depression in children and adolescents: a meta-analytic review. Clinical Psychology Review, 34, 595–607. doi: 10.1016/j.cpr.2014.10.002.PubMedCrossRefGoogle Scholar
  82. Merikangas, K. R., He, J. P., Burstein, M., Swanson, S. A., Avenevoli, S., Cui, L. H., et al. (2010). Lifetime prevalence of mental disorders in U.S. adolescents: results from the National Comorbidity Survey Replication-Adolescent Supplement (NCS-A). Journal of the American Academy of Child and Adolescent Psychiatry, 49, 980–989. doi: 10.1016/j.jaac.2010.05.017.PubMedPubMedCentralCrossRefGoogle Scholar
  83. Moffitt, T. E. (1993). The neuropsychology of conduct disorder. Development and Psychopathology, 5, 135–151.CrossRefGoogle Scholar
  84. Morgan, A. B., & Lilienfeld, S. O. (2000). A meta-analytic review of the relation between antisocial behavior and neuropsychological measures of executive function. Clinical Psychology Review, 20, 113–136.PubMedCrossRefGoogle Scholar
  85. Moustafa, A. A., Keri, S., Somlai, Z., Balsdon, T., Frydecka, D., Misiak, B., & White, C. (2015). Drift diffusion model of reward and punishment learning in schizophrenia: modeling and experimental data. Behavioural Brain Research, 291, 147–154. doi: 10.1016/j.bbr.2015.05.024.PubMedCrossRefGoogle Scholar
  86. Muthén, L.K., & Muthén, B.O. (1998-2012). Mplus user’s guide, 7th Ed. Los Angeles, CA: Muthén & MuthénGoogle Scholar
  87. Nieuwenstein, M. R., Aleman, A., & de Haan, E. H. F. (2001). Relationship between symptom dimensions and neurocognitive functioning in schizophrenia: a meta-analysis of WCST and CPT studies. Journal of Psychiatric Research, 35, 119–125.PubMedCrossRefGoogle Scholar
  88. Norman, D. A., & Shallice, T. (1986). Attention to action: willed and automatic control of behavior. In R. J. Davidson, G. E. Schwartz, & D. Shapiro (Eds.), Consciousness and self-regulation: advances in research and theory (Vol. 4, pp. 1–18). New York: Plenum Press.Google Scholar
  89. Oosterlaan, J., Logan, G., & Sergeant, J. A. (1998). Response inhibition in AD/HD, CD, comorbid AD/HD + CD, anxious, and control children: a meta-analysis of studies with the stop task. Journal of Child Psychology and Psychiatry, 39, 411–425.PubMedCrossRefGoogle Scholar
  90. Paelecke-Habermann, Y., Pohl, J., & Leplow, B. (2005). Attention and executive functions in remitted major depression patients. Journal of Affective Disorders, 89, 125–135. doi: 10.1016/j.jad.2005.09.006.PubMedCrossRefGoogle Scholar
  91. Pennington, B. F., & Ozonoff, S. (1996). Executive functions and developmental psychopathology. Journal of Child Psychology and Psychiatry, 37, 51–87.PubMedCrossRefGoogle Scholar
  92. Pessoa, L. (2009). How do emotion and motivation direct executive control? Trends in Cognitive Sciences, 13, 160–166. doi: 10.1016/j.tics.2009.01.006.PubMedPubMedCentralCrossRefGoogle Scholar
  93. Quraishi, S., & Frangou, S. (2002). Neuropsychology of bipolar disorder: a review. Journal of Affective Disorders, 72, 209–226. doi: 10.1016/s0165-0327(02)00091-5.PubMedCrossRefGoogle Scholar
  94. Raghubar, K. P., Barnes, M. A., & Hecht, S. A. (2010). Working memory and mathematics: a review of developmental, individual difference, and cognitive approaches. Learning and Individual Differences, 20, 110–122. doi: 10.1016/j.lindif.2009.10.005.CrossRefGoogle Scholar
  95. Raine, A., Buchsbaum, M. S., Stanley, J., Lottenberg, S., Abel, L., & Stoddard, J. (1994). Selective reductions in prefrontal glucose metabolism in murderers. Biological Psychiatry, 36, 365–373. doi: 10.1016/0006-3223(94)91211-4.PubMedCrossRefGoogle Scholar
  96. Raine, A., Moffitt, T. E., Caspi, A., Loeber, R., Stouthamer-Loeber, M., & Lynam, D. (2005). Neurocognitive impairments in boys on the life-course persistent antisocial path. Journal of Abnormal Psychology, 114, 38–49. doi: 10.1037/0021-843x.114.1.38.PubMedCrossRefGoogle Scholar
  97. Ratcliff, R., & McKoon, G. (2008). The diffusion decision model: theory and data for two-choice decision tasks. Neural Computation, 20, 873–922. doi: 10.1162/neco.2008.12-06-420.PubMedPubMedCentralCrossRefGoogle Scholar
  98. Ratcliff, R., Thapar, A., & McKoon, G. (2004). A diffusion model analysis of the effects of aging on recognition memory. Journal of Memory and Language, 50, 408–424. doi: 10.1016/j.jml.2003.11.002.CrossRefGoogle Scholar
  99. Ratcliff, R., Thapar, A., & McKoon, G. (2011). Effects of aging and IQ on item and associative memory. Journal of Experimental Psychology. General, 140, 464–487. doi: 10.1037/a0023810.PubMedPubMedCentralCrossRefGoogle Scholar
  100. Ratcliff, R., Love, J., Thompson, C. A., & Opfer, J. E. (2012). Children are not like older adults: a diffusion model analysis of developmental changes in speeded responses. Child Development, 83, 367–381. doi: 10.1111/j.1467-8624.2011.01683.x.PubMedCrossRefGoogle Scholar
  101. Reynolds, C., & Kamphaus, R. (2004). Behavioral assessment system for children, 2nd Ed. Manual. MN: AGS Publishing.Google Scholar
  102. Rogers, M. A., Kasai, K., Koji, M., Fukuda, R., Iwanami, A., Nakagome, K., et al. (2004). Executive and prefrontal dysfunction in unipolar depression: a review of neuropsychological and imaging evidence. Neuroscience Research, 50, 1–11. doi: 10.1016/j.neures.2004.05.003.PubMedCrossRefGoogle Scholar
  103. Rohling, M. L., Green, P., Allen, L. M., & Iverson, G. L. (2002). Depressive symptoms and neurocognitive test scores in patients passing symptom validity tests. Archives of Clinical Neuropsychology, 17, 205–222. doi: 10.1016/s0887-6177(01)00109-3.PubMedCrossRefGoogle Scholar
  104. Satorra, A., & Bentler, P. M. (2001). A scaled difference chi-square test statistic for moment structure analysis. Psychometrika, 66, 507–514. doi: 10.1007/bf02296192.CrossRefGoogle Scholar
  105. Satorra, A., & Bentler, P. M. (2010). Ensuring positiveness of the scaled difference chi-square test statistic. Psychometrika, 75, 243–248. doi: 10.1007/s11336-009-9135-y.PubMedPubMedCentralCrossRefGoogle Scholar
  106. Schachar, R., Mota, V. L., Logan, G. D., Tannock, R., & Klim, P. (2000). Confirmation of an inhibitory control deficit in attention-deficit/hyperactivity disorder. Journal of Abnormal Child Psychology, 28, 227–235. doi: 10.1023/a:1005140103162.PubMedCrossRefGoogle Scholar
  107. Shaffer, D., Fisher, P., Lucas, C. P., Dulcan, M. K., & Schwab-Stone, M. E. (2000). NIMH diagnostic interview schedule for children version IV (NIMH DISC-IV): description, differences from previous versions, and reliability of some common diagnoses. Journal of the American Academy of Child and Adolescent Psychiatry, 39, 28–38.PubMedCrossRefGoogle Scholar
  108. Shanahan, M. A., Pennington, B. F., & Willcutt, E. W. (2008). Do motivational incentives reduce the inhibition deficit in ADHD? Developmental Neuropsychology, 33, 137–159. doi: 10.1080/87565640701884238.PubMedCrossRefGoogle Scholar
  109. Sheslow, D., & Adams, W. (2003). Wide range assessment of memory and learning, 2nd Ed (WRAML-2): administration and technical manual. DE: Wide Range.Google Scholar
  110. Shiels, K., Hawk Jr., L. W., Lysczek, C. L., Tannock, R., Pelham Jr., W. E., Spencer, S. V., et al. (2008). The effects of incentives on visual-spatial working memory in children with attention-deficit/hyperactivity disorder. Journal of Abnormal Child Psychology, 36, 903–913. doi: 10.1007/s10802-008-9221-0.PubMedPubMedCentralCrossRefGoogle Scholar
  111. Shiffrin, R. M., & Schneider, W. (1977). Controlled and automatic human information processing. II. Perceptual learning, automatic attending, and a general theory. Psychological Review, 84, 127–190.CrossRefGoogle Scholar
  112. Smoller, J. W., Craddock, N., Kendler, K., Lee, P. H., Neale, B. M., Nurnberger, J. I., et al. (2013). Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet, 381, 1371–1379. doi: 10.1016/s0140-6736(12)62129-1.CrossRefGoogle Scholar
  113. Snyder, H. R., Miyake, A., & Hankin, B. L. (2015). Advancing understanding of executive function impairments and psychopathology: bridging the gap between clinical and cognitive approaches. Frontiers in Psychology, 6, 24. doi: 10.3389/fpsyg.2015.00728.CrossRefGoogle Scholar
  114. Starns, J. J., & Ratcliff, R. (2010). The effects of aging on the speed-accuracy compromise: boundary optimality in the diffusion model. Psychology and Aging, 25, 377–390. doi: 10.1037/a0018022.PubMedPubMedCentralCrossRefGoogle Scholar
  115. Steiger, J., & Lind, J. (1980). Statistically based tests for the number of common factors. Paper presented at the psychometric society annual meeting, Iowa City.Google Scholar
  116. Tackett, J. L., Lahey, B. B., van Hulle, C., Waldman, I., Krueger, R. F., & Rathouz, P. J. (2013). Common genetic influences on negative emotionality and a general psychopathology factor in childhood and adolescence. Journal of Abnormal Psychology, 122, 1142–1153. doi: 10.1037/a0034151.PubMedPubMedCentralCrossRefGoogle Scholar
  117. Tannock, R. (2009). ADHD with anxiety disorders. In T. E. Brown (Ed.), ADHD comorbidities: handbook for ADHD complications in children and adults (pp. 131–155). Washington, D.C.: American Psychiatric Publishing, Inc..Google Scholar
  118. Toplak, M. E., Pitch, A., Flora, D. B., Iwenofu, L., Ghelani, K., Jain, U., & Tannock, R. (2009). The unity and diversity of inattention and hyperactivity/impulsivity in ADHD: evidence for a general factor with separable dimensions. Journal of Abnormal Child Psychology, 37, 1137–1150. doi: 10.1007/s10802-009-9336-y.PubMedCrossRefGoogle Scholar
  119. Toplak, M. E., Sorge, G. B., Flora, D. B., Chen, W., Banaschewski, T., Buitelaar, J., et al. (2012). The hierarchical factor model of ADHD: invariant across age and national groupings? Journal of Child Psychology and Psychiatry, 53, 292–303. doi: 10.1111/j.1469-7610.2011.02500.x.PubMedCrossRefGoogle Scholar
  120. Towse, J., Hutton, U., & Hitch, G. (1998). Grass is coloured…red? Further sentence completion norms for children during a working memory reading task (Tech Rep No CDRG3). Retrieved from http://www.pc.rhbnc.ac.uk/papers/tr.html.
  121. Tucker, L. R., & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38, 1–10. doi: 10.1007/BF02291170.CrossRefGoogle Scholar
  122. Unsworth, N., & Engle, R. W. (2006). Simple and complex memory spans and their relation to fluid abilities: evidence from list-length effects. Journal of Memory and Language, 54, 68–80. doi: 10.1016/j.jml.2005.06.003.CrossRefGoogle Scholar
  123. Unsworth, N., & Engle, R. W. (2007). The nature of individual differences in working memory capacity: active maintenance in primary memory and controlled search from secondary memory. Psychological Review, 114, 104–132. doi: 10.1037/0033-295x.114.1.104.PubMedCrossRefGoogle Scholar
  124. Wechsler, D. (2003). Wechsler intelligence scale for children- fourth edition: technical and interpretive manual. San Antonio: Harcourt Brace.Google Scholar
  125. Weigard, A., & Huang-Pollock, C. L. (2014). A diffusion modeling approach to understanding contextual cueing effects in children with ADHD. Journal of Child Psychology and Psychiatry, 55, 1336–1344. doi: 10.1111/jcpp.12250.PubMedPubMedCentralCrossRefGoogle Scholar
  126. Weigard, A., & Huang-Pollock, C. L. (2016). The role of speed in ADHD-related working memory deficits: a time-based resource-sharing and diffusion model account. Clinical Psychological Science, (in press).Google Scholar
  127. Weigard, A., Huang-Pollock, C., & Brown, S. (2016). Evaluating the consequences of impaired monitoring of learned behavior in attention-deficit/hyperactivity disorder using a Bayesian hierarchical model of choice response time. Neuropsychology, 30, 502–515. doi: 10.1037/neu0000257.PubMedPubMedCentralCrossRefGoogle Scholar
  128. White, J. L., Moffitt, T. E., Caspi, A., Bartusch, D. J., Needles, D. J., & Stouthamerloeber, M. (1994). Measuring impulsivity and examining its relationship to delinquency. Journal of Abnormal Psychology, 103, 192–205. doi: 10.1037/0021-843x.103.2.192.PubMedCrossRefGoogle Scholar
  129. Wiecki, T. V., Poland, J., & Frank, M. J. (2015). Model-based cognitive neuroscience approaches to computational psychiatry: clustering and classification. Clinical Psychological Science, 3, 378–399. doi: 10.1177/2167702614565359.CrossRefGoogle Scholar
  130. Willcutt, E. G., Pennington, B. F., Boada, R., Ogline, J. S., Tunick, R. A., Chhabildas, N. A., & Olson, R. K. (2001). A comparison of the cognitive deficits in reading disability and attention-deficit/hyperactivity disorder. Journal of Abnormal Psychology, 110, 157–172. doi: 10.1037//0021-843x.1001.1.157.PubMedCrossRefGoogle Scholar
  131. Willcutt, E. G., Doyle, A., Nigg, J. T., Faraone, S. V., & Pennington, B. F. (2005). Validity of the executive function theory of attention-deficit/hyperactivity disorder: a meta-analytic review. Biological Psychiatry, 57, 1336–1346. doi: 10.1016/j.biopsych.2005.02.006.PubMedCrossRefGoogle Scholar
  132. Wright, A. G. C., Krueger, R. F., Hobbs, M. J., Markon, K. E., Eaton, N. R., & Slade, T. (2013). The structure of psychopathology: toward an expanded quantitative empirical model. Journal of Abnormal Psychology, 122, 281–294. doi: 10.1037/a0030133.PubMedCrossRefGoogle Scholar
  133. Young, S. E., Friedman, N. P., Miyake, A., Willcutt, E. G., Corley, R. P., Haberstick, B. C., & Hewitt, J. K. (2009). Behavioral disinhibition: liability for externalizing spectrum disorders and its genetic and environmental relation to response inhibition across adolescence. Journal of Abnormal Psychology, 118, 117–130. doi: 10.1037/a0014657.PubMedPubMedCentralCrossRefGoogle Scholar
  134. Youngstrom, E., Loeber, R., & Stouthamer-Loeber, M. (2000). Patterns and correlates of agreement between parent, teacher, and male adolescent ratings of externalizing and internalizing problems. Journal of Consulting and Clinical Psychology, 68, 1038–1050. doi: 10.1037/0022-006x.68.6.1038.PubMedCrossRefGoogle Scholar
  135. Zahn-Waxler, C., Shirtcliff, E. A., & Marceau, K. (2008). Disorders of childhood and adolescence: Gender and psychopathology. Annual Review of Clinical Psychology, 4, 275–303.PubMedCrossRefGoogle Scholar

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© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Psychology, 130 Moore BuildingThe Pennsylvania State UniversityUniversity ParkUSA

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