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Part III - Experimental and Biological Approaches

Published online by Cambridge University Press:  23 March 2020

Edited by
Aidan G. C. Wright  and
Michael N. Hallquist
Aidan G. C. Wright
Affiliation:
University of Pittsburgh
Michael N. Hallquist
Affiliation:
Pennsylvania State University
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The Cambridge Handbook of Research Methods in Clinical Psychology , pp. 91 - 190
Publisher: Cambridge University Press
Print publication year: 2020

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References

References

Abramson, L. Y., & Seligman, M. E. P. (1977). Modeling Psychopathology in the Laboratory: History and Rationale. In Maser, J. P. & Seligman, M. E. P. (Eds.), Psychopathology: Experimental Models (pp. 1–26). San Francisco: Freeman.Google Scholar
Berner, E. S., & Graber, M. L. (2008). Overconfidence as a Cause of Diagnostic Error in Medicine. American Journal of Medicine, 121(Suppl. 5), s2‒s23.Google Scholar
Bickhard, M. H., & Campbell, D. T. (2000). Emergence. In Andersen, P. B., Emmeche, C., Finnemann, N. O., & Christiansen, P. V. (Eds.), Downward Causation: Minds, Body, Matter (pp. 322–348). Ǻrhus: Ǻrhus University Press.Google Scholar
Cohen, J. (1994). The Earth is Round (p < 0.05). American Psychologist, 49, 997–1003.Google Scholar
Forsyth, J. P., & Zvolensky, M. J. (2002). Experimental Psychopathology, Clinical Science, and Practice: An Irrelevant or Indispensable Alliance? Applied and Preventive Psychology: Current Scientific Perspectives, 10, 243–264.Google Scholar
Gottesman, I. I., & Gould, T. D. (2003). The Endophenotype Concept in Psychiatry: Etymology and Strategic Intentions. American Journal of Psychiatry, 160, 636–645.Google Scholar
Hoch, P. H. & Zubin, J. (Eds.) (1957). Experimental Psychopathology. New York: Grune & Stratton.Google Scholar
Imbens, G. W., & Rubin, D. B. (2010). Causal Inference in Statistics and the Medical and Social Sciences. New York: Cambridge University Press.Google Scholar
Kernberg, O. F. (1984). Severe Personality Disorders. New Haven, CT: Yale University Press.Google Scholar
Kimmel, H. D. (1971). Introduction. In Kimmel, H. D. (Ed.), Experimental Psychopathology: Recent Research and Theory (pp. 1–10). New York: Academic Press.Google Scholar
Kivelson, S., & Kivelson, S. A. (2016). Defining Emergence in Physics. NPJ Quantum Materials, 1, 1–2.Google Scholar
Korolenko, C. P., Volkov, P. P., Evceeva, T. A., & Shmatko, N. S. (1966). Experimental Psychopathology and Its Significance for the Clinical Study of Exogenous Psychoses. L' Evolution psychiatrique, 31, 777–785.Google Scholar
Kosslyn, S. M., & Rosenberg, R. S. (2005). The Brain and Your Students: How to Explain Why Neuroscience Is Relevant to Psychology. In Perlman, B., McCann, L. I., & Buskist, W. (Eds.), Voices of Experience: Memorable Talks from the National Institute on the Teaching of Psychology Volume One (pp. 71–82). Washington, DC: American Psychological Society.Google Scholar
Lenzenweger, M. F. (2004). Consideration of the Challenges, Complications, and Pitfalls of Taxometric Analysis. Journal of Abnormal Psychology, 113, 10–23.Google Scholar
Lenzenweger, M. F. (2010). Schizotypy and Schizophrenia: The View from Experimental Psychopathology. New York: Guilford Press.Google Scholar
Lenzenweger, M. F. (2013). Thinking Clearly about the Endophenotype vs. Intermediate Phenotype vs. Biomarker Distinctions in Developmental Psychopathology Research. Invited Essay for 25th Anniversary Issue. Development & Psychopathology, 25, 1347–1357.Google Scholar
Lenzenweger, M. F., & Depue, R. A. (2016). Toward a Developmental Psychopathology of Personality Disturbance: A Neurobehavioral Dimensional Model Incorporating Genetic, Environmental, and Epigenetic Factors (pp. 1079–1110). In Cicchetti, D. (Ed.), Developmental Psychopathology, Volume 3, Maladaptation and Psychopathology (3rd edn.). New York: Wiley.Google Scholar
Lenzenweger, M. F., & Hooley, J. M. (Eds.) (2003). Principles of Experimental Psychopathology: Essays in Honor of Brendan A. Maher. Washington, DC: American Psychological Association.Google Scholar
Lenzenweger, M. F., Jensen, S., & Rubin, D. B. (2003). Finding the “Genuine” Schizotype: A Model and Method for Resolving Heterogeneity in Performance on Laboratory Measures in Experimental Psychopathology Research. Journal of Abnormal Psychology, 112, 457–468.Google Scholar
Maher, B. A. (1966). Principles of Psychopathology: An Experimental Approach. Oxford: McGraw-Hill.Google Scholar
Maher, B. A. (2003). Psychopathology and Delusions: Reflections on Methods and Models. In Lenzenweger, M. F. & Hooley, J. M. (Eds.), Principles of Experimental Psychopathology: Essays in Honor of Brendan A. Maher (pp. 9–28). Washington, DC: American Psychological Association.Google Scholar
Meehl, P. E. (1967). (1967). Theory-Testing in Psychology and Physics: A Methodological Paradox Philosophy of Science, 34, 103–115.Google Scholar
Meehl, P. E. (1972). Specific Genetic Etiology, Psychodynamics and Therapeutic Nihilism. International Journal of Mental Health, 1, 10–27.Google Scholar
Meehl, P. E. (1977). Specific Etiology and Other Forms of Strong Influence: Some Quantitative Meanings. Journal of Medicine and Philosophy, 2, 33–53.Google Scholar
Meehl, P. E. (1978). Theoretical Risks and Tabular Asterisks: Sir Karl, Sir Ronald, and the Slow Progress of Soft Psychology. Journal of Consulting and Clinical Psychology, 46, 806–834.Google Scholar
Meehl, P. E. (1986). Diagnostic Taxa as Open Concepts: Metatheoretical and Statistical Questions about Reliability and Construct Validity in the Grand Strategy of Nosological Revision. In Millon, T. & Klerman, G. L. (Eds.), In Contemporary Directions in Psychopathology (pp. 215–231). New York: Guilford.Google Scholar
Meehl, P. E. (1990). Toward an Integrated Theory of Schizotaxia, Schizotypy, and Schizophrenia. Journal of Personality Disorders, 4, 1–99.Google Scholar
Meehl, P. E. (1998/2006). The Power of Quantitative Thinking. In Waller, N. G., Yonce, L. J., Grove, W. M., Faust, D., & Lenzenweger, M. F. (Eds.). A Paul Meehl Reader: Essays on the Practice of Scientific Psychology (pp. 433–444). Mahwah, NJ: Erlbaum.Google Scholar
Meehl, P. E., & Sellars, W. (1956). The Concept of Emergence. In Feigl, H. & Scriven, M. (Eds.), Minnesota Studies in the Philosophy of Science: Vol. I. The Foundations of Science and the Concepts of Psychology and Psychoanalysis (pp. 239–252). Minneapolis: University of Minnesota Press.Google Scholar
Pearl, J. (2009). Causality: Models, Reasoning and Inference (2nd edn.). New York: Cambridge University Press.Google Scholar
Reichenbach, H. (1938). Experience and Prediction. Chicago, IL: University of Chicago Press.Google Scholar
Reichenbach, H. (1956). The Rise of Scientific Discovery. Berkeley, CA: University of California Press.Google Scholar
Rumelhart, D. E. (1984). The Emergence of Cognitive Phenomena from Sub-Symbolic Processes. In Proceedings of the Sixth Annual Conference of the Cognitive Science Society Colorado, 1984. Hillsdale, NJ: Erlbaum.Google Scholar
Shields, J., & Gottesman, I. I. (1973). Genetic Studies of Schizophrenia as Signposts to Biochemistry. In Iversen, L. L. & Rose, S. P. R. (Eds.), Biochemistry and Mental Illness (pp. 165–174). London: Biochemical Society.Google Scholar
Silverstein, S. M. (2008). Measuring Specific, rather than Generalized, Cognitive Deficits and Maximizing Between-Group Effect Size in Studies of Cognition and Cognitive Change. Schizophrenia Bulletin, 34(4), 645–655.Google Scholar
Wagenmakers, E.-J., Morey, R. D., & Lee, M. D. (2016). Bayesian Benefits for the Pragmatic Researcher. Current Directions in Psychological Science, 25, 169–176.Google Scholar
Waller, N. G. (2008). Commingled Samples: A Neglected Source of Bias in Reliability Analysis. Applied Psychological Measurement, 32, 211–223.Google Scholar
Waller, N. G., Yonce, L. J., Grove, W. M., Faust, D. A., & Lenzenweger, M. F. (2006). A Paul Meehl Reader: Essays on the Practice of Scientific Psychology. Mahwah, NJ: Lawrence Erlbaum.Google Scholar
Weisberg, D. S., Keil, F. C., Goodstein, J., Rawson, E., & Gray, J. R. (2008). The Seductive Allure of Neuroscience Explanations. Journal of Cognitive Neuroscience, 20(3), 470–477.CrossRefGoogle ScholarPubMed
Wilkinson, L., & The Task Force on Statistical Inference. (1999). Statistical Methods in Psychology Journals: Guidelines and Explanations. American Psychologist, 54, 594–604.Google Scholar
Zachar, P., Krueger, R. F., & Kendler, K. S. (2016). Personality Disorder in DSM-5: An Oral History. Psychological Medicine, 46, 1–10.Google Scholar
Zvolensky, M. J., Forsyth, J. P., & Johnson, K. (2013). Laboratory Methods in Experimental Psychopathology. In Comer, J. S. & Kendall, P. C. (Eds.), Oxford Library of Psychology: The Oxford Handbook of Research Strategies for Clinical Psychology (pp. 7–23). New York: Oxford University Press.Google Scholar

References

Abikoff, H., Courtney, M., Pelham, W. E. Jr., & Koplewicz, H. S. (1993). Teachers’ Ratings of Disruptive Behaviors: The Influence of Halo Effects. Journal of Abnormal Child Psychology, 21(5), 519–533.CrossRefGoogle ScholarPubMed
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(2), 213–232.Google Scholar
Achenbach, T. M., Krukowski, R. A., Dumenci, L., & Ivanova, M. Y. (2005). Assessment of Adult Psychopathology: Meta-Analyses and Implications of Cross-Informant Correlations. Psychological Bulletin, 131(3), 361–382.CrossRefGoogle ScholarPubMed
Barkley, R. A. (1997). Behavioral Inhibition, Sustained Attention, and Executive Functions: Constructing a Unifying Theory of ADHD. Psychological Bulletin, 121(1), 65–94.Google Scholar
Baron-Cohen, S., Leslie, A. M., & Frith, U. (1986). Mechanical, Behavioral, and Intentional Understanding of Picture Stories in Autistic Children. British Journal of Developmental Psychology, 4, 113–125.Google Scholar
Basco, M. R., Bostic, J. Q., Davies, D., Rush, A. J., Witte, B., Hendrickse, W., & Barnett, V. (2000). Methods to Improve Diagnostic Accuracy in a Community Mental Health Setting. American Journal of Psychiatry, 157(10), 1599–1605.Google Scholar
Bentall, R. P. (1996). At the Centre of a Science of Psychopathology? Characteristics and Limitations of Cognitive Research. Cognitive Neuropsychiatry, 1(4), 265–273.Google Scholar
Berkson, J. (1946). Limitations of the Application of Fourfold Table Analysis to Hospital Data. Biometrics Bulletin, 2(3), 47–53.Google Scholar
Bilder, R. M., Howe, A., Novak, N., Sabb, F. W., & Parker, D. S. (2011). The Genetics of Cognitive Impairment in Schizophrenia: A Phenomic Perspective. Trends in Cognitive Sciences, 15(9), 428–435.Google Scholar
Bird, H. R., Gould, M. S., & Staghezza, B. (1992). Aggregating Data from Multiple Informants in Child Psychiatry Epidemiologic Research. Journal of the American Academy of Child and Adolescent Psychiatry, 31(1), 78–85.CrossRefGoogle Scholar
Boonstra, A. M., Oosterlaan, J., Sergeant, J. A., & Buitelaar, J. K. (2005). Executive Functioning in Adult ADHD: A Meta-Analytic Review. Psychological Medicine, 35(8), 1097–1108.Google Scholar
Brown, S. D., & Heathcote, A. (2008). The Simplest Complete Model of Choice Response Time: Linear Ballistic Accumulation. Cognitive Psychology, 57(3), 153–178.Google Scholar
Brunshaw, J. M., & Szatmari, P. (1988). The Agreement between Behaviour Checklists and Structured Psychiatric Interviews for Children. Canadian Journal of Psychiatry, 33(6), 474–481.Google Scholar
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(2), 119–137.Google Scholar
Clay, C., Ellis, M. A., Amodeo, M., Fassler, I., & Griffin, M. L. (2003). Recruiting a Community Sample of African American Subjects: The Nuts and Bolts of a Successful Effort. Families in Society – The Journal of Contemporary Human Services, 84(3), 396–404.Google Scholar
Cohen, P., & Cohen, J. (1984). The Clinician’s Illusion. Archives of General Psychiatry, 41(12), 1178–1182.Google Scholar
Cohen-Gilbert, J. E., Killgore, W. D. S., White, C. N., Schwab, Z. J., Crowley, D. J., Covell, M. J., … Silveri, M. M. (2014). Differential Influence of Safe versus Threatening Facial Expressions on Decision-Making during an Inhibitory Control Task in Adolescence and Adulthood. Developmental Science, 17(2), 212–223.Google Scholar
Conners, C. K., Erhardt, D., & Sparrow, E. P. (1999). Conners’ Adult ADHD Rating Scales (CAARS): Technical Manual. Toronto: Multi-Health Systems.Google Scholar
Conway, A., Kane, M. J., Bunting, M. F., Hambrick, D. Z., Wilhelm, O., & Engle, R. W. (2005). Working Memory Span Tasks: A Methodological Review and User’s Guide. Psychonomic Bulletin & Review, 12(5), 769–786.Google Scholar
Corbie-Smith, G., Thomas, S. B., & St. George, D. M. M. (2002). Distrust, Race, and Research. Archives of Internal Medicine, 162(21), 2458–2463.Google Scholar
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(8), 837–844.Google Scholar
Dantas, O. M., Ximenes, R. A., de Albuquerque, M. d. F. P., Montarroyos, U. R., de Souza, W. V., Varejão, P., & Rodrigues, L. C. (2007). Selection Bias: Neighbourhood Controls and Controls Selected from Those Presenting to a Health Unit in a Case Control Study of Efficacy of BCG Revaccination. BMC Medical Research Methodology, 7(1), 11.Google Scholar
Dawson, M. R. W. (1988). Fitting the Ex-Gaussian Equation to Reaction Time Distributions. Behavior Research Methods Instruments & Computers, 20(1), 54–57.Google Scholar
De Los Reyes, A., Thomas, S. A., Goodman, K. L., & Kundey, S. M. A. (2013). Principles Underlying the Use of Multiple Informants’ Reports. Annual Review of Clinical Psychology, 9(9), 123–149.Google Scholar
De Los Reyes, A., Augenstein, T. M., Wang, M., Thomas, S. A., Drabick, D. A. G., Burgers, D. E., & Rabinowitz, J. (2015). The Validity of the Multi-Informant Approach to Assessing Child and Adolescent Mental Health. Psychological Bulletin, 141(4), 858–900.Google Scholar
Deary, I. J., Strand, S., Smith, P., & Fernandes, C. (2007). Intelligence and Educational Achievement. Intelligence, 35(1), 13–21.Google Scholar
Doyle, A., Ostrander, R., Skare, S., Crosby, R. D., & August, G. J. (1997). Convergent and Criterion-Related Validity of the Behavior Assessment System for Children-Parent Rating Scale. Journal of Clinical Child Psychology, 26(3), 276–284.Google Scholar
Dudeney, J., Sharpe, L., & Hunt, C. (2015). Attentional Bias towards Threatening Stimuli in Children with Anxiety: A Meta-Analysis. Clinical Psychology Review, 40, 66–75.Google Scholar
Engle, R. W., Tuholski, S. W., Laughlin, J. E., & Conway, A. (1999). Working Memory, Short-Term Memory, and General Fluid Intelligence: A Latent-Variable Approach. Journal of Experimental Psychology-General, 128(3), 309–331.Google Scholar
Epstein, J. N., Langberg, J. M., Rosen, P. J., Graham, A., Narad, M. E., Antonini, T. N., … Altaye, M. (2011). Evidence for Higher Reaction Time Variability for Children with ADHD on a Range of Cognitive Tasks including Reward and Event Rate Manipulations. Neuropsychology, 25(4), 427–441.Google Scholar
Farmer, D. F., Jackson, S. A., Camacho, F., & Hall, M. A. (2007). Attitudes of African American and Low Socioeconomic Status White Women toward Medical Research. Journal of Health Care for the Poor and Underserved, 18(1), 85–99.Google Scholar
Fombonne, E. (2009). Epidemiology of Pervasive Developmental Disorders. Pediatric Research, 65(6), 591–598.Google Scholar
Garnier-Villarreal, M., Rhemtulla, M., & Little, T. D. (2014). Two-Method Planned Missing Designs for Longitudinal Research. International Journal of Behavioral Development, 38(5), 411–422.Google Scholar
Geary, D. C. (2011). Cognitive Predictors of Achievement Growth in Mathematics: A 5-Year Longitudinal Study. Developmental Psychology, 47(6), 1539–1552.Google Scholar
Gibbons, R. D., Weiss, D. J., Frank, E., & Kupfer, D. (2016). Computerized Adaptive Diagnosis and Testing of Mental Health Disorders. Annual Review of Clinical Psychology, 12(1), 83–104.Google Scholar
Gilmore, R. O., & Adolph, K. E. (2017). Video Can Make Science More Open, Transparent, Robust, and Reproducible. Retrieved from https://psyarxiv.com/tcfqf/Google Scholar
Gilmore, R. O., Diaz, M. T., Wyble, B. A., & Yarkoni, T. (2017). Progress toward Openness, Transparency, and Reproducibility in Cognitive Neuroscience. Annals of the New York Academy of Sciences, 1396(1), 5–18.Google Scholar
Golden, C. J., Fishburne, F. J., Lewis, G. P., Conley, F. K., Moses, J. A., Engum, E., … Graber, B. (1981). Cross-Validation of the Luria-Nebraska Neuropsychological Battery for the Presence, Lateralization, and Localization of Brain-Damage. Journal of Consulting and Clinical Psychology, 49(4), 491–507.Google Scholar
Goodman, S. H., Lahey, B. B., Fielding, B., Dulcan, M., Narrow, W., & Regier, D. (1997). Representativeness of Clinical Samples of Youths with Mental Disorders: A Preliminary Population-Based Study. Journal of Abnormal Psychology, 106(1), 3–14.Google Scholar
Gottesman, I. I., & Gould, T. D. (2003). The Endophenotype Concept in Psychiatry: Etymology and Strategic Intentions. American Journal of Psychiatry, 160(4), 636–645.Google Scholar
Graham, J. W. (2012). Missing Data: Analysis and Design. New York: Springer.Google Scholar
Graham, J. W., Taylor, B. J., Olchowski, A. E., & Cumsille, P. E. (2006). Planned Missing Data Designs in Psychological Research. Psychological Methods, 11(4), 323–343.Google Scholar
Grimes, D. A., & Schulz, K. F. (2005). Compared to What? Finding Controls for Case-Control Studies. Lancet, 365(9468), 1429–1433.Google Scholar
Grobbee, D., & Hoes, A. (2015). Clinical Epidemiology: Principles, Methods, and Applications for Clinical Research. Burlington, MA: Jones and Bartlett Learning, LLC.Google Scholar
Halperin, J. M. (2016). Executive Functioning – A Key Construct for Understanding Developmental Psychopathology or a “Catch-All” Term in Need of Some Rethinking? Journal of Child Psychology and Psychiatry, 57(4), 443–445.CrossRefGoogle ScholarPubMed
Halperin, J. M., Wolf, L., Greenblatt, E. R., & Young, G. (1991). Subtype Analysis of Commission Errors on the Continuous Performance Test in Children. Developmental Neuropsychology, 7(2), 207–217.Google Scholar
Hart, E. L., Lahey, B. B., Loeber, R., & Hanson, K. S. (1994). Criterion Validity of Informants in the Diagnosis of Disruptive Behavior Disorders in Children: A Preliminary Study. Journal of Consulting and Clinical Psychology, 62(2), 410–414.Google Scholar
Hartung, C. M., Van Pelt, J. C., Armendariz, M. L., & Knight, L. A. (2006). Biases in Ratings of Disruptive Behavior in Children: Effects of Sex and Negative Halos. Journal of Attention Disorders, 9(4), 620–630.Google Scholar
Haywood, H. C., & Raffard, S. (2017). Cognition and Psychopathology: Overview. Journal of Cognitive Education and Psychology, 16(1), 3–8.Google Scholar
Hill, E. L. (2004). Executive Dysfunction in Autism. Trends in Cognitive Sciences, 8(1), 26–32.Google Scholar
Hinshaw, S. P., & Nigg, J. T. (1999). Behavior Rating Scales in the Assessment of Disruptive Behavior Problems in childhood. In Shaffer, D., Lucas, C. P., & Richters, J. E. (Eds.), Diagnostic Assessment in Child and Adolescent Psychopathology (pp. 91–126). New York: Guilford Press.Google Scholar
Hohmann, A. A., & Parron, D. L. (1996). How the New NIH Guidelines on Inclusion of Women and Minorities Apply: Efficacy Trials, Effectiveness Trials, and Validity. Journal of Consulting and Clinical Psychology, 64(5), 851–855.Google Scholar
Huang-Pollock, C., & Nigg, J. T. (2003). Searching for the Attention Deficit in Attention Deficit Hyperactivity Disorder: The Case of Visuospatial Orienting. Clinical Psychology Review, 23(6), 801–830.Google Scholar
Huang-Pollock, C., Nigg, J. T., & Halperin, J. M. (2006). Single Dissociation Findings of ADHD Deficits in Vigilance but not Anterior or Posterior Attention Systems. Neuropsychology, 20(4), 420–429.Google Scholar
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(2), 360–371.Google Scholar
Huang-Pollock, C., Ratcliff, R., McKoon, G., Shapiro, Z., Weigard, A., & Galloway-Long, H. (2017a). Using the Diffusion Model to Explain Cognitive Deficits in Attention Deficit Hyperactivity Disorder. Journal of Abnormal Child Psychology, 45(1), 57–68.Google Scholar
Huang-Pollock, C., Shapiro, Z., Galloway-Long, H., & Weigard, A. (2017b). Is Poor Working Memory a Transdiagnostic Risk Factor for Psychopathology? Journal of Abnormal Child Psychology, 45(8), 1477–1490.Google Scholar
Hunsley, J., & Meyer, G. J. (2003). The Incremental Validity of Psychological Testing and Assessment: Conceptual, Methodological, and Statistical Issues. Psychological Assessment, 15(4), 446–455.Google Scholar
Ibrahim, M. A., & Spitzer, W. O. (1979). Case Control Study: Problem and the Prospect. Journal of Chronic Diseases, 32(1‒2), 139–144.Google Scholar
Jacoby, L. L. (1991). A Process Dissociation Framework – Separating Automatic from Intentional Uses of Memory. Journal of Memory and Language, 30(5), 513–541.Google Scholar
Jensen-Doss, A., & Hawley, K. M. (2010). Understanding Barriers to Evidence-Based Assessment: Clinician Attitudes toward Standardized Assessment Tools. Journal of Clinical Child and Adolescent Psychology, 39(6), 885–896.Google Scholar
Jensen, P. S., Rubio-Stipec, M., Canino, G., Bird, H. R., Dulcan, M. K., Schwab-Stone, M. E., & Lahey, B. B. (1999). Parent and Child Contributions to Diagnosis of Mental Disorder: Are Both Informants Always Necessary? Journal of the American Academy of Child and Adolescent Psychiatry, 38(12), 1569–1579.Google Scholar
Johnston, C., & Murray, C. (2003). Incremental Validity in the Psychological Assessment of Children and Adolescents. Psychological Assessment, 15(4), 496–507.Google Scholar
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(5), 837–850.Google Scholar
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(6), 684–694.CrossRefGoogle ScholarPubMed
Karalunas, S. L., Geurts, H. M., Konrad, K., Bender, S., & Nigg, J. T. (2014). Annual Research Review: Reaction Time Variability in ADHD and Autism Spectrum Disorders: Measurement and Mechanisms of a Proposed Trans-Diagnostic Phenotype. Journal of Child Psychology and Psychiatry, 55(6), 685–710.Google Scholar
Klein, D. N., Dougherty, L. R., & Olino, T. M. (2005). Toward Guidelines for Evidence-Based Assessment of Depression in Children and Adolescents. Journal of Clinical Child & Adolescent Psychology, 34(3), 412–432.Google Scholar
Klin, A. (2000). Attributing Social Meaning to Ambiguous Visual Stimuli in Higher-Functioning Autism and Asperger Syndrome: The Social Attribution Task. Journal of Child Psychology and Psychiatry and Allied Disciplines, 41(7), 831–846.Google Scholar
Kopec, J. A., & Esdaile, J. M. (1990). Bias in Case Control Studies, A Review. Journal of Epidemiology and Community Health, 44(3), 179–186.Google Scholar
Kozak, M. J., & Cuthbert, B. N. (2016). The NIMH Research Domain Criteria Initiative: Background, Issues, and Pragmatics. Psychophysiology, 53(3), 286–297.Google Scholar
Kraemer, H. C., Measelle, J. R., Ablow, J. C., Essex, M. J., Boyce, W. T., & Kupfer, D. J. (2003). A New Approach to Integrating Data from Multiple Informants in Psychiatric Assessment and Research: Mixing and Matching Contexts and Perspectives. American Journal of Psychiatry, 160(9), 1566–1577.Google Scholar
Lacouture, Y., & Cousineau, D. (2008). How to Use MATLAB to Fit the Ex-Gaussian and Other Probability Functions to a Distribution of Response Times. Tutorials in Quantitative Methods for Psychology, 4(1), 35–45.Google Scholar
Lahey, B. B., Applegate, B., Barkley, R. A., Garfinkel, B., McBurnett, K., Kerdyk, L., … Shaffer, D. (1994a). DSM-IV Field Trials for Oppositional Defiant Disorder and Conduct Disorder in Children and Adolescents. American Journal of Psychiatry, 151(8), 1163–1171.Google Scholar
Lahey, B. B., Applegate, B., McBurnett, K., Biederman, J., Greenhill, L., Hynd, G. W., … Shaffer, D. (1994b). DSM-IV Field Trials for Attention-Deficit Hyperactivity Disorder in Children and Adolescents. American Journal of Psychiatry, 151(11), 1673–1685.Google Scholar
Levin-Aspenson, H. F., & Watson, D. (2018). Mode of Administration Effects in Psychopathology Assessment: Analyses of Gender, Age, and Education Differences in Self-Rated versus Interview-Based Depression. Psychological Assessment, 30(3), 287–295.Google Scholar
Lijffijt, M., Kenemans, J. L., Verbaten, M. N., & van Engeland, H. (2005). A Meta-Analytic Review of Stopping Performance in Attention-Deficit/Hyperactivity Disorder: Deficient Inhibitory Motor Control? Journal of Abnormal Psychology, 114(2), 216–222.Google Scholar
Little, T. D., & Rhemtulla, M. (2013). Planned Missing Data Designs for Developmental Researchers. Child Development Perspectives, 7(4), 199–204.Google Scholar
Logan, G. D., Van Zandt, T., Verbruggen, F., & Wagenmakers, E.-J. (2014). On the Ability to Inhibit Thought and Action: General and Special Theories of an Act of Control. Psychological Review, 121(1), 66–95.Google Scholar
Longwell, B. T., & Truax, P. (2005). The Differential Effects of Weekly, Monthly, and Bimonthly Administrations of the Beck Depression Inventory-II: Psychometric Properties and Clinical Implications. Behavior Therapy, 36(3), 265–275.Google Scholar
Lumsden, J., Edwards, E. A., Lawrence, N. S., Coyle, D., & Munafo, M. R. (2016). Gamification of Cognitive Assessment and Cognitive Training: A Systematic Review of Applications and Efficacy. Journal of Medical Internet Research Serious Games, 4(2), 14.Google Scholar
Lund, E. (1989). The Validity of Different Control Groups in a Case-Control Study: Oral Contraceptive Use and Breast Cancer in Young Women. Journal of Clinical Epidemiology, 42(10), 987–993.Google Scholar
Ma, X. M., Buffler, P. A., Layefsky, M., Does, M. B., & Reynolds, P. (2004). Control Selection Strategies in Case-Control Studies of Childhood Diseases. American Journal of Epidemiology, 159(10), 915–921.Google Scholar
Magnússon, P., Smári, J., Sigurðardóttir, D., Baldursson, G., Sigmundsson, J., Kristjánsson, K., … Guðmundsson, Ó. Ó. (2006). Validity of Self-Report and Informant Rating Scales of Adult ADHD Symptoms in Comparison with a Semistructured Diagnostic Interview. Journal of Attention Disorders, 9(3), 494–503.Google Scholar
Martel, M. M., Schimmack, U., Nikolas, M., & Nigg, J. T. (2015). Integration of Symptom Ratings from Multiple Informants in ADHD Diagnosis: A Psychometric Model with Clinical Utility. Psychological Assessment, 27(3), 1060–1071.Google Scholar
Martin, A., Rief, W., Klaiberg, A., & Braehler, E. (2006). Validity of the Brief Patient Health Questionnaire Mood Scale (PHQ-9) in the General Population. General Hospital Psychiatry, 28(1), 71–77.Google Scholar
Martin, R. P., Hooper, S., & Snow, J. (1986). Behavior Rating Scale Approaches to Personality Assessment in Children and Adolescents. In Knoff, H. M. (Ed.), The Assessment of Child and Adolescent Personality (pp. 309–348). New York: Guilford Press.Google Scholar
Masten, A. S., Hubbard, J. J., Gest, S. D., Tellegen, A., Garmezy, N., & Ramirez, M. (1999). Competence in the Context of Adversity: Pathways to Resilience and Maladaptation from Childhood to Late Adolescence. Development and Psychopathology, 11(1), 143–169.Google Scholar
Matzke, D., Love, J., Wiecki, T. V., Brown, S. D., Logan, G. D., & Wagenmakers, E. J. (2013). Release the BEESTS: Bayesian Estimation of Ex-Gaussian Stop Signal Reaction Time Distributions. Frontiers in Psychology, 4, 918.Google Scholar
McTeague, L. M., Huemer, J., Carreon, D. M., Jiang, Y., Eickhoff, S. B., & Etkin, A. (2017). Identification of Common Neural Circuit Disruptions in Cognitive Control across Psychiatric Disorders. American Journal of Psychiatry, 174(7), 676–685.Google Scholar
Moffitt, T. E. (1993). Adolescence-Limited and Life-Course Persistent Antisocial Behavior: A Developmental Taxonomy. Psychological Review, 100(4), 674–701.Google Scholar
Mulder, M. J., Bos, D., Weusten, J. M. H., van Belle, J., van Dijk, S. C., Simen, P., … Durston, S. (2010). Basic Impairments in Regulating the Speed-Accuracy Tradeoff Predict Symptoms of Attention-Deficit/Hyperactivity Disorder. Biological Psychiatry, 68(12), 1114–1119.Google Scholar
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(2), 119–125.Google Scholar
Nigg, J. T., Blaskey, L. G., Stawicki, J. A., & Sachek, J. (2004). Evaluating the Endophenotype Model of ADHD Neuropsychological Deficit: Results for Parents and Siblings of Children with ADHD Combined and Inattentive Subtypes. Journal of Abnormal Psychology, 113(4), 614–625.Google Scholar
Nigg, J. T., Willcutt, E. G., Doyle, A., & Sonuga-Barke, E. J. S. (2005). Causal Heterogeneity in Attention-Deficit/Hyperactivity Disorder: Do We Need Neuropsychologically Impaired Subtypes? Biological Psychiatry, 57(11), 1224–1230.Google Scholar
Nigg, J. T., Jester, J. M., Stavro, G. M., Ip, K. I., Puttler, L. I., & Zucker, R. A. (2017). Specificity of Executive Functioning and Processing Speed Problems in Common Psychopathology. Neuropsychology, 31(4), 448–466.Google Scholar
Nigg, J. T., Gustafsson, H. C., Karalunas, S. L., Ryabinin, P., McWeeney, S. K., Faraone, S. V., … Wilmot, B. (2018). Working Memory and Vigilance as Multivariate Endophenotypes Related to Common Genetic Risk for Attention-Deficit/Hyperactivity Disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 57(3), 175–182.Google Scholar
Nolen-Hoeksema, S., & Watkins, E. R. (2011). A Heuristic for Developing Transdiagnostic Models of Psychopathology: Explaining Multifinality and Divergent Trajectories. Perspectives on Psychological Science, 6(6), 589–609.Google Scholar
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(3), 411–425.Google Scholar
Pauli-Pott, U., & Becker, K. (2011). Neuropsychological Basic Deficits in Preschoolers at Risk for ADHD: A Meta-Analysis. Clinical Psychology Review, 31(4), 626–637.Google Scholar
Pelham, W. E., Jr., Fabiano, G. A., & Massetti, G. M. (2005). Evidence-Based Assessment of Attention Deficit Hyperactivity Disorder in Children and Adolescents. Journal of Clinical Child & Adolescent Psychology, 34(3), 449–476.Google Scholar
Pennington, B. F., & Ozonoff, S. (1996). Executive Functions and Developmental Psychopathology. Journal of Child Psychology and Psychiatry, 37(1), 51–87.Google Scholar
Polanczyk, G. V., Salum, G. A., Sugaya, L. S., Caye, A., & Rohde, L. A. (2015). Annual Research Review: A Meta-Analysis of the Worldwide Prevalence of Mental Disorders in Children and Adolescents. Journal of Child Psychology and Psychiatry, 56(3), 345–365.Google Scholar
Quraishi, S., & Frangou, S. (2002). Neuropsychology of Bipolar Disorder: A Review. Journal of Affective Disorders, 72(3), 209–226.Google Scholar
Ratcliff, R., & Frank, M. J. (2012). Reinforcement-Based Decision Making in Corticostriatal Circuits: Mutual Constraints by Neurocomputational and Diffusion Models. Neural Computation, 24(5), 1186–1229.Google Scholar
Ratcliff, R., & McKoon, G. (2008). The Diffusion Decision Model: Theory and Data for Two-Choice Decision Tasks. Neural Computation, 20(4), 873–922.CrossRefGoogle ScholarPubMed
Ratcliff, R., Thapar, A., & McKoon, G. (2001). The Effects of Aging on Reaction Time in a Signal Detection Task. Psychology and Aging, 16(2), 323–341.Google Scholar
Ratcliff, R., Thapar, A., & McKoon, G. (2006). Aging, Practice, and Perceptual Tasks: A Diffusion Model Analysis. Psychology and Aging, 21(2), 353–371.Google Scholar
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(1), 367–381.Google Scholar
Reynolds, W. M., & Kobak, K. A. (1995). Reliability and Validity of the Hamilton Depression Inventory: A Paper-and-Pencil Version of the Hamilton Depression Rating Scale Clinical Interview. Psychological Assessment, 7(4), 472–483.Google Scholar
Rhemtulla, M., & Little, T. (2012). Tools of the Trade: Planned Missing Data Designs for Research in Cognitive Development. Journal of Cognition and Development: Official Journal of the Cognitive Development Society, 13(4), 425–438.Google Scholar
Richman, W. L., Kiesler, S., Weisb, S., & Drasgow, F. (1999). A Meta-Analytic Study of Social Desirability Distortion in Computer-Administered Questionnaires, Traditional Questionnaires, and Interviews. Journal of Applied Psychology, 84(5), 754–775.Google Scholar
Rothman, K. (1986). Modern Epidemiology. Boston: Little, Brown, and Company.Google Scholar
Salum, G. A., Sergeant, J., Sonuga-Barke, E., Vandekerckhove, J., Gadelha, A., Pan, P. M., … Rohde, L. A. P. (2014). Specificity of Basic Information Processing and Inhibitory Control in Attention Deficit Hyperactivity Disorder. Psychological Medicine, 44(3), 617–631.Google Scholar
Sanislow, C. A., Pine, D. S., Quinn, K. J., Kozak, M. J., Garvey, M. A., Heinssen, R. K., … Cuthbert, B. N. (2010). Developing Constructs for Psychopathology Research: Research Domain Criteria. Journal of Abnormal Psychology, 119(4), 631–639.Google Scholar
Schachar, R., Sandberg, S., & Rutter, M. (1986). Agreement between Teachers’ Ratings and Observations of Hyperactivity, Inattentiveness, and Defiance. Journal of Abnormal Child Psychology, 14(2), 331–345.Google Scholar
Schmidt, F. (2010). Detecting and Correcting the Lies that Data Tell. Perspectives on Psychological Science, 5(3), 233–242.Google Scholar
Schwartz, K., & Verhaeghen, P. (2008). ADHD and Stroop Interference from Age 9 to Age 41 Years: A Meta-Analysis of Developmental Effects. Psychological Medicine, 38(11), 1607–1616.Google Scholar
Schwarz, N. (1999). Self-Reports: How the Questions Shape the Answers. American Psychologist, 54(2), 93–105.Google Scholar
Seidman, L. J., Biederman, J., Monuteaux, M. C., Weber, W., & Faraone, S. V. (2000). Neuropsychological Functioning in Nonreferred Siblings of Children with Attention Deficit/Hyperactivity Disorder. Journal of Abnormal Psychology, 109(2), 252–265.Google Scholar
Shapiro, Z., Huang-Pollock, C. L., Graham, J., & Neely, K. (in press). Making the Most of It: Application of Planned Missingness Design to Increase the Efficiency of Diagnostic Assessment.Google Scholar
Shemmassian, S. K., & Lee, S. S. (2015). Predictive Utility of Four Methods of Incorporating Parent and Teacher Symptom Ratings of ADHD for Longitudinal Outcomes. Journal of Clinical Child & Adolescent Psychology, 45(2), 1–12.Google Scholar
Silverman, W. K., & Ollendick, T. H. (2005). Evidence-Based Assessment of Anxiety and Its Disorders in Children and Adolescents. Journal of Clinical Child and Adolescent Psychology, 34(3), 380–411.Google Scholar
Smith, P. L. (2016). Diffusion Theory of Decision Making in Continuous Report. Psychological Review, 123(4), 425–451.Google Scholar
Stavraky, K. M., & Clarke, E. A. (1983). Hospital or Population Controls: An Unanswered Question. Journal of Chronic Diseases, 36(4), 301–307.Google Scholar
UyBico, S. J., Pavel, S., & Gross, C. P. (2007). Recruiting Vulnerable Populations into Research: A Systematic Review of Recruitment Interventions. Journal of General Internal Medicine, 22(6), 852–863.Google Scholar
Vaughn, A. J., & Hoza, B. (2013). The Incremental Utility of Behavioral Rating Scales and a Structured Diagnostic Interview in the Assessment of Attention-Deficit/Hyperactivity Disorder. Journal of Emotional and Behavioral Disorders, 21(4), 227–239.Google Scholar
Vega, A., & Parsons, O. A. (1967). Cross-Validation of Halstead-Reitan Tests for Brain Damage. Journal of Consulting Psychology, 31(6), 619–625.Google Scholar
Verbruggen, F., McLaren, I. P. L., & Chambers, C. D. (2014). Banishing the Control Homunculi in Studies of Action Control and Behavior Change. Perspectives on Psychological Science, 9(5), 497–524.Google Scholar
Voss, A., Nagler, M., & Lerche, V. (2013). Diffusion Models in Experimental Psychology: A Practical Introduction. Experimental Psychology, 60(6), 385–402.Google Scholar
Wacholder, S., Silverman, D. T., McLaughlin, J. K., & Mandel, J. S. (1992a). Selection of Controls in case-Control Studies 2: Types of Controls. American Journal of Epidemiology, 135(9), 1029–1041.Google Scholar
Wacholder, S., Silverman, D. T., McLaughlin, J. K., & Mandel, J. S. (1992b). Selection of Controls in Case-Control Studies 3: Design Options. American Journal of Epidemiology, 135(9), 1042–1050.Google Scholar
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(12), 1336–1344.Google Scholar
Weigard, A., & Huang-Pollock, C. (2017). The Role of Speed in ADHD-Related Working Memory Deficits. Clinical Psychological Science, 5(2), 195–211.Google Scholar
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(4), 502–515.Google Scholar
Weigard, A., Huang-Pollock, C., Brown, S., & Heathcote, A. (2018). Testing Formal Predictions of Neuroscientific Theories of ADHD with a Cognitive Model-Based Approach. Journal of Abnormal Psychology, 127(5), 529–539.Google Scholar
White, C. N., Ratcliff, R., & Starns, J. J. (2011). Diffusion Models of the Flanker Task: Discrete versus Gradual Attentional Selection. Cognitive Psychology, 63(4), 210–238.Google Scholar
White, C. N., Ratcliff, R., Vasey, M. W., & McKoon, G. (2010). Anxiety Enhances Threat Processing without Competition among Multiple Inputs: A Diffusion Model Analysis. Emotion, 10(5), 662–677.Google Scholar
White, C. N., Skokin, K., Carlos, B., & Weaver, A. (2015). Using Decision Models to Decompose Anxiety-Related Bias in Threat Classification. Emotion, 16(2), 196–207.Google Scholar
White, L. K., Moore, T. M., Calkins, M. E., Wolf, D. H., Satterthwaite, T. D., Leibenluft, E., … Gur, R. E. (2017). An Evaluation of the Specificity of Executive Function Impairment in Developmental Psychopathology. Journal of the American Academy of Child & Adolescent Psychiatry, 56(11), 975–982.Google Scholar
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(11), 1336–1346.Google Scholar
Yeargin-Allsopp, M., Rice, C., Karapurkar, T., Doernberg, N., Boyle, C., & Murphy, C. (2003). Prevalence of Autism in a US Metropolitan Area. Journal of the American Medical Association, 289(1), 49–55.Google Scholar
Yirmiya, N., Erel, O., Shaked, M., & Solomonica-Levi, D. (1998). Meta-Analyses Comparing Theory of Mind Abilities of Individuals with Autism, Individuals with Mental Retardation, and Normally Developing Individuals. Psychological Bulletin, 124(3), 283–307.Google Scholar
Zawadzki, M. J., Graham, J. W., & Gerin, W. (2012). Increasing the Validity and Efficiency of Blood Pressure Estimates Using Ambulatory and Clinic Measurements and Modern Missing Data Methods. American Journal of Hypertension, 25(7), 764–769.Google Scholar
Zvolensky, M., Forsyth, J., & Johnson, K. (2013). Laboratory Methods in Experimental Psychopathology. In Comer, J. S. & Kendall, P. C. (Eds.), The Oxford Handbook of Research Strategies for Clinical Psychology. New York: Oxford University Press.Google Scholar

References

Aaron, R. V., & Benning, S. D. (2016). Postauricular Reflexes Elicited by Soft Acoustic Clicks and Loud Noise Probes: Reliability, Prepulse Facilitation, and Sensitivity to Picture Contents. Psychophysiology, 53(12), 1900‒1908.Google Scholar
Armstrong, T., & Olatunji, B. O. (2012). Eye Tracking of Attention in the Affective Disorders: A Meta-Analytic Review and Synthesis. Clinical Psychology Review, 32(8), 704‒723.Google Scholar
Bai, X., Li, J., Zhou, L., & Li, X. (2009). Influence of the Menstrual Cycle on Nonlinear Properties of Heart Rate Variability in Young Women. American Journal of Physiology ‒ Heart and Circulatory Physiology, 297(2), H765‒H774.Google Scholar
Bast, N., Poustka, L., & Freitag, C. M. (2018). The Locus Coeruleus–Norepinephrine System as Pacemaker of Attention – A Developmental Mechanism of Derailed Attentional Function in Autism Spectrum Disorder. European Journal of Neuroscience, 47(2), 115‒125.Google Scholar
Beauchaine, T. (2001). Vagal Tone, Development, and Gray’s Motivational Theory: Toward an Integrated Model of Autonomic Nervous System Functioning in Psychopathology. Development and Psychopathology, 13(2), 183‒214.Google Scholar
Beauchaine, T. P. (2015). Respiratory Sinus Arrhythmia: A Transdiagnostic Biomarker of Emotion Dysregulation and Psychopathology. Current Opinion in Psychology, 3, 43‒47.Google Scholar
Beauchaine, T. P., & Gatzke-Kopp, L. M. (2012). Instantiating the Multiple Levels of Analysis Perspective in a Program of Study on Externalizing Behavior. Development and Psychopathology, 24(3), 1003‒1018.Google Scholar
Beauchaine, T. P., Katkin, E. S., Strassberg, Z., & Snarr, J. (2001). Disinhibitory Psychopathology in Male Adolescents: Discriminating Conduct Disorder from Attention-Deficit/Hyperactivity Disorder through Concurrent Assessment of Multiple Autonomic States. Journal of Abnormal Psychology, 110(4), 610‒624.Google Scholar
Beauchaine, T. P., & Thayer, J. F. (2015). Heart Rate Variability as a Transdiagnostic Biomarker of Psychopathology. International Journal of Psychophysiology, 98(2), 338‒350.Google Scholar
Benarroch, E. E. (1993). The Central Autonomic Network: Functional Organization, Dysfunction, and Perspective. In Mayo Clinic Proceedings (Vol. 68, No. 10, pp. 988‒1001). Rochester, MN: Elsevier.Google Scholar
Benning, S. D., & Ait Oumeziane, B. (2017). Reduced Positive Emotion and Underarousal are Uniquely Associated with Subclinical Depression Symptoms: Evidence from Psychophysiology, Self‐Report, and Symptom Clusters. Psychophysiology, 54(7), 1010–1030.Google Scholar
Berthoud, H.-R., & Neuhuber, W. L. (2000). Functional and Chemical Anatomy of the Afferent Vagal System. Autonomic Neuroscience, 85(1), 1‒17.Google Scholar
Bertsch, K., Hagemann, D., Naumann, E., Schächinger, H., & Schulz, A. (2012). Stability of Heart Rate Variability Indices Reflecting Parasympathetic Activity. Psychophysiology, 49(5), 672‒682.Google Scholar
Blumenthal, T. D., Cuthbert, B. N., Filion, D. L., Hackley, S., Lipp, O. V., & Van Boxtel, A. (2005). Committee Report: Guidelines for Human Startle Eyeblink Electromyographic Studies. Psychophysiology, 42(1), 1‒15.Google Scholar
Boucsein, W. (2012). Electrodermal Activity. New York: Springer Science & Business Media.Google Scholar
Bradford, D. E., Starr, M. J., Shackman, A. J., & Curtin, J. J. (2015). Empirically Based Comparisons of the Reliability and Validity of Common Quantification Approaches for Eyeblink Startle Potentiation in Humans. Psychophysiology, 52(12), 1669‒1681.Google Scholar
Bradley, M. M., Miccoli, L., Escrig, M. A., & Lang, P. J. (2008). The Pupil as a Measure of Emotional Arousal and Autonomic Activation. Psychophysiology, 45(4), 602‒607.Google Scholar
Brenner, S. L., & Beauchaine, T. P. (2011). Pre‐Ejection Period Reactivity and Psychiatric Comorbidity Prospectively Predict Substance Use Initiation among Middle‐Schoolers: A Pilot Study. Psychophysiology, 48(11), 1588‒1596.Google Scholar
Brown, C. C. (1967). A Proposed Standard Nomenclature for Psychophysiologic Measures. Psychophysiology, 4(2), 260‒264.Google Scholar
Brown, J. S., Kalish, H. I., & Farber, I. (1951). Conditioned Fear as Revealed by Magnitude of Startle Response to an Auditory Stimulus. Journal of Experimental Psychology, 41(5), 317‒328.Google Scholar
Burkhouse, K. L., Siegle, G. J., & Gibb, B. E. (2014). Pupillary Reactivity to Emotional Stimuli in Children of Depressed and Anxious Mothers. Journal of Child Psychology and Psychiatry, 55(9), 1009‒1016.Google Scholar
Burkhouse, K. L., Siegle, G. J., Woody, M. L., Kudinova, A. Y., & Gibb, B. E. (2015). Pupillary Reactivity to Sad Stimuli as a Biomarker of Depression Risk: Evidence from a Prospective Study of Children. Journal of Abnormal Psychology, 124(3), 498‒506.Google Scholar
Burleson, M. H., Poehlmann, K. M., Hawkley, L. C., Ernst, J. M., Berntson, G. G., Malarkey, W. B., … Cacioppo, J. T. (2003). Neuroendocrine and Cardiovascular Reactivity to Stress in Mid‐Aged and Older Women: Long‐Term Temporal Consistency of Individual Differences. Psychophysiology, 40(3), 358‒369.Google Scholar
Cacioppo, J. T., Tassinary, L. G., & Berntson, G. (2007). Handbook of Psychophysiology. Cambridge: Cambridge University Press.Google Scholar
Camm, A. J., Malik, M., Bigger, J., Breithardt, G., Cerutti, S., Cohen, R., … Kleiger, R. (1996). Heart Rate Variability: Standards of Measurement, Physiological Interpretation and Clinical Use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Circulation, 93(5), 1043‒1065.Google Scholar
Cannon, W. B. (1929). Bodily Changes in Pain, Fear, Hunger, and Rage. New York: Appleton.Google Scholar
Cannon, W. B. (1932). The Wisdom of the Body. New York: W. W. Norton.Google Scholar
Cellini, N., Whitehurst, L. N., McDevitt, E. A., & Mednick, S. C. (2016). Heart Rate Variability during Daytime Naps in Healthy Adults: Autonomic Profile and Short‐Term Reliability. Psychophysiology, 53(4), 473‒481.Google Scholar
Chalmers, J. A., Quintana, D. S., Maree, J., Abbott, A., & Kemp, A. H. (2014). Anxiety Disorders are Associated with Reduced Heart Rate Variability: A Meta-Analysis. Frontiers in Psychiatry, 5, 1‒11.Google Scholar
Chita-Tegmark, M. (2016). Social Attention in ASD: A Review and Meta-Analysis of Eye-Tracking Studies. Research in Developmental Disabilities, 48, 79‒93.Google Scholar
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd edn.). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Coyne, J., & Sibley, C. (2016). Investigating the Use of Two Low Cost Eye Tracking Systems for Detecting Pupillary Response to Changes in Mental Workload. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 60, No. 1, pp. 37‒41). Los Angeles, CA: Sage’Google Scholar
Davis, M. (2006). Neural Systems Involved in Fear and Anxiety Measured with Fear-Potentiated Startle. American Psychologist, 61(8), 741‒756.Google Scholar
Davis, R. C. (1958). The Domain of Homeostasis. Psychological Review, 65(1), 8‒13.Google Scholar
Dawson, M. E., Schell, A. M., & Bohmelt, A. H. (2008). Startle Modification: Implications for Neuroscience, Cognitive Science, and Clinical Science. Cambridge: Cambridge University Press.Google Scholar
Dawson, M. E., Schell, A. M., Braaten, J. R., & Catania, J. J. (1985). Diagnostic Utility of Autonomic Measures for Major Depressive Disorders. Psychiatry Research, 15(4), 261–270.Google Scholar
Dawson, M. E., Schell, A. M., & Filion, D. L. (2007). The Electrodermal System. Handbook of Psychophysiology, 2, 200‒223.Google Scholar
Debat, V., & David, P. (2001). Mapping Phenotypes: Canalization, Plasticity and Developmental Stability. Trends in Ecology & Evolution, 16(10), 555‒561.Google Scholar
Delabarre, E. B. (1898). A Method of Recording Eye-Movements. The American Journal of Psychology, 9(4), 572‒574.Google Scholar
Diefendorf, A. R., & Dodge, R. (1908). An Experimental Study of the Ocular Reactions of the Insane from Photographic Records. Brain, 31(3), 451‒489.Google Scholar
Duchowski, A. T. (2007). Eye Tracking Methodology. Theory and Practice (3rd edn.). Basel: Springer International.Google Scholar
Eaton, R. C. (1984). Neural Mechanisms of Startle Behavior. New York: Springer Science & Business Media.Google Scholar
Einthoven, W., Fahr, G., & de Waart, A. (1913). Ueber die Rechtung und die Manifeste Grösse der Potential schwankungon im menschlechen Herzen und ueber den Einfluss der Herzglage auf die Form des Electrokardiogramms. Pflügers Archives European Journal of Physiology, 150, 275‒315.Google Scholar
Eppinger, H., Hess, L., Kraus, W. M., & Jelliffe, S. E. (1915). Vagotonia: A Clinical Study in Vegetative Neurology. New York: Nervous and Mental Disease Publishing Company.Google Scholar
Ettinger, U., Kumari, V., Crawford, T. J., Davis, R. E., Sharma, T., & Corr, P. J. (2003). Reliability of Smooth Pursuit, Fixation, and Saccadic Eye Movements. Psychophysiology, 40(4), 620‒628.Google Scholar
Exner, S. (1874). Experimental Investigation of the Simplest Mental Process: First Article. Pflugers Archiv: European Journal of Physiology, 7, 601‒660.Google Scholar
Farzin, F., Scaggs, F., Hervey, C., Berry-Kravis, E., & Hessl, D. (2011). Reliability of Eye Tracking and Pupillometry Measures in Individuals with Fragile X Syndrome. Journal of Autism and Developmental Disorders, 41(11), 1515‒1522.Google Scholar
Fere, C. (1888). Note on Changes in Electrical Resistance under the Effect of Sensory Stimulation and Emotion. Comptes rendus des Seancs de la Societe de Biologie, 5, 28‒33.Google Scholar
Fowles, D. C. (1980). The Three Arousal Model: Implications of Gray’s Two‐Factor Learning Theory for Heart Rate, Electrodermal Activity, and Psychopathy. Psychophysiology, 17(2), 87‒104.Google Scholar
Franco, J., De Pablo, J., Gaviria, A., Sepulveda, E., & Vilella, E. (2014). Smooth Pursuit Eye Movements and Schizophrenia: Literature Review. Archivos de la Sociedad Española de Oftalmología (English Edition), 89(9), 361‒367.Google Scholar
Franzen, J., & Brinkmann, K. (2015). Blunted Cardiovascular Reactivity in Dysphoria during Reward and Punishment Anticipation. International Journal of Psychophysiology, 95(3), 270‒277.Google Scholar
Freedman, L. W., Scerbo, A. S., Dawson, M. E., Raine, A., McClure, W. O., & Venables, P. H. (1994). The Relationship of Sweat Gland Count to Electrodermal Activity. Psychophysiology, 31(2), 196‒200.Google Scholar
Friedman, M. (1945). Studies Concerning the Etiology and Pathogenesis of Neurocirculatory Asthenia: III. The Cardiovascular Manifestations of Neurocirculatory Asthenia. American Heart Journal, 30(5), 478‒491.Google Scholar
Gatzke‐Kopp, L. M. (2016). Diversity and Representation: Key Issues for Psychophysiological Science. Psychophysiology, 53(1), 3‒13.Google Scholar
Gavin, W., & Davies, P. (2008). Obtaining Reliable Psychophysiological Data with Child Participants: Methodological Considerations. In Developmental Psychophysiology: Theory, Systems, and Methods (pp. 424‒447). New York: Cambridge University Press.Google Scholar
Goldberger, E. (1945). The Validity of the Einthoven Triangle Hypothesis. American Heart Journal, 29(3), 369‒377.Google Scholar
Gooding, D. C., Iacono, W. G., & Beiser, M. (1994). Temporal Stability of Smooth‐Pursuit Eye Tracking in First‐Episode Psychosis. Psychophysiology, 31(1), 62‒67.Google Scholar
Gordan, R., Gwathmey, J. K., & Xie, L.-H. (2015). Autonomic and Endocrine Control of Cardiovascular Function. World Journal of Cardiology, 7(4), 204‒214.Google Scholar
Gorka, S. M., Lieberman, L., Shankman, S. A., & Phan, K. L. (2017). Startle Potentiation to Uncertain Threat as a Psychophysiological Indicator of Fear-Based Psychopathology: An Examination across Multiple Internalizing Disorders. Journal of Abnormal Psychology, 126(1), 8‒18.Google Scholar
Granholm, E., Ruiz, I., Gallegos-Rodriguez, Y., Holden, J., & Link, P. C. (2016). Pupillary Responses as a Biomarker of Diminished Effort Associated with Defeatist Attitudes and Negative Symptoms in Schizophrenia. Biological Psychiatry, 80(8), 581‒588.Google Scholar
Gray, J. A. (1975). Elements of a Two-Process Theory of Learning. Oxford: Academic Press.Google Scholar
Harker, M. (2013). Psychological Sweating: A Systematic Review Focused on Aetiology and Cutaneous Response. Skin Pharmacology and Physiology, 26(2), 92‒100.Google Scholar
Hastrup, J. L. (1986). Duration of Initial Heart Rate Assessment in Psychophysiology: Current Practices and Implications. Psychophysiology, 23(1), 15‒18.Google Scholar
Hess, E. H., & Polt, J. M. (1960). Pupil Size as Related to Interest Value of Visual Stimuli. Science, 132(3423), 349‒350.Google Scholar
Hess, E. H., & Polt, J. M. (1964). Pupil Size in Relation to Mental Activity during Simple Problem-Solving. Science, 143(3611), 1190‒1192.Google Scholar
Hirshoren, N., Tzoran, I., Makrienko, I., Edoute, Y., Plawner, M. M., Itskovitz-Eldor, J., & Jacob, G. (2002). Menstrual Cycle Effects on the Neurohumoral and Autonomic Nervous Systems Regulating the Cardiovascular System. Journal of Clinical Endocrinology & Metabolism, 87(4), 1569‒1575.Google Scholar
Iacono, W. G., & Lykken, D. T. (1981). Two‐Year Retest Stability of Eye Tracking Performance and a Comparison of Electro‐Oculographic and Infrared Recording Techniques: Evidence of EEG in the Electro‐Oculogram. Psychophysiology, 18(1), 49‒55.Google Scholar
Insel, T., Cuthbert, B., Garvey, M., Heinssen, R., Pine, D. S., Quinn, K., … Wang, P. (2010). Research Domain Criteria (RDoC): Toward a New Classification Framework for Research on Mental Disorders. American Journal of Psychiatry, 167(7), 748‒751.Google Scholar
James, W. (1884). What Is an Emotion? Mind, 9(34), 188‒205.Google Scholar
Jänig, W. (2008). Integrative Action of the Autonomic Nervous System: Neurobiology of Homeostasis. Cambridge: Cambridge University Press.Google Scholar
Jennings, J. R., Kamarck, T., Stewart, C., Eddy, M., & Johnson, P. (1992). Alternate Cardiovascular Baseline Assessment Techniques: Vanilla or Resting Baseline. Psychophysiology, 29(6), 742‒750.Google Scholar
Kathmann, N., Hochrein, A., Uwer, R., & Bondy, B. (2003). Deficits in Gain of Smooth Pursuit Eye Movements in Schizophrenia and Affective Disorder Patients and Their Unaffected Relatives. American Journal of Psychiatry, 160(4), 696‒702.Google Scholar
Kaye, J. T., Bradford, D. E., & Curtin, J. J. (2016). Psychometric Properties of Startle and Corrugator Response in NPU, Affective Picture Viewing, and Resting State Tasks. Psychophysiology, 53(8), 1241‒1255.Google Scholar
Keefe, R. S., Silverman, J. M., Mohs, R. C., Siever, L. J., Harvey, P. D., Friedman, L., … Schmeidler, J. (1997). Eye Tracking, Attention, and Schizotypal Symptoms in Nonpsychotic Relatives of Patients with Schizophrenia. Archives of General Psychiatry, 54(2), 169‒176.Google Scholar
Kelsey, R. M., Ornduff, S. R., & Alpert, B. S. (2007). Reliability of Cardiovascular Reactivity to Stress: Internal Consistency. Psychophysiology, 44(2), 216‒225.Google Scholar
Kendler, K. S. (2009). An Historical Framework for Psychiatric Nosology. Psychological Medicine, 39(12), 1935‒1941.Google Scholar
Kumra, S., Sporn, A., Hommer, D. W., Nicolson, R., Thaker, G., Israel, E., … Gochman, P. (2001). Smooth Pursuit Eye-Tracking Impairment in Childhood-Onset Psychotic Disorders. American Journal of Psychiatry, 158(8), 1291‒1298.Google Scholar
Laborde, S., Mosley, E., & Thayer, J. F. (2017). Heart Rate Variability and Cardiac Vagal Tone in Psychophysiological Research–Recommendations for Experiment Planning, Data Analysis, and Data Reporting. Frontiers in Psychology, 8, 1‒18.Google Scholar
Lacey, J. I., & Lacey, B. C. (1958). The Relationship of Resting Autonomic Activity to Motor Impulsivity. Research Publications of the Association for Research in Nervous & Mental Disease, 36, 144‒209.Google Scholar
Lambert, R. H., Monty, R. A., & Hall, R. J. (1974). High-Speed Data Processing and Unobtrusive Monitoring of Eye Movements. Behavior Research Methods & Instrumentation, 6(6), 525‒530.Google Scholar
Lang, P. J., Greenwald, M. K., Bradley, M. M., & Hamm, A. O. (1993). Looking at Pictures: Affective, Facial, Visceral, and Behavioral Reactions. Psychophysiology, 30(3), 261‒273.Google Scholar
Lang, P. J., & McTeague, L. M. (2009). The Anxiety Disorder Spectrum: Fear Imagery, Physiological Reactivity, and Differential Diagnosis. Anxiety, Stress, & Coping, 22(1), 5–25.Google Scholar
Larsen, P., Tzeng, Y., Sin, P., & Galletly, D. (2010). Respiratory Sinus Arrhythmia in Conscious Humans during Spontaneous Respiration. Respiratory Physiology & Neurobiology, 174(1), 111‒118.Google Scholar
Lehrer, P. M., & Gevirtz, R. (2014). Heart Rate Variability Biofeedback: How and Why Does It Work? Frontiers in Psychology, 5, 1‒9.Google Scholar
Levy, M. N. (1971). Brief Reviews: Sympathetic-Parasympathetic Interactions in the Heart. Circulation Research, 29(5), 437‒445.Google Scholar
Lieberman, L., Stevens, E. S., Funkhouser, C. J., Weinberg, A., Sarapas, C., Huggins, A. A., & Shankman, S. A. (2017). How Many Blinks are Necessary for a Reliable Startle Response? A Test Using the NPU-Threat Task. International Journal of Psychophysiology, 114, 24‒30.Google Scholar
Loewenfeld, I. E. (1999). The Pupil: Anatomy, Physiology, and Clinical Applications. Oxford: Butterworth-Heinemann.Google Scholar
Lubman, D. I., Yücel, M., Kettle, J. W., Scaffidi, A., MacKenzie, T., Simmons, J. G., & Allen, N. B. (2009). Responsiveness to Drug Cues and Natural Rewards in Opiate Addiction: Associations with Later Heroin Use. Archives of General Psychiatry, 66(2), 205‒212.Google Scholar
Lykken, D. T., & Venables, P. H. (1971). Direct Measurement of Skin Conductance: A Proposal for Standardization. Psychophysiology, 8(5), 656‒672.Google Scholar
Mckinley, P. S., King, A. R., Shapiro, P. A., Slavov, I., Fang, Y., Chen, I. S., … Sloan, R. P. (2009). The Impact of Menstrual Cycle Phase on Cardiac Autonomic Regulation. Psychophysiology, 46(4), 904‒911.Google Scholar
McTeague, L. M., & Lang, P. J. (2012). The Anxiety Spectrum and the Reflex Physiology of Defense: From Circumscribed Fear to Broad Distress. Depression and Anxiety, 29(4), 264–281.Google Scholar
Moore, T., & Zirnsak, M. (2017). Neural Mechanisms of Selective Visual Attention. Annual Review of Psychology, 68, 47‒72.Google Scholar
Nassar, M. R., Rumsey, K. M., Wilson, R. C., Parikh, K., Heasly, B., & Gold, J. I. (2012). Rational Regulation of Learning Dynamics by Pupil-Linked Arousal Systems. Nature Neuroscience, 15(7), 1040‒1046.Google Scholar
Nelson, B. D., & Hajcak, G. (2017). Anxiety and Depression Symptom Dimensions Demonstrate Unique Relationships with the Startle Reflex in Anticipation of Unpredictable Threat in 8 to 14 Year-Old Girls. Journal of Abnormal Child Psychology, 45(2), 397‒410.Google Scholar
Nelson, B. D., Hajcak, G., & Shankman, S. A. (2015). Event‐Related Potentials to Acoustic Startle Probes during the Anticipation of Predictable and Unpredictable Threat. Psychophysiology, 52(7), 887‒894.Google Scholar
O’Driscoll, G. A., & Callahan, B. L. (2008). Smooth Pursuit in Schizophrenia: A Meta-Analytic Review of Research since 1993. Brain and Cognition, 68(3), 359‒370.Google Scholar
Ortiz, J., & Raine, A. (2004). Heart Rate Level and Antisocial Behavior in Children and Adolescents: A Meta-Analysis. Journal of the American Academy of Child & Adolescent Psychiatry, 43(2), 154‒162.Google Scholar
Pabst, O., Tronstad, C., Grimnes, S., Fowles, D., & Martinsen, Ø. G. (2017). Comparison between the AC and DC Measurement of Electrodermal Activity. Psychophysiology, 54(3), 374‒385.Google Scholar
Papagiannopoulou, E. A., Chitty, K. M., Hermens, D. F., Hickie, I. B., & Lagopoulos, J. (2014). A Systematic Review and Meta-Analysis of Eye-Tracking Studies in Children with Autism Spectrum Disorders. Social Neuroscience, 9(6), 610‒632.Google Scholar
Payne, A. F., Schell, A. M., & Dawson, M. E. (2016). Lapses in Skin Conductance Responding across Anatomical Sites: Comparison of Fingers, Feet, Forehead, and Wrist. Psychophysiology, 53(7), 1084‒1092.Google Scholar
Peysakhovich, V., Vachon, F., & Dehais, F. (2017). The Impact of Luminance on Tonic and Phasic Pupillary Responses to Sustained Cognitive Load. International Journal of Psychophysiology, 112, 40‒45.Google Scholar
Piferi, R. L., Kline, K. A., Younger, J., & Lawler, K. A. (2000). An Alternative Approach for Achieving Cardiovascular Baseline: Viewing an Aquatic Video. International Journal of Psychophysiology, 37(2), 207‒217.Google Scholar
Pittig, A., Arch, J. J., Lam, C. W., & Craske, M. G. (2013). Heart Rate and Heart Rate Variability in Panic, Social Anxiety, Obsessive-Compulsive, and Generalized Anxiety Disorders at Baseline and in Response to Relaxation and Hyperventilation. International Journal of Psychophysiology, 87(1), 19‒27.Google Scholar
Porges, S. W. (1992). Vagal Tone: A Physiologic Marker of Stress Vulnerability. Pediatrics, 90(3), 498‒504.Google Scholar
Porges, S. W. (1995). Orienting in a Defensive World: Mammalian Modifications of Our Evolutionary Heritage. A Polyvagal Theory. Psychophysiology, 32(4), 301‒318.Google Scholar
Portnoy, J., & Farrington, D. P. (2015). Resting Heart Rate and Antisocial Behavior: An Updated Systematic Review and Meta-Analysis. Aggression and Violent Behavior, 22, 33‒45.Google Scholar
Portnoy, J., Raine, A., Chen, F. R., Pardini, D., Loeber, R., & Jennings, J. R. (2014). Heart Rate and Antisocial Behavior: The Mediating Role of Impulsive Sensation Seeking. Criminology, 52(2), 292‒311.Google Scholar
Raine, A. (2002). Biosocial Studies of Antisocial and Violent Behavior in Children and Adults: A Review. Journal of Abnormal Child Psychology, 30(4), 311‒326.Google Scholar
Ramsay, D. S., & Woods, S. C. (2014). Clarifying the Roles of Homeostasis and Allostasis in Physiological Regulation. Psychological Review, 121(2), 225‒247.Google Scholar
Ray, W. J., Molnar, C., Aikins, D., Yamasaki, A., Newman, M. G., Castonguay, L., & Borkovec, T. D. (2009). Startle Response in Generalized Anxiety Disorder. Depression and Anxiety, 26(2), 147‒154.Google Scholar
Richards, A., French, C. C., Calder, A. J., Webb, B., Fox, R., & Young, A. W. (2002). Anxiety-Related Bias in the Classification of Emotionally Ambiguous Facial Expressions. Emotion, 2(3), 273‒287.Google Scholar
Rosenbaum, B. L., Bui, E., Marin, M.-F., Holt, D. J., Lasko, N. B., Pitman, R. K., … Milad, M. R. (2015). Demographic Factors Predict Magnitude of Conditioned Fear. International Journal of Psychophysiology, 98(1), 59‒64.Google Scholar
Roth, W. T., Telch, M. J., Taylor, C. B., Sachitano, J. A., Gallen, C. C., Kopell, M. L., … Pfefferbaum, A. (1986). Autonomic Characteristics of Agoraphobia with Panic Attacks. Biological Psychiatry, 21(12), 1133‒1154.Google Scholar
Roth, W. T., Dawson, M. E., & Filion, D. L. (2012). Publication Recommendations for Electrodermal Measurements. Psychophysiology, 49(8), 1017‒1034.Google Scholar
Roy, J.-C., Boucsein, W., Fowles, D. C., & Gruzelier, J. (2012). Progress in Electrodermal Research (Vol. 249). New York: Springer Science & Business Media.Google Scholar
Sarchiapone, M., Gramaglia, C., Iosue, M., Carli, V., Mandelli, L., Serretti, A., … Zeppegno, P. (2018). The Association between Electrodermal Activity (EDA), Depression and Suicidal Behaviour: A Systematic Review and Narrative Synthesis. BMC Psychiatry, 18(1), 1‒27.Google Scholar
Scerbo, A. S., Freedman, L. W., Raine, A., Dawson, M. E., & Venables, P. H. (1992). A Major Effect of Recording Site on Measurement of Electrodermal Activity. Psychophysiology, 29(2), 241‒246.Google Scholar
Schächinger, H., Weinbacher, M., Kiss, A., Ritz, R., & Langewitz, W. (2001). Cardiovascular Indices of Peripheral and Central Sympathetic Activation. Psychosomatic Medicine, 63(5), 788‒796.Google Scholar
Schell, A. M., Dawson, M. E., Nuechterlein, K. H., Subotnik, K. L., & Ventura, J. (2002). The Temporal Stability of Electrodermal Variables Over a One-Year Period in Patients with Recent-Onset Schizophrenia and in Normal Subjects. Psychophysiology, 39(2), 124‒132.Google Scholar
Scott, L. N., Zalewski, M., Beeney, J. E., Jones, N. P., & Stepp, S. D. (2017). Pupillary and Affective Responses to Maternal Feedback and the Development of Borderline Personality Disorder Symptoms. Development and Psychopathology, 29(3), 1089‒1104.Google Scholar
Sherwood, A., Allen, M. T., Fahrenberg, J., Kelsey, R. M., Lovallo, W. R., & Doornen, L. J. (1990). Methodological Guidelines for Impedance Cardiography. Psychophysiology, 27(1), 1‒23.Google Scholar
Sijtsema, J. J., Veenstra, R., Lindenberg, S., van Roon, A. M., Verhulst, F. C., Ormel, J., & Riese, H. (2010). Mediation of Sensation Seeking and Behavioral Inhibition on the Relationship between Heart Rate and Antisocial Behavior: The TRAILS Study. Journal of the American Academy of Child & Adolescent Psychiatry, 49(5), 493‒502.Google Scholar
Sirois, S., & Brisson, J. (2014). Pupillometry. Wiley Interdisciplinary Reviews: Cognitive Science, 5(6), 679‒692.Google Scholar
Sloan, D. M., & Sandt, A. R. (2010). Depressed Mood and Emotional Responding. Biological Psychology, 84(2), 368‒374.Google Scholar
Smith, R., Thayer, J. F., Khalsa, S. S., & Lane, R. D. (2017). The Hierarchical Basis of Neurovisceral Integration. Neuroscience & Biobehavioral Reviews, 75, 274‒296.Google Scholar
Staib, M., Castegnetti, G., & Bach, D. R. (2015). Optimising a Model-Based Approach to Inferring Fear Learning from Skin Conductance Responses. Journal of Neuroscience Methods, 255, 131‒138.Google Scholar
Stein, M. B., Tancer, M. E., & Uhde, T. W. (1992). Heart Rate and Plasma Norepinephrine Responsivity to Orthostatic Challenge in Anxiety Disorders: Comparison of Patients with Panic Disorder and Social Phobia and Normal Control Subjects. Archives of General Psychiatry, 49 (4), 311‒317.Google Scholar
Sterling, P. (2004). Principles of Allostasis: Optimal Design, Predictive Regulation, Pathophysiology, and Rational. In Schulkin, J. (Ed.), Allostasis, Homeostasis, and the Costs of Physiological Adaptation (pp. 17‒64). Cambridge: Cambridge University Press.Google Scholar
Sterling, P., & Eyer, J. (1988). Allostasis: A New Paradigm to Explain Arousal Pathology. In Fisher, S. & Reason, J. (Eds.), Handbook of Life Stress, Cognition and Health (pp. 629‒649). Oxford: John Wiley.Google Scholar
Tarchanoff, J. (1890). Galvanic Phenomena in the Human Skin during Stimulation of the Sensory Organs and during Various Forms of Mental Activity. Pflügers Archiv für die gesammte Physiologie des Menschen und der Tiere, 46, 46‒55.Google Scholar
Tassinary, L. G., Hess, U., & Carcoba, L. M. (2012). Peripheral Physiological Measures of Psychological Constructs. APA Handbook of Research Methods in Psychology, 1, 461–488.Google Scholar
Teixeira, A. L., Ramos, P. S., Vianna, L. C., & Ricardo, D. R. (2015). Heart Rate Variability across the Menstrual Cycle in Young Women Taking Oral Contraceptives. Psychophysiology, 52(11), 1451‒1455.Google Scholar
Thayer, J. F., & Lane, R. D. (2000). A Model of Neurovisceral Integration in Emotion Regulation and Dysregulation. Journal of Affective Disorders, 61(3), 201‒216.Google Scholar
Thier, P., & Ilg, U. J. (2005). The Neural Basis of Smooth-Pursuit Eye Movements. Current Opinion in Neurobiology, 15(6), 645‒652.Google Scholar
Venables, P. H., & Christie, M. J. (1980). Electrodermal Activity. In Martin, I. & Venables, P. H. (Eds.), Techniques in Psychophysiology (pp. 3‒67). New York: John Wiley.Google Scholar
Vigouroux, R. (1879). Sur le role de la resistance electrique des tissues dans l’electro-diagnostic. Comptes Rendus Societe de Biologie, 31, 336‒339.Google Scholar
Waddington, C. H. (1957). The Strategy of the Genes: A Discussion of Some Aspects of Theoretical Biology. With an Appendix by H. Kacser. London: Allen & UnwinGoogle Scholar
Watson, D. (2005). Rethinking the Mood and Anxiety Disorders: A Quantitative Hierarchical Model for DSM-5. Journal of Abnormal Psychology, 114(4), 522‒536.Google Scholar
Wilder, J. (1958). Modern Psychophysiology and the Law of Initial Value. American Journal of Psychotherapy, 12, 199‒221.Google Scholar
Yoshie, N., & Okudaira, T. (1969). Myogenic Evoked Potential Responses to Clicks in Man. Acta Oto-laryngologica, 67(sup252), 89‒103.Google Scholar
Young, L. R., & Sheena, D. (1975). Survey of Eye Movement Recording Methods. Behavior Research Methods & Instrumentation, 7(5), 397‒429.Google Scholar
Zahn, T. P., Frith, C. D., & Steinhauer, S. R. (1991). Autonomic Functioning in Schizophrenia: Electrodermal Activity, Heart Rate, Pupillography. In Steinhauer, S. R., Gruzelier, J. H., & Zubin, J. (Eds.), Handbook of Schizophrenia, Vol. 5. Neuropsychology, Psychophysiology, and Information Processing (pp. 185‒224). New York: Elsevier Science.Google Scholar
Zisner, A. R., & Beauchaine, T. P. (2016). Psychophysiological Methods and Developmental Psychopathology. In Cicchetti, D. (Ed.), Developmental Psychopathology (pp. 832‒884). Hoboken, NJ: John Wiley.Google Scholar

References

1000 Genomes Project Consortium, Auton, A., Brooks, L. D., Durbin, R. M., Garrison, E. P., Kang, H. M., … Abecasis, G. R. (2015). A Global Reference for Human Genetic Variation. Nature, 526(7571), 68–74.Google Scholar
Addington, A. M., & Rapoport, J. L. (2012). Annual Research Review: Impact of Advances in Genetics in Understanding Developmental Psychopathology. Journal of Child Psychology and Psychiatry, 53(5), 510–518.Google Scholar
Anastasi, A. (1958). Heredity, Environment, and the Question How? Psychological Review, 65(4), 197–208.Google Scholar
Andrew, T., Hart, D. J., Snieder, H., Lange, M. de, Spector, T. D., & MacGregor, A. J. (2001). Are Twins and Singletons Comparable? A Study of Disease-Related and Lifestyle Characteristics in Adult Women. Twin Research and Human Genetics, 4(6), 464–477.Google Scholar
Antonarakis, S. E., & Beckmann, J. S. (2006). Mendelian Disorders Deserve More Attention. Nature Reviews Genetics, 7(4), 277–282.Google Scholar
Arranz, M. J., & de Leon, J. (2007). Pharmacogenetics and Pharmacogenomics of Schizophrenia: A Review of Last Decade of Research. Molecular Psychiatry, 12(8), 707–747.Google Scholar
Autism Spectrum Disorders Working Group of the Psychiatric Genomics Consortium. (2017). Meta-analysis of GWAS of over 16,000 Individuals with Autism Spectrum Disorder Highlights a Novel Locus at 10q24.32 and a Significant Overlap with Schizophrenia. Molecular Autism, 8, 21.Google Scholar
Bassett, A. S., & Chow, E. W. C. (2008). Schizophrenia and 22q11.2 Deletion Syndrome. Current Psychiatry Reports, 10(2), 148–157.Google Scholar
Bin, Xu, Roos, J. L., Levy, S., van Rensburg, E. J., Gogos, J. A., & Karayiorgou, M. (2008). Strong Association of De Novo Copy Number Mutations with Sporadic Schizophrenia. Nature Genetics, 40(7), 880–885.Google Scholar
Boomsma, D., Busjahn, A., & Peltonen, L. (2002). Classical Twin Studies and Beyond. Nature Reviews Genetics, 3(11), 872‒882.Google Scholar
Boraska, V., Franklin, C. S., Floyd, J. a. B., Thornton, L. M., Huckins, L. M., Southam, L., … Bulik, C. M. (2014). A Genome-Wide Association Study of Anorexia Nervosa. Molecular Psychiatry, 19(10), 1085–1094.Google Scholar
Buckholtz, J. W., & Meyer-Lindenberg, A. (2012). Psychopathology and the Human Connectome: Toward a Transdiagnostic Model of Risk for Mental Illness. Neuron, 74(6), 990–1004.Google Scholar
Bulik, C. M., Sullivan, P. F., Tozzi, F., Furberg, H., Lichtenstein, P., & Pedersen, N. L. (2006). Prevalence, Heritability, and Prospective Risk Factors for Anorexia Nervosa. Archives of General Psychiatry, 63(3), 305–312.Google Scholar
Burbridge, D. (2001). Francis Galton on Twins, Heredity and Social Class. British Journal for the History of Science, 34(3), 323–340.Google Scholar
Burt, S. A. (2009). Rethinking Environmental Contributions to Child and Adolescent Psychopathology: A Meta-Analysis of Shared Environmental Influences. Psychological Bulletin, 135(4), 608‒637.Google Scholar
Bush, W. S., & Moore, J. H. (2012). Chapter 11: Genome-Wide Association Studies. PLOS Computational Biology, 8(12), e1002822.Google Scholar
Carlborg, Ö., Haley, C. S., & Carlborg, O. (2004). Epistasis: Too Often Neglected in Complex Trait Studies? Nature Reviews Genetics, 5(8), 618–625.Google Scholar
Caspi, A., Sugden, K., Moffitt, T. E., Taylor, A., Craig, I. W., Harrington, H., … Poulton, R. (2003). Influence of Life Stress on Depression: Moderation by a Polymorphism in the 5-HTT Gene. Science, 301(5631), 386–389.Google Scholar
Caspi, A., Moffitt, T. E., Cannon, M., McClay, J., Murray, R., Harrington, H., … Craig, I. W. (2005). Moderation of the Effect of Adolescent-Onset Cannabis Use on Adult Psychosis by a Functional Polymorphism in the Catechol-O-Methyltransferase Gene: Longitudinal Evidence of a Gene X Environment Interaction. Biological Psychiatry, 57(10), 1117–1127.Google Scholar
Chen, C.-H., Lee, C.-S., Lee, M.-T. M., Ouyang, W.-C., Chen, C.-C., Chong, M.-Y., … Cheng, A. T.-A. (2014). Variant GADL1 and Response to Lithium Therapy in Bipolar I Disorder. New England Journal of Medicine, 370(2), 119–128.Google Scholar
Chial, H. (2008). Rare Genetic Disorders: Learning about Genetic Disease through Gene Mapping, SNPs, and Microarray Data. Nature Education, 1(1), 192.Google Scholar
Costas, J., Sanjuán, J., Ramos-Ríos, R., Paz, E., Agra, S., Tolosa, A., … Arrojo, M. (2011). Interaction between COMT Haplotypes and Cannabis in Schizophrenia: A Case-Only Study in Two Samples from Spain. Schizophrenia Research, 127(1–3), 22–27.Google Scholar
Crespi, B. J. (2016). Autism as a Disorder of High Intelligence. Frontiers in Neuroscience, 10, 300.Google Scholar
Cross-Disorder Group of the Psychiatric Genomics Consortium, Lee, S. H., Ripke, S., Neale, B. M., Faraone, S. V., Purcell, S. M., … International Inflammatory Bowel Disease Genetics Consortium (IIBDGC). (2013). Genetic Relationship between Five Psychiatric Disorders Estimated from Genome-Wide SNPs. Nature Genetics, 45(9), 984–994.Google Scholar
Culverhouse, R. C., Saccone, N. L., Horton, A. C., Ma, Y., Anstey, K. J., Banaschewski, T., … Bierut, L. J. (2017). Collaborative Meta-Analysis Finds no Evidence of a Strong Interaction between Stress and 5-HTTLPR Genotype Contributing to the Development of Depression. Molecular Psychiatry, 23, 133‒142.Google Scholar
Demontis, D., Walters, R. K., Martin, J., Mattheisen, M., Als, T. D., Agerbo, E., … Neale, B. M. (2019). Discovery of the First Genome-Wide Significant Risk Loci for Attention Deficit/Hyperactivity Disorder. Nature Genetics, 51(1), 63–75.Google Scholar
Dick, D. M., & Kendler, K. S. (2012). The Impact of Gene-Environment Interaction on Alcohol Use Disorders. Alcohol Research: Current Reviews, 34(3), 318–324.Google Scholar
Drew, L. J., Crabtree, G. W., Markx, S., Stark, K. L., Chaverneff, F., Xu, B., … Karayiorgou, M. (2011). The 22q11.2 Microdeletion: Fifteen Years of Insights into the Genetic and Neural Complexity of Psychiatric Disorders. International Journal of Developmental Neuroscience, 29(3), 259–281.Google Scholar
Duncan, L. E., & Keller, M. C. (2011). A Critical Review of the First 10 Years of Candidate Gene-by-Environment Interaction Research in Psychiatry. American Journal of Psychiatry, 168(10), 1041–1049.Google Scholar
Eichler, E. E., Flint, J., Gibson, G., Kong, A., Leal, S. M., Moore, J. H., & Nadeau, J. H. (2010). Missing Heritability and Strategies for Finding the Underlying Causes of Complex Disease. Nature Reviews Genetics, 11(6), 446–450.Google Scholar
Falconer, D. S. (1965). The Inheritance of Liability to Certain Diseases, Estimated from the Incidence among Relatives. Annals of Human Genetics, 29(1), 51–76.Google Scholar
Falconer, D. S., & Mackay, T. F. C. (1996). Introduction to Quantitative Genetics. Harlow: Pearson Education.Google Scholar
Faraone, S. V., Perlis, R. H., Doyle, A. E., Smoller, J. W., Goralnick, J. J., Holmgren, M. A., & Sklar, P. (2005). Molecular Genetics of Attention-Deficit/Hyperactivity Disorder. Biological Psychiatry, 57(11), 1313–1323.Google Scholar
Felson, J. (2014). What Can We Learn from Twin Studies? A Comprehensive Evaluation of the Equal Environments Assumption. Social Science Research, 43, 184–199.Google Scholar
Furberg, H., Kim, YunJung, Dackor, J., Boerwinkle, E., Franceschini, N., Ardissino, D., … Furberg, C. D. (2010). Genome-Wide Meta-Analyses Identify Multiple Loci Associated with Smoking Behavior. Nature Genetics, 42(5), 441–447.Google Scholar
Gandal, M. J., Leppa, V., Won, H., Parikshak, N. N., & Geschwind, D. H. (2016). The Road to Precision Psychiatry: Translating Genetics into Disease Mechanisms. Nature Neuroscience, 19(11), 1397–1407.Google Scholar
Gelernter, J., Kranzler, H. R., Sherva, R., Almasy, L., Koesterer, R., Smith, A. H., … Farrer, L. A. (2014). Genome-Wide Association Study of Alcohol Dependence: Significant Findings in African- and European-Americans Including Novel Risk Loci. Molecular Psychiatry, 19(1), 41–49.Google Scholar
Gormley, P., Anttila, V., Winsvold, B. S., Palta, P., Esko, T., Pers, T. H., … Palotie, A. (2016). Meta-Analysis of 375,000 Individuals Identifies 38 Susceptibility Loci for Migraine. Nature Genetics, 48(8), 856–866.Google Scholar
Hamburg, M. A., & Collins, F. S. (2010). The Path to Personalized Medicine. New England Journal of Medicine, 363(4), 301–304.Google Scholar
Hardy, J., & Singleton, A. (2009). Genomewide Association Studies and Human Disease. New England Journal of Medicine, 360(17), 1759–1768.Google Scholar
Hettema, J. M., Neale, M. C., & Kendler, K. S. (2001). A Review and Meta-Analysis of the Genetic Epidemiology of Anxiety Disorders. American Journal of Psychiatry, 158(10), 1568–1578.Google Scholar
Hill, W. G., Goddard, M. E., & Visscher, P. M. (2008). Data and Theory Point to Mainly Additive Genetic Variance for Complex Traits. PLOS Genetics, 4(2), e1000008.Google Scholar
Hirschhorn, J. N., & Daly, M. J. (2005). Genome-Wide Association Studies for Common Diseases and Complex Traits. Nature Reviews Genetics, 6(2), 95–108.Google Scholar
Hong, E. P., & Park, J. W. (2012). Sample Size and Statistical Power Calculation in Genetic Association Studies. Genomics & Informatics, 10(2), 117–122.Google Scholar
Hur, Y.-M., & Craig, J. M. (2013). Twin Registries Worldwide: An Important Resource for Scientific Research. Twin Research and Human Genetics, 16(1), 1–12.Google Scholar
Hyde, C. L., Nagle, M. W., Tian, C., Chen, X., Paciga, S. A., Wendland, J. R., … Winslow, A. R. (2016). Identification of 15 Genetic Loci Associated with Risk of Major Depression in Individuals of European Descent. Nature Genetics, 48(9), 1031–1036.Google Scholar
Jaffee, S. R., & Price, T. S. (2008). Genotype-Environment Correlations: Implications for Determining the Relationship between Environmental Exposures and Psychiatric Illness. Psychiatry, 7(12), 496–499.Google Scholar
Johnson, R. C., Nelson, G. W., Troyer, J. L., Lautenberger, J. A., Kessing, B. D., Winkler, C. A., & O’Brien, S. J. (2010). Accounting for Multiple Comparisons in a Genome-Wide Association Study (GWAS). BMC Genomics, 11, 724.Google Scholar
Jorde, L. B. (2000). Linkage Disequilibrium and the Search for Complex Disease Genes. Genome Research, 10(10), 1435–1444.Google Scholar
Kendler, K. S., & Baker, J. H. (2007). Genetic Influences on Measures of the Environment: A Systematic Review. Psychological Medicine, 37(5), 615–626.Google Scholar
Kendler, K. S., Neale, M. C., Kessler, R. C., Heath, A. C., & Eaves, L. J. (1993). A Test of the Equal-Environment Assumption in Twin Studies of Psychiatric Illness. Behavior Genetics, 23(1), 21–27.Google Scholar
Kendler, K. S., Gatz, M., Gardner, C. O., & Pedersen, N. L. (2006). A Swedish National Twin Study of Lifetime Major Depression. American Journal of Psychiatry, 163(1), 109–114.Google Scholar
Kieseppä, T., Partonen, T., Haukka, J., Kaprio, J., & Lönnqvist, J. (2004). High Concordance of Bipolar I Disorder in a Nationwide Sample of Twins. American Journal of Psychiatry, 161(10), 1814–1821.Google Scholar
Kirov, G., Pocklington, A. J., Holmans, P., Ivanov, D., Ikeda, M., Ruderfer, D., … Böttcher, Y. (2012). De Novo CNV Analysis Implicates Specific Abnormalities of Postsynaptic Signalling Complexes in the Pathogenesis of Schizophrenia. Molecular Psychiatry, 17(2), 142–153.Google Scholar
Kwon, J. M., & Goate, A. M. (2000). The Candidate Gene Approach. Alcohol Research & Health, 24(3), 164‒168.Google Scholar
Lee, S. H., Ripke, S., Neale, B. M., Faraone, S. V., Purcell, S. M., Perlis, R. H., … Asherson, P. (2013). Genetic Relationship between Five Psychiatric Disorders Estimated from Genome-Wide SNPs. Nature Genetics, 45(9), 984–994.Google Scholar
Li, M. D., Cheng, R., Ma, J. Z., & Swan, G. E. (2003). A Meta-Analysis of Estimated Genetic and Environmental Effects on Smoking Behavior in Male and Female Adult Twins. Addiction, 98(1), 23–31.Google Scholar
Maes, H. H. M., Neale, M. C., Kendler, K. S., Hewitt, J. K., Silberg, J. L., Foley, D. L., … Eaves, L. J. (1998). Assortative Mating for Major Psychiatric Diagnoses in Two Population-Based Samples. Psychological Medicine, 28(6), 1389–1401.Google Scholar
Maher, B. (2008). Personal Genomes: The Case of the Missing Heritability. Nature, 456(7218), 18–21.Google Scholar
Malhotra, D., & Sebat, J. (2012). CNVs: Harbingers of a Rare Variant Revolution in Psychiatric Genetics. Cell, 148(6), 1223–1241.Google Scholar
Manolio, T. A., Collins, F. S., Cox, N. J., Goldstein, D. B., Hindorff, L. A., Hunter, D. J., … Boehnke, M. (2009). Finding the Missing Heritability of Complex Diseases. Nature, 461(7265), 747–753.Google Scholar
Mazzeo, S. E., Mitchell, K. S., Bulik, C. M., Aggen, S. H., Kendler, K. S., & Neale, M. C. (2010). A Twin Study of Specific Bulimia Nervosa Symptoms. Psychological Medicine, 40(7), 1203–1213.Google Scholar
McClellan, J., & King, M.-C. (2010). Genetic Heterogeneity in Human Disease. Cell, 141(2), 210–217.Google Scholar
McGue, M., Keyes, M., Sharma, A., Elkins, I., Legrand, L., Johnson, W., & Iacono, W. (2007). The Environments of Adopted and Non-Adopted Youth: Evidence on Range Restriction from the Sibling Interaction and Behavior Study (SIBS). Behavior Genetics, 37(3), 449–462.Google Scholar
McGuffin, P., Rijsdijk, F., Andrew, M., Sham, P., Katz, R., & Cardno, A. (2003). The Heritability of Bipolar Affective Disorder and the Genetic Relationship to Unipolar Depression. Archives of General Psychiatry, 60(5), 497–502.Google Scholar
Mühleisen, T. W., Leber, M., Schulze, T. G., Strohmaier, J., Degenhardt, F., Treutlein, J., … Cichon, S. (2014). Genome-Wide Association Study Reveals Two New Risk Loci for Bipolar Disorder. Nature Communications, 5, 3339.Google Scholar
Munafò, M. R. (2006). Candidate Gene Studies in the 21st Century: Meta-Analysis, Mediation, Moderation. Genes, Brain & Behavior, 5, 3–8.Google Scholar
Neale, M., & Cardon, L. (2013). Methodology for Genetic Studies of Twins and Families. New York: Springer Science & Business Media.Google Scholar
Nikolas, M. A., & Alexandra, S. (2010). Genetic and Environmental Influences on ADHD Symptom Dimensions of Inattention and Hyperactivity: A Meta-Analysis. Journal of Abnormal Psychology, 119(1), 1–17.Google Scholar
Otowa, T., Hek, K., Lee, M., Byrne, E. M., Mirza, S. S., Nivard, M. G., … Hettema, J. M. (2016). Meta-Analysis of Genome-Wide Association Studies of Anxiety Disorders. Molecular Psychiatry, 21(10), 1391–1399.Google Scholar
Phillips, P. C. (2008). Epistasis ‒ The Essential Role of Gene Interactions in the Structure and Evolution of Genetic Systems. Nature Reviews Genetics, 9(11), 855–867.Google Scholar
Plomin, R., & Bergeman, C. S. (1991). The Nature of Nurture: Genetic Influence on “Environmental” Measures. Behavioral and Brain Sciences, 14(3), 373–386.Google Scholar
Plomin, R., DeFries, J. C., & Loehlin, J. C. (1977). Genotype-Environment Interaction and Correlation in the Analysis of human behavior. Psychological Bulletin, 84(2), 309–322.Google Scholar
Plomin, R., Owen, M. J., & McGuffin, P. (1994). The Genetic Basis of Complex Human Behaviors. Science, 264(5166), 1733–1739.Google Scholar
Plomin, R., DeFries, J. C., Knopik, V. S., & Neiderheiser, J. (2013). Behavioral Genetics (6th edn.). New York: Worth Publishers.Google Scholar
Polderman, T. J. C., Benyamin, B., de Leeuw, C. A., Sullivan, P. F., van Bochoven, A., Visscher, P. M., & Posthuma, D. (2015). Meta-Analysis of the Heritability of Human Traits Based on Fifty Years of Twin Studies. Nature Genetics, 47(7), 702–709.Google Scholar
Power, R. A., Steinberg, S., Bjornsdottir, G., Rietveld, C. A., Abdellaoui, A., Nivard, M. M., … Stefansson, K. (2015). Polygenic Risk Scores for Schizophrenia and Bipolar Disorder Predict Creativity. Nature Neuroscience, 18(7), 953–955.Google Scholar
Purcell, S. M., Wray, N. R., Stone, J. L., Visscher, P. M., O’Donovan, M. C., Sullivan, P. F., … Moran, J. L. (2009). Common Polygenic Variation Contributes to Risk of Schizophrenia and Bipolar Disorder. Nature, 460(7256), 748–752.Google Scholar
Reich, D. E., & Lander, E. S. (2001). On the Allelic Spectrum of Human Disease. Trends in Genetics, 17(9), 502–510.Google Scholar
Ripke, S., O’Dushlaine, C., Chambert, K., Moran, J. L., Kähler, A. K., Akterin, S., … Sullivan, P. F. (2014). Genome-Wide Association Analysis Identifies 13 New Risk Loci for Schizophrenia. Nature Genetics, 45(10), 1150–1159.Google Scholar
Robinson, M. R., Wray, N. R., & Visscher, P. M. (2014). Explaining Additional Genetic Variation in Complex Traits. Trends in Genetics, 30(4), 124–132.Google Scholar
Sanders, S. J., Ercan-Sencicek, A. G., Hus, V., Luo, R., Murtha, M. T., Moreno-De-Luca, D., … State, M. W. (2011). Multiple Recurrent De Novo CNVs, Including Duplications of the 7q11.23 Williams Syndrome Region, Are Strongly Associated with Autism. Neuron, 70(5), 863–885.Google Scholar
Schizophrenia Psychiatric Genome-Wide Association Study (GWAS) Consortium. (2011). Genome-Wide Association Study Identifies Five New Schizophrenia Loci. Nature Genetics, 43(10), 969–976.Google Scholar
Schizophrenia Working Group of the Psychiatric Genomics Consortium. (2014). Biological Insights from 108 Schizophrenia-Associated Genetic Loci. Nature, 511(7510), 421–427.Google Scholar
Schwekendiek, D. (2009). Height and Weight Differences between North and South Korea. Journal of Biosocial Science, 41(1), 51–55.Google Scholar
Scriver, C. R. (2007). The PAH Gene, Phenylketonuria, and a Paradigm Shift. Human Mutation, 28(9), 831–845.Google Scholar
Sebat, J., Lakshmi, B., Troge, J., Alexander, J., Young, J., Lundin, P., … Wigler, M. (2004). Large-Scale Copy Number Polymorphism in the Human Genome. Science, 305(5683), 525–528.Google Scholar
Shao, H., Burrage, L. C., Sinasac, D. S., Hill, A. E., Ernest, S. R., O’Brien, W., … Nadeau, J. H. (2008). Genetic Architecture of Complex Traits: Large Phenotypic Effects and Pervasive Epistasis. Proceedings of the National Academy of Sciences of the United States of America, 105(50), 19910–19914.Google Scholar
Stefansson, H., Rujescu, D., Cichon, S., Pietiläinen, O. P. H., Ingason, A., Steinberg, S., … Sigurdsson, A. (2008). Large Recurrent Microdeletions Associated with Schizophrenia. Nature, 455(7210), 232–236.Google Scholar
Stone, J. L., O’Donovan, M. C., Gurling, H., Kirov, G. K., Blackwood, D. H. R., Corvin, A., … Macgregor, S. (2008). Rare Chromosomal Deletions and Duplications Increase Risk of Schizophrenia. Nature, 455(7210), 237–241.Google Scholar
Stoolmiller, M. (1999). Implications of the Restricted Range of Family Environments for Estimates of Heritability and Nonshared Environment in Behavior-Genetic Adoption Studies. Psychological Bulletin, 125(4), 392–409.Google Scholar
Stranger, B. E., Stahl, E. A., & Raj, T. (2011). Progress and Promise of Genome-Wide Association Studies for Human Complex Trait Genetics. Genetics, 187(2), 367–383.Google Scholar
Stulp, G., & Barrett, L. (2016). Evolutionary Perspectives on Human Height Variation. Biological Reviews, 91(1), 206–234.Google Scholar
Sullivan, P. F., Neale, M. C., & Kendler, K. S. (2000). Genetic Epidemiology of Major Depression: Review and Meta-Analysis. American Journal of Psychiatry, 157(10), 1552–1562.Google Scholar
Sullivan, P. F., Kendler, K. S., & Neale, M. C. (2003). Schizophrenia as a Complex Trait: Evidence from a Meta-Analysis of Twin Studies. Archives of General Psychiatry, 60(12), 1187–1192.Google Scholar
Taylor, S. (2011). Etiology of Obsessions and Compulsions: A Meta-Analysis and Narrative Review of Twin Studies. Clinical Psychology Review, 31(8), 1361–1372.Google Scholar
Tenesa, A., & Haley, C. S. (2013). The Heritability of Human Disease: Estimation, Uses and Abuses. Nature Reviews Genetics, 14(2), 139–149.Google Scholar
Tick, B., Bolton, P., Happe, F., Rutter, M., & Rijsdijk, F. (2016). Heritability of Autism Spectrum Disorders: A Meta-Analysis of Twin Studies. Journal of Child Psychology and Psychiatry, 57(5), 585–595.Google Scholar
Turkheimer, E. (2000). Three Laws of Behavior Genetics and What They Mean. Current Directions in Psychological Science, 9(5), 160–164.Google Scholar
Van Winkel, R. (2011). Family-Based Analysis of Genetic Variation Underlying Psychosis-Inducing Effects of Cannabis: Sibling Analysis and Proband Follow-Up. Archives of General Psychiatry, 68(2), 148–157.Google Scholar
Verhulst, B., Neale, M. C., & Kendler, K. S. (2015). The Heritability of Alcohol Use Disorders: A Meta-Analysis of Twin and Adoption Studies. Psychological Medicine, 45(5), 1061–1072.Google Scholar
Verweij, K. J. H., Zietsch, B. P., Lynskey, M. T., Medland, S. E., Neale, M. C., Martin, N. G., … Vink, J. M. (2010). Genetic and Environmental Influences on Cannabis Use Initiation and Problematic Use: A Meta-Analysis of Twin Studies. Addiction, 105(3), 417–430.Google Scholar
Weaver, I. C. G., Cervoni, N., Champagne, F. A., D’Alessio, A. C., Sharma, S., Seckl, J. R., … Meaney, M. J. (2004). Epigenetic Programming by Maternal Behavior. Nature Neuroscience, 7(8), 847–854.Google Scholar
Weiss, L. A., Shen, Y., Korn, J. M., Arking, D. E., Miller, D. T., Fossdal, R., … Daly, M. J. (2008). Association between Microdeletion and Microduplication at 16p11.2 and Autism. New England Journal of Medicine, 358(7), 667–675.Google Scholar
Wilson, B. J., & Nicholls, S. G. (2015). The Human Genome Project, and Recent Advances in Personalized Genomics. Risk Management and Healthcare Policy, 8, 9–20.Google Scholar
Wood, A. R., Esko, T., Yang, J., Vedantam, S., Pers, T. H., Gustafsson, S., … Frayling, T. M. (2014). Defining the Role of Common Variation in the Genomic and Biological Architecture of Adult Human Height. Nature Genetics, 46(11), 1173–1186.Google Scholar
Yang, J., Lee, S. H., Goddard, M. E., & Visscher, P. M. (2011a). GCTA: A Tool for Genome-Wide Complex Trait Analysis. The American Journal of Human Genetics, 88(1), 76–82.Google Scholar
Yang, J., Weedon, M. N., Purcell, S., Lettre, G., Estrada, K., Willer, C. J., … Goddard, M. E. (2011b). Genomic Inflation Factors under Polygenic Inheritance. European Journal of Human Genetics, 19(7), 807–812.Google Scholar
Yengo, L., Sidorenko, J., Kemper, K. E., Zheng, Z., Wood, A. R., Weedon, M. N., … Consortium, G. (2018). Meta-Analysis of Genome-Wide Association Studies for Height and Body Mass Index in ~700,000 Individuals of European Ancestry. BioRxiv, 274654.Google Scholar
Zammit, S., Spurlock, G., Williams, H., Norton, N., Williams, N., O’Donovan, M. C., & Owen, M. J. (2007). Genotype Effects of CHRNA7, CNR1 and COMT in Schizophrenia: Interactions with Tobacco and Cannabis Use. The British Journal of Psychiatry, 191(5), 402–407.Google Scholar
Zondervan, K. T., & Cardon, L. R. (2004). The Complex Interplay among Factors that Influence Allelic Association. Nature Reviews Genetics, 5(2), 89–100.Google Scholar
Zuk, O., Hechter, E., Sunyaev, S. R., & Lander, E. S. (2012). The Mystery of Missing Heritability: Genetic Interactions Create Phantom Heritability. Proceedings of the National Academy of Sciences of the United States of America, 109(4), 1193–1198.Google Scholar

Reference

Amaro, E. Jr., & Barker, G. J. (2006). Study Design in fMRI: Basic Principles. Brain and Cognition, 60(3), 220‒232.Google Scholar
Anderson, A. W., Heptulla, R. A., Driesen, N., Flanagan, D., Goldberg, P. A., Jones, T. W., … Gore, J. C. (2006). Effects of Hypoglycemia on Human Brain Activation Measured with fMRI. Magnetic Resonance Imaging, 24, 693‒697.Google Scholar
Anderson, K. M., Krienen, F. M., Choi, E. Y., Reinen, J. M., Yeo, B. T. T., & Holmes, A. J. (2018). Gene Expression Links Functional Networks across Cortex and Striatum. Nature Communications, 9, 1428.Google Scholar
Baker, J. T., Holmes, A. J., Masters, G. A., Yeo, B. T. T., Krienen, F. M., Buckner, R. L., & Öngür, D. (2014). Disruption of Cortical Association Networks in Schizophrenia and Psychotic Bipolar Disorder. JAMA Psychiatry, 71(2), 109‒118.Google Scholar
Barch, D. M., Burgess, G. C., Harms, M. P., Petersen, S. E., Schlaggar, B. L., Corbetta, M., … Van Essen, D. C. (2013). Function in the Human Connectome: Task-fMRI and Individual Differences in Behavior. NeuroImage, 80, 169‒189.Google Scholar
Biswal, B. B., Mennes, M., Zuo, X.-N., Gohel, S., Kelly, C., Smith, S. M., … Milham, M. P. (2010). Toward Discovery Science of Human Brain Function. Proceedings of the National Academy of Sciences, 107(10), 4734‒4739.Google Scholar
Biswal, B. B., Yetkin, F. Z., Haughton, V. M., & Hyde, J. S. (1995). Functional Connectivity in the Motor Cortex of Resting Human Brain Using Echo-Planar MRI. Magnetic Resonance in Medicine, 34(4), 537‒541.Google Scholar
Braver, T. S., Reynolds, J. R., & Donaldson, D. I. (2003). Neural Mechanisms of Transient and Sustained Cognitive Control during Task Switching. Neuron, 39(4), 713‒726.Google Scholar
Breiter, H. C., Etcoff, N. L., Whalen, P. J., Kennedy, W. A., Rauch, S. L., Buckner, R. L., … Rosen, B. R. (1996). Response and Habituation of the Human Amygdala during Visual Processing of Facial Expression. Neuron, 17(5), 875‒887.Google Scholar
Buckholtz, J. W., & Meyer-Lindenberg, A. (2012). Psychopathology and the Human Connectome: Toward a Transdiagnostic Model of Risk for Mental Illness. Neuron, 74(6), 990‒1004.Google Scholar
Calhoun, V. D., Adali, T., Pearlson, G. D., & Pekar, J. J. (2001). A Method for Making Group Inferences from Functional MRI Data Using Independent Component Analysis. Human Brain Mapping, 14, 140‒151.Google Scholar
Chekroud, A. M., Ward, E. J., Rosenberg, M. D., & Holmes, A. J. (2016). Patterns in the Human Brain Mosaic Discriminate Males from Females. Proceedings of the National Academy of Sciences, 113(14), E1968.Google Scholar
Ciric, R., Wolf, D. H., Power, J. D., Roalf, D. R., Baum, G. L., Ruparel, K., … Satterthwaite, T. D. (2018). Benchmarking of Participant-Level Confound Regression Strategies for the Control of Motion Artifact in Studies of Functional Connectivity. NeuroImage, 154, 174–187.Google Scholar
Cole, M. W., Repov, G., & Anticevic, A. (2014). The Frontoparietal Control System: A Central Role in Mental Health. The Neuroscientist, 20(6), 652‒664.Google Scholar
Crossley, N. A., Mechelli, A., Vértes, P. E., Winton-Brown, T. T., Patel, A. X., Ginestet, C. E., … Bullmore, E. T. (2013). Cognitive Relevance of the Community Structure of the Human Brain Functional Coactivation Network. Proceedings of the National Academy of Sciences of the United States of America, 110(28), 11583‒11588.Google Scholar
Dale, A. M., & Buckner, R. L. (1997). Selective Averaging of Rapidly Presented Individual Trials Using fMRI. Human Brain Mapping, 5(5), 329‒340.Google Scholar
Deco, G., Jirsa, V. K., & McIntosh, A. R. (2011). Emerging Concepts for the Dynamical Organization of Resting-State Activity in the Brain. Nature Reviews Neuroscience, 12(1), 43‒56.Google Scholar
Deng, C., Yuan, H., & Dai, J. (2018). Behavioral Manipulation by Optogenetics in the Nonhuman Primate. The Neuroscientist; 24(5), 526‒539.Google Scholar
Farkas, T., Wolf, A. P., Jaeger, J., Brodie, J. D., Christman, D. R., & Fowler, J. S. (1984). Regional Brain Glucose Metabolism in Chronic Schizophrenia: A Positron Emission Transaxial Tomographic Study. Archives of General Psychiatry, 41(3), 293‒300.Google Scholar
Fox, P. T., Mintun, M. A., Reiman, E. M., & Raichle, M. E. (1988). Enhanced Detection of Focal Brain Responses Using Intersubject Averaging and Change-Distribution Analysis of Subtracted PET Images. Journal of Cerebral Blood Flow and Metabolism, 8(5), 642‒653.Google Scholar
Fox, P. T., Parsons, L. M., & Lancaster, J. L. (1998). Beyond the Single Study: Function/Location Metanalysis in Cognitive Neuroimaging. Current Opinion in Neurobiology, 8(2), 178‒187.Google Scholar
Friston, K.J., Frith, C. D., Liddle, P. F., & Frackowiak, R. S. J. (1993). Functional Connectivity: The Principal-Component Analysis of Large (PET) Data Sets. Journal of Cerebral Blood Flow and Metabolism, 13(1), 5‒14.Google Scholar
Friston, K. J., Holmes, A. P., Worsley, K. J., Poline, J.-P., Frith, C. D., & Frackowiak, R. S. J. (1994). Statistical Parametric Maps in Functional Imaging: A General Linear Approach. Human Brain Mapping, 2(4), 189‒210.Google Scholar
Friston, K. J., Buechel, C., Fink, G. R., Morris, J., Rolls, E., & Dolan, R. J. (1997). Psychophysiological and Modulatory Interactions in Neuroimaging. Neuroimage, 6(3), 218‒229.Google Scholar
Friston, K. J., Harrison, L., & Penny, W. (2003). Dynamic Causal Modelling. Neuroimage, 19(4), 1273‒1302.Google Scholar
Funahashi, S., Bruce, C. J., & Goldman-Rakic, P. S. (1989). Mnemonic Coding of Visual Space in the Monkey’s Dorsolateral Prefrontal Cortex. Journal of Neurophysiology, 61(2), 331‒349.Google Scholar
Ge, T., Holmes, A. J., Buckner, R. L., Smoller, J. W., & Sabuncu, M. R. (2018). Heritability Analysis with Repeat Measurements and Its Application to Resting-State Functional Connectivity. Proceedings of the National Academy of Sciences, 114(21), 5521‒5526.Google Scholar
Gee, D. G., Gabard-Durnam, L. J., Flannery, J., Goff, B., Humphreys, K. L., Telzer, E. H., … Tottenham, N. (2013). Early Developmental Emergence of Human Amygdala-Prefrontal Connectivity after Maternal Deprivation. Proceedings of the National Academy of Sciences of the United States of America, 110(39), 15638‒15643.Google Scholar
Glasser, M. F., Coalson, T. S., Robinson, E. C., Hacker, C. D., Harwell, J., Yacoub, E., … Van Essen, D. C. (2016). A Multi-Modal Parcellation of Human Cerebral Cortex. Nature, 536(7615), 171‒178.Google Scholar
Goldstein, R. Z., Leskovjan, A. C., Hoff, A. L., Hitzemann, R., Bashan, F., Khalsa, S. S., … Volkow, N. D. (2004). Severity of Neuropsychological Impairment in Cocaine and Alcohol Addiction: Association with Metabolism in the Prefrontal Cortex. Neuropsychologia, 42(11), 1447‒1458.Google Scholar
Goodwin, G. M. (1997). Neuropsychological and Neuroimaging Evidence for the Involvement of the Frontal Lobes in Depression. Journal of Psychopharmacology, 11(2), 115‒122.Google Scholar
Gur, R. C., & Gur, R. E. (1995). Hypofrontality in Schizophrenia: RIP. Lancet, 345(8962), 1338–1340.Google Scholar
Haut, K. M., Lim, K. O., & MacDonald, A. (2010). Prefrontal Cortical Changes following Cognitive Training in Patients with Chronic Schizophrenia: Effects of Practice, Generalization, and Specificity. Neuropsychopharmacology, 35(9), 1850‒1859.Google Scholar
Henson, R. (2006). Forward Inference Using Functional Neuroimaging: Dissociations versus Associations. Trends in Cognitive Sciences, 10(2), 64‒69.Google Scholar
Holmes, A. J., & Patrick, L. M. (2018). The Myth of Optimality in Clinical Neuroscience. Trends in Cognitive Sciences, 22(3), 241‒257.Google Scholar
Holmes, A. J., & Yeo, B. T. T. (2015). From Phenotypic Chaos to Neurobiological Order. Nature Neuroscience, 18(11), 1532‒1534.Google Scholar
Holmes, A. J., Hollinshead, M. O., O’Keefe, T. M., Petrov, V. I., Fariello, G. R., Wald, L. L., … Buckner, R. L. (2015). Brain Genomics Superstruct Project Initial Data Release with Structural, Functional, and Behavioral Measures. Scientific Data, 2, 150031.Google Scholar
Huettel, S. A. (2012). Event-Related fMRI in Cognition. Neuroimage, 62(2), 1152‒1156.Google Scholar
Huettel, S. A., Song, A. W., & McCarthy, G. (2004). Functional Magnetic Resonance Imaging (Vol. 1). Sunderland, MA: Sinauer Associates Sunderland.Google Scholar
Hutchison, R. M., Womelsdorf, T., Allen, E. A., Bandettini, P. A., Calhoun, V. D., Corbetta, M., … Chang, C. (2013). Dynamic Functional Connectivity: Promise, Issues, and Interpretations. NeuroImage, 80, 360‒378.Google Scholar
Ingvar, D. H., & Franzen, G. (1974). Distribution of Cerebral Activity in Chronic Schizophrenia. Lancet, 304(7895), 1484‒1486.Google Scholar
Kamiński, M., Ding, M., Truccolo, W. A., & Bressler, S. L. (2001). Evaluating Causal Relations in Neural Systems: Granger Causality, Directed Transfer Function and Statistical Assessment of Significance. Biological Cybernetics, 85(2), 145‒157.Google Scholar
Koch, M. A., Norris, D. G., & Hund-Georgiadis, M. (2002). An Investigation of Functional and Anatomical Connectivity Using Magnetic Resonance Imaging. Neuroimage, 16(1), 241‒250.Google Scholar
Kong, R., Li, J., Sun, N., Sabuncu, M. R., Schaefer, A., Scholz, M., … Yeo, B. T. T. (2019). Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality and Emotion. Cerebral Cortex, 29(6), 2533–2551.Google Scholar
Krienen, F. M., Yeo, B. T. T., & Buckner, R. L. (2014). Reconfigurable Task-Dependent Functional Coupling Modes Cluster around a Core Functional Architecture. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 369(1653), 20130526.Google Scholar
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(4), 971‒977.Google Scholar
Laird, A. R., Mickle Fox, P., Price, C. J., Glahn, D. C., Uecker, A. M., Lancaster, J. L., … Fox, P. T. (2005). ALE Meta-Analysis: Controlling the False Discovery Rate and Performing Statistical Contrasts. Human Brain Mapping, 25(1), 155‒164.Google Scholar
Laurienti, P. J., Field, A. S., Burdette, J. H., Maldjian, J. A., Yen, Y.-F., & Moody, D. M. (2002). Dietary Caffeine Consumption Modulates fMRI Measures. Neuroimage, 17, 751‒757.Google Scholar
Logothetis, N. K., Pauls, J., Augath, M., Trinath, T., & Oeltermann, A. (2001). Neurophysiological Investigation of the Basis of the fMRI Signal. Nature, 412, 150‒157.Google Scholar
MacDonald, A. W., Becker, T. M., & Carter, C. S. (2006). Functional Magnetic Resonance Imaging Study of Cognitive Control in the Healthy Relatives of Schizophrenia Patients. Biological Psychiatry, 60(11), 1241‒1249.Google Scholar
MacDonald, A. W. (2015). Differential Deficit. In Cautin, R. & Lilienfeld, S. (Eds.), The Encyclopedia of Clinical Psychology (1st edn.). Hoboken, NJ: John Wiley.Google Scholar
Mayberg, H. S. (1997). Limbic-Cortical Dysregulation: A Proposed Model of Depression. Journal of Neuropsychiatry and Clinical Neurosciences, 9(3), 471‒481.Google Scholar
McIntosh, A. R., & Gonzalez-Lima, F. (1991). Structural Modeling of Functional Neural Pathways Mapped with 2-Deoxyglucose: Effects of Acoustic Startle Habituation on the Auditory System. Brain Research, 547(2), 295‒302.Google Scholar
McKeown, M. J., & Sejnowski, T. J. (1998). Independent Component Analysis of fMRI Data: Examining the Assumptions. Human Brain Mapping, 6, 368‒372.Google Scholar
Monti, M. M. (2011). Statistical Analysis of fMRI Time-Series: A Critical Review of the GLM Approach. Frontiers in Human Neuroscience, 5(28), 1‒13.Google Scholar
Ollier, W., Sprosen, T., & Peakman, T. (2005). UK Biobank: From Concept to Reality. Pharmacogenomics, 6(6), 639‒646.Google Scholar
Ollinger, J. M., Shulman, G. L., & Corbetta, M. (2001). Separating Processes Within a Trial in Event-Related Functional MRI I: The Method. NeuroImage, 13(1), 210‒217.Google Scholar
Park, S., Holzman, P. S., & Goldman-Rakic, P. S. (1995). Spatial Working Memory Deficits in the Relatives of Schizophrenic Patients. Archives of General Psychiatry, 52(10), 821‒828.Google Scholar
Penny, W. D., Stephan, K. E., Mechelli, A., & Friston, K. J. (2004). Modelling Functional Integration: A Comparison of Structural Equation and Dynamic Causal Models. Neuroimage, 23(Suppl. 1), S264–274.Google Scholar
Poldrack, R. A. (2006). Can Cognitive Processes Be Inferred from Neuroimaging Data? Trends in Cognitive Sciences, 10(2), 59‒63.Google Scholar
Poldrack, R. A. (2010). Mapping Mental Function to Brain Structure: How Can Cognitive Neuroimaging Succeed? Perspectives on Psychological Science, 5, 753‒761.Google Scholar
Poppe, A. B., Wisner, K., Atluri, G., Lim, K. O., Kumar, V., & MacDonald, A. W. (2013). Toward a Neurometric Foundation for Probabilistic Independent Component Analysis of fMRI Data. Cognitive, Affective, & Behavioral Neuroscience, 13(3), 641‒659.Google Scholar
Poppe, A. B., Barch, D. M., Carter, C. S., Gold, J. M., Ragland, J. D., Silverstein, S. M., & MacDonald, A. W. (2016). Reduced Frontoparietal Activity in Schizophrenia Is Linked to a Specific Deficit in Goal Maintenance: A Multisite Functional Imaging Study. Schizophrenia Bulletin, 42(5), 1149‒1157.Google Scholar
Power, J. D., Cohen, A. L., Nelson, S. M., Wig, G. S., Barnes, K. A., Church, J. A., … Petersen, S E. (2011). Functional Network Organization of the Human Brain. Neuron, 72(4), 665–678.Google Scholar
Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2014). Methods to Detect, Characterize, and Remove Motion Artifact in Resting State fMRI. NeuroImage, 84, 320‒341.Google Scholar
Price, J. L., & Drevets, W. C. (2012). Neural Circuits Underlying the Pathophysiology of Mood Disorders. Trends in Cognitive Sciences, 16(1), 61‒71.Google Scholar
Raichle, M. E. (2009). A Brief History of Human Brain Mapping. Trends in Neurosciences, 32(2), 118‒126.Google Scholar
Reinen, J. M., Chen, O. Y., Hutchison, R. M., Yeo, B. T. T., Anderson, K. M., Sabuncu, M. R., … Holmes, A. J. (2018). The Human Cortex Possesses a Reconfigurable Dynamic Network Architecture That Is Disrupted in Psychosis. Nature Communications, 9, 1157.Google Scholar
Richiardi, J., Altmann, A., Milazzo, A.-C., Chang, C., Chakravarty, M. M., Banaschewski, T., … Greicius, M. D. (2015). Correlated Gene Expression Supports Synchronous Activity in Brain Networks. Science, 348(6240), 1241‒1244.Google Scholar
Rosenberg, M. D., Finn, E. S., Scheinost, D., Papademetris, X., Shen, X., Constable, R. T., & Chun, M. M. (2016). A Neuromarker of Sustained Attention from Whole-Brain Functional Connectivity. Nature Neuroscience, 19(1), 165‒171.Google Scholar
Rosenberg, M. D., Casey, B. J., & Holmes, A. J. (2018). Prediction Complements Explanation in Understanding the Developing Brain. Nature Communications, 9, 589.Google Scholar
Salem, J. E., Kring, A. M., & Kerr, S. L. (1996). More Evidence for Generalized Poor Performance in Facial Emotion Expression in Schizophrenia. Journal of Abnormal Psychology, 105(3), 480‒483.Google Scholar
Schaefer, A., Kong, R., Gordon, E. M., Laumann, T. O., Zuo, X.-N., Holmes, A., … Yeo, B. T. (2018). Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. Cerebral Cortex, 28, 3095‒3114.Google Scholar
Seifritz, E., Bilecen, D., Hänggi, D., Haselhorst, R., Radü, E. W., Wetzel, S., … Scheffler, K. (2000). Effect of Ethanol on BOLD Response to Acoustic Stimulation: Implications for Neuropharmacological fMRI. Psychiatry Research Neuroimaging, 99(1), 1‒13.Google Scholar
Sepulcre, J., Liu, H., Talukdar, T., Martincorena, I., Yeo, B. T. T., & Buckner, R. L. (2010). The Organization of Local and Distant Functional Connectivity in the Human Brain. PLoS Computational Biology, 6(6), e1000808.Google Scholar
Shen, X., Tokoglu, F., Papademetris, X., & Constable, R. T. (2013). Groupwise Whole-Brain Parcellation from Resting-State fMRI Data for Network Node Identification. Neuroimage, 82, 403‒415.Google Scholar
Shmueli, K., van Gelderen, P., de Zwart, J. A., Horovitz, S. G., Fukunaga, M., Jansma, J. M., & Duyn, J. H. (2007). Low-Frequency Fluctuations in the Cardiac Rate as a Source of Variance in the Resting-State fMRI BOLD Signal. Neuroimage, 38(2), 306‒320.Google Scholar
Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Mickle Fox, P., Mackay, C. E., … Beckmann, C. F. (2009). Correspondence of the Brain’s Functional Architecture during Activation and Rest. Proceedings of the National Academy of Sciences, 106(31), 13040–13045.Google Scholar
Smith, S. M., Vidaurre, D., Beckmann, C. F., Glasser, M. F., Jenkinson, M., Miller, K. L., … Van Essen, D. C. (2013). Functional Connectomics from Resting-State fMRI. Trends in Cognitive Sciences, 17(12), 666‒682.Google Scholar
Smoller, J. W., Gallagher, P. J., Duncan, L. E., McGrath, L. M., Haddad, S. A., Holmes, A. J., … Cohen, B. M. (2014). The Human Ortholog of Acid-Sensing Ion Channel Gene ASIC1a Is Associated with Panic Disorder and Amygdala Structure and Function. Biological Psychiatry, 76(11), 902‒910.Google Scholar
Sporns, O. (2014). Contributions and Challenges for Network Models in Cognitive Neuroscience. Nature Neuroscience, 17(5), 652‒660.Google Scholar
Sprooten, E., Rasgon, A., Goodman, M., Carlin, A., Leibu, E., Lee, W. H., & Frangou, S. (2018). Addressing Reverse Inference in Psychiatric Neuroimaging: Meta-Analyses of Task-Related Brain Activation in Common Mental Disorders. Human Brain Mapping, 38(4), 1846‒1864.Google Scholar
Tavor, I., Parker Jones, O., Mars, R. B., & Smith, S. M. (2016). Task-Free MRI Predicts Individual Differences in Brain Activity during Task Performance. Science, 352(6282), 216–220.Google Scholar
Thiel, C. M., & Fink, G. R. (2007). Visual and Auditory Alertness: Modality-Specific and Supramodal Neural Mechanisms and Their Modulation by Nicotine. Journal of Neurophysiology, 97(4), 2758‒2768.Google Scholar
Van Dijk, K. R. A., Hedden, T., Venkataraman, A., Evans, K. C., Lazar, S. W., & Buckner, R. L. (2010). Intrinsic Functional Connectivity as a Tool for Human Connectomics: Theory, Properties, and Optimization. Journal of Neurophysiology, 103(1), 297‒321.Google Scholar
Van Dijk, K. R. A., Sabuncu, M. R., & Buckner, R. L. (2012). The Influence of Head Motion on Intrinsic Functional Connectivity MRI. Neuroimage, 59(1), 431‒438.Google Scholar
Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E. J., Yacoub, E., & Ugurbil, K. (2013). The WU-Minn Human Connectome Project: An Overview. Neuroimage, 80, 62–79.Google Scholar
Vincent, J. L., Snyder, A. Z., Fox, M. D., Shannon, B. J., Andrews, J. R., Raichle, M. E., & Buckner, R. L. (2006). Coherent Spontaneous Activity Identifies a Hippocampal-Parietal Memory Network. Journal of Neurophysiology, 96(6), 3517‒3531.Google Scholar
Viviani, R., Grön, G., & Spitzer, M., (2005). Functional Principal Component Analysis of fMRI Data. Human Brain Mapping, 24, 109‒129.Google Scholar
Wang, D., Buckner, R. L., Fox, M. D., Holt, D. J., Holmes, A. J., Stoecklein, S., … Liu, H. (2015). Parcellating Cortical Functional Networks in Individuals. Nature Neuroscience, 18(12), 1853‒1860.Google Scholar
Wisner, K. M., Atluri, G., Lim, K. O., & MacDonald, A. W. (2013). Neurometrics of Intrinsic Connectivity Networks at Rest Using fMRI: Retest Reliability and Cross-Validation Using a Meta-Level Method. Neuroimage, 76, 236‒251.Google Scholar
Yarkoni, T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C., & Wager, T. D. (2011). Large-Scale Automated Synthesis of Human Functional Neuroimaging Data. Nature Methods, 8(8), 665‒670.Google Scholar
Yeo, B. T. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., … Buckner, R. L. (2011). The Organization of the Human Cerebral Cortex Estimated by Intrinsic Functional Connectivity. Journal of Neurophysiology, 106(3), 1125‒1165.Google Scholar
Zang, Y., Jiang, T., Lu, Y., He, T., & Tian, L. (2004). Regional Homogeneity Approach to fMRI Data Analysis. NeuroImage, 22, 394‒400.Google Scholar
Zhang, X., Mormino, E. C., Sun, N., Sperling, R. A., Sabuncu, M. R., & Yeo, B. T. T. (2016). Bayesian Model Reveals Latent Atrophy Factors with Dissociable Cognitive Trajectories in Alzheimer’s Disease. Proceedings of the National Academy of Sciences of the United States of America, 113(42), E6535‒E6544.Google Scholar
Zuo, X.-N., Anderson, J. S., Bellec, P., Birn, R. M., Biswal, B. B., Blautzik, J., … Milham, M. P. (2014). An Open Science Resource for Establishing Reliability and Reproducibility in Functional Connectomics. Scientific Data, 1, 140049.Google Scholar

Further Reading

For an in-depth treatment of reinforcement learning, we recommend Sutton and Barto’s recently updated classic book, Reinforcement Learning: An Introduction (2018). The Oxford Handbook of Computational and Mathematical Psychology introduces the reader to cognitive modeling and contains Gureckis and Love’s superb chapter on reinforcement learning (2015). Excellent computational neuroscience texts include Miller’s Introductory Course in Computational Neuroscience (2018) and Dayan and Abbott’s Theoretical Neuroscience (2005). Miller covers useful preliminary material, including mathematics, circuit physics and even computing and MATLAB (much of existing code for reinforcement learning modeling is written in MATLAB, but R and Python are becoming increasingly popular). Dayan and Abbot treat conditioning and reinforcement learning in greater detail. A more detailed treatment of model-based cognitive neuroscience can be found in An Introduction to Model-Based Cognitive Neuroscience (Forstmann & Wagenmakers, 2015).

References

Bartra, O., McGuire, J. T., & Kable, J. W. (2013). The Valuation System: A Coordinate-Based Meta-Analysis of BOLD fMRI Experiments Examining Neural Correlates of Subjective Value. Neuroimage, 76, 412–427.Google Scholar
Bouret, S., & Richmond, B. J. (2010). Ventromedial and Orbital Prefrontal Neurons Differentially Encode Internally and Externally Driven Motivational Values in Monkeys. Journal of Neuroscience, 30(25), 8591–8601.Google Scholar
Bower, G. H. (1994). A Turning Point in Mathematical Learning Theory. Psychological Review, 101, 290–300.Google Scholar
Box, G. E. P. (1979). Robustness in the Strategy of Scientific Model Building. In Launer, R. L. & Wilkinson, G. N. (Eds.), Robustness in Statistics (pp. 201–236). New York: Academic Press.Google Scholar
Burnham, K. P., & Anderson, D. R. (2002). Model Selection and Multi-Model Inference: A Practical Information-Theoretic Approach (2nd edn.). New York: Springer.Google Scholar
Bush, R. R., & Mosteller, F. (1951). A Mathematical Model for Simple Learning. Psychological Review., 58, 313–323.Google Scholar
Cai, X., & Padoa-Schioppa, C. (2012). Neuronal Encoding of Subjective Value in Dorsal and Ventral Anterior Cingulate Cortex. Journal of Neuroscience, 32, 3791–3808.Google Scholar
Cavanagh, J. F. (2015). Cortical Delta Activity Reflects Reward Prediction Error and Related Behavioral Adjustments, but at Different Times. NeuroImage, 110, 205–216.Google Scholar
Cavanagh, J. F., Eisenberg, I., Guitart-Masip, M., Huys, Q., & Frank, M. J. (2013). Frontal Theta Overrides Pavlovian Learning Biases. Journal of Neuroscience, 33, 8541–8548.Google Scholar
Chase, H. W., Kumar, P., Eickhoff, S. B., & Dombrovski, A. Y. (2015). Reinforcement Learning Models and Their Neural Correlates: An Activation Likelihood Estimation Meta-Analysis. Cognitive, Affective, & Behavioral Neuroscience, 15(2), 435‒459.Google Scholar
Craver, C. F. (2001). Role Functions, Mechanisms, and Hierarchy. Philosophy of Science, 68, 53–74.Google Scholar
Critchley, H. D., & Rolls, E. T. (1996). Hunger and Satiety Modify the Responses of Olfactory and Visual Neurons in the Primate Orbitofrontal Cortex. Journal of Neurophysiology, 75(4), 1673–1686.Google Scholar
D’Ardenne, K., McClure, S. M., Nystrom, L. E., & Cohen, J. D. (2008). BOLD Responses Reflecting Dopaminergic Signals in the human Ventral Tegmental Area. Science, 319, 1264–1267.Google Scholar
Dayan, P., & Abbott, L. F. (2005). Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems (Revised edn.). Cambridge, MA: MIT Press.Google Scholar
Delgado, M. R., Frank, R. H., & Phelps, E. A. (2005). Perceptions of Moral Character Modulate the Neural Systems of Reward during the Trust Game. Nature Neuroscience, 8, 1611–1618.Google Scholar
Dombrovski, A. Y., Szanto, K., Clark, L., Reynolds, C. F., & Siegle, G. J. (2013). Reward Signals, Attempted Suicide, and Impulsivity in Late-Life Depression. JAMA Psychiatry, 70, 1020–1030.Google Scholar
Dombrovski, A. Y., Hallquist, M. N., Brown, V. M., Wilson, J., & Szanto, K. (2019). Value-Based Choice, Contingency Learning, and Suicidal Behavior in Mid- and Late-Life Depression. Biological Psychiatry, 85(6), 506–516.Google Scholar
Dreher, J.-C., & Tremblay, L. (Eds.) (2016). Decision Neuroscience: An Integrative Perspective (1st edn.). Amsterdam: Academic Press.Google Scholar
Elston, G. N. (2003). Cortex, Cognition and the Cell: New Insights into the Pyramidal Neuron and Prefrontal Function. Cerebral Cortex, 13(11), 1124–1138.Google Scholar
Estes, W.K., 1950. Toward a Statistical Theory of Learning. Psychological Review, 57, 94‒107.Google Scholar
Fiorillo, C. D., Newsome, W. T., & Schultz, W. (2008). The Temporal Precision of Reward Prediction in Dopamine Neurons. Nature Neuroscience, 11, 966–973.Google Scholar
Forstmann, B. U., & Wagenmakers, E.-J. (Eds.) (2015). An Introduction to Model-Based Cognitive Neuroscience. New York: Springer.Google Scholar
Fouragnan, E., Retzler, C., & Philiastides, M. G., 2018. Separate Neural Representations of Prediction Error Valence and Surprise: Evidence from an fMRI Meta-Analysis. Human Brain Mapping, 39, 2887–2906.Google Scholar
Gershman, S. J. (2016). Empirical Priors for Reinforcement Learning Models. Journal of Mathematical Psychology, 71, 1–6.Google Scholar
Glimcher, P. W. (2011). Understanding Dopamine and Reinforcement Learning: The Dopamine Reward Prediction Error Hypothesis. Proceedings of the National Academy of Sciences of the United States of America, 108(Suppl 3), 15647–15654.Google Scholar
Gureckis, T., & Love, B. (2015). Computational Reinforcement Learning. In The Oxford Handbook of Computational and Mathematical Psychology (pp. 99‒117). Oxford: Oxford University PressGoogle Scholar
Hallquist, M. N., & Dombrovski, A. Y., 2019. Selective Maintenance of Value Information Helps Resolve the Exploration/Exploitation Dilemma. Cognition, 183, 226–243.Google Scholar
Hertwig, R., & Erev, I., 2009. The Description-Experience Gap in Risky Choice. Trends in Cognitive Science, 13, 517–523.Google Scholar
Holroyd, C. B., & Krigolson, O. E., 2007. Reward Prediction Error Signals Associated with a Modified Time Estimation Task. Psychophysiology, 44, 913–917.Google Scholar
Hursh, S. R., & Silberberg, A. (2008). Economic Demand and Essential Value. Psychological Review, 115, 186–198.Google Scholar
Jocham, G., Neumann, J., Klein, T. A., Danielmeier, C., & Ullsperger, M. (2009). Adaptive Coding of Action Values in the Human Rostral Cingulate Zone. Journal of Neuroscience, 29, 7489–7496.Google Scholar
Kaelbling, L. P., Littman, M. L., & Moore, A. W., 1996. Reinforcement Learning: A Survey. Journal of Artificial Intelligence Research, 4, 237–285.Google Scholar
Kamin, L. J. (1968). Predictability, Surprise, Attention, and Conditioning. In Campbell, B. A. & Church, R. M. (Eds.), Punishment and Aversive Behavior (pp. 279–296). New York: Appleton-Century-Crofts.Google Scholar
Katahira, K. (2016). How Hierarchical Models Improve Point Estimates of Model Parameters at the Individual Level. Journal of Mathematical Psychology, 73, 37–58.Google Scholar
Kennerley, S. W., & Wallis, J. D. (2009). Evaluating Choices by Single Neurons in the Frontal Lobe: Outcome Value Encoded across Multiple Decision Variables. European Journal of Neuroscience, 29, 2061–2073.Google Scholar
Kennerley, S. W., Dahmubed, A. F., Lara, A. H., & Wallis, J. D. (2009). Neurons in the Frontal Lobe Encode the Value of Multiple Decision Variables. Journal of Cognitive Neuroscience, 21, 1162–1178.Google Scholar
Kobayashi, S., Pinto de Carvalho, O., & Schultz, W. (2010). Adaptation of Reward Sensitivity in Orbitofrontal Neurons. Journal of Neuroscience, 30, 534–544.Google Scholar
Kriegeskorte, N., & Douglas, P. K. (2018). Cognitive Computational Neuroscience. Nature Neuroscience, 21, 1148–1160.Google Scholar
Kruschke, J. (2014). Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan. Cambridge, MA: Academic Press.Google Scholar
Laird, A. R., Fox, P. M., Eickhoff, S. B., Turner, J. A., Ray, K. L., McKay, D. R., … Fox, P. T. (2011). Behavioral Interpretations of Intrinsic Connectivity Networks. Journal of Cognitive Neuroscience, 23, 4022–4037.Google Scholar
Li, J., Schiller, D., Schoenbaum, G., Phelps, E. A., & Daw, N. D. (2011). Differential Roles of Human Striatum and Amygdala in Associative Learning. Nature Neuroscience, 14, 1250‒1252.Google Scholar
Logothetis, N.K., Pfeuffer, J., 2004. On the Nature of the BOLD fMRI Contrast Mechanism. Magnetic Resonance Imaging, 22, 1517–1531.Google Scholar
Love, B. C. (2015). The Algorithmic Level Is the Bridge between Computation and Brain. Topics in Cognitive Science, 7, 230–242.Google Scholar
Marr, D. (1982). Chapter 1: The Philosophy and the Approach. In Vision: A Computational Investigation into the Human Representation and Processing Visual Information (pp. 8–38). Cambridge, MA: MIT Press.Google Scholar
McClelland, J. L., & Rumelhart, D. E. (1986). Parallel Distributed Processing, Explorations in the Microstructure of Cognition: Foundations. Cambridge, MA: MIT Press.Google Scholar
McCulloch, W. S., & Pitts, W. (1943). A Logical Calculus of the Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics, 5, 115–133.Google Scholar
Miller, P. (2018). An Introductory Course in Computational Neuroscience (1st edn). Cambridge, MA: MIT Press.Google Scholar
Miller, R. R., Barnet, R. C., & Grahame, N. J. (1995). Assessment of the Rescorla-Wagner Model. Psychological Bulletin, 117, 363–386.Google Scholar
Minsky, M. L. (1967). Computation: Finite and Infinite Machines. Upper Saddle River, NJ: Prentice-Hall.Google Scholar
Minsky, M. L., & Papert, S. A. (1969). Perceptrons: An Introduction to Computational Geometry. Cambridge, MA: MIT Press.Google Scholar
Mohebi, A., Pettibone, J., Hamid, A., Wong, J.-M., Kennedy, R., & Berke, J. (2018). Forebrain Dopamine Value Signals Arise Independently from Midbrain Dopamine Cell Firing. bioRxiv, 334060.Google Scholar
Montague, P. R., Dayan, P., & Sejnowski, T. J. (1996). A Framework for Mesencephalic Dopamine Systems Based on Predictive Hebbian Learning. Journal of Neuroscience, 16, 1936–1947.Google Scholar
Montague, P. R., Dolan, R. J., Friston, K. J., & Dayan, P. (2012). Computational Psychiatry. Trends in Cognitive Science, 16, 72–80.Google Scholar
Moran, R. J., Symmonds, M., Stephan, K. E., Friston, K. J., & Dolan, R. J. (2011). An In Vivo Assay of Synaptic Function Mediating Human Cognition. Current Biology, 21, 1320–1325.Google Scholar
Nocedal, J., & Wright, S. (2006). Numerical Optimization (2nd edn.). New York: Springer.Google Scholar
Noonan, M. P., Chau, B. K. H., Rushworth, M. F. S., & Fellows, L. K. (2017). Contrasting Effects of Medial and Lateral Orbitofrontal Cortex Lesions on Credit Assignment and Decision-Making in Humans. Journal of Neuroscience, 37, 7023–7035.Google Scholar
O’Doherty, J. P., Dayan, P., Friston, K., Critchley, H., & Dolan, R. J. (2003). Temporal Difference Models and Reward-Related Learning in the Human Brain. Neuron, 38, 329–337.Google Scholar
Padoa-Schioppa, C., & Assad, J. A. (2006). Neurons in the Orbitofrontal Cortex Encode Economic Value. Nature, 441, 223–226.Google Scholar
Parker, N. F., Cameron, C. M., Taliaferro, J. P., Lee, J., Choi, J. Y., Davidson, T. J., … Witten, I. B. (2016). Reward and Choice Encoding in Terminals of Midbrain Dopamine Neurons Depends on Striatal Target. Nature Neuroscience, 19, 845–854.Google Scholar
Paton, J. J., Belova, M. A., Morrison, S. E., & Salzman, C. D. (2006). The Primate Amygdala Represents the Positive and Negative Value of Visual Stimuli during Learning. Nature, 439, 865–870.Google Scholar
Pawlow, I. P. (1904). Nobel-Vortrag*. Nordiskt Medicinskt Arkiv, 37, 1–20.Google Scholar
Rescorla, R. A., & Wagner, A. R. (1972). A Theory of Pavlovian Conditioning: Variations in the Effectiveness of Reinforcement and Nonreinforcement. In: Black, A. H. & Prokasy, W. F. (Eds.), Classical Conditioning II (pp. 64–99). New York: Appleton-Century-Crofts.Google Scholar
Rigoux, L., & Daunizeau, J. (2015). Dynamic Causal Modelling of Brain-Behaviour Relationships. NeuroImage, 117, 202–221.Google Scholar
Rigoux, L., Stephan, K. E., Friston, K. J., & Daunizeau, J. (2014). Bayesian Model Selection for Group Studies ‒ Revisited. NeuroImage, 84, 971–985.Google Scholar
Roesch, M. R., & Olson, C. R. (2005). Neuronal Activity in Primate Orbitofrontal Cortex Reflects the Value of Time. Journal of Neurophysiology, 94, 2457–2471.Google Scholar
Rosenblatt, F. (1958). The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain. Psychological Review, 65, 386–408.Google Scholar
Ruff, C. C., & Fehr, E. (2014). The Neurobiology of Rewards and Values in Social Decision Making. Nature Reviews Neuroscience, 15, 549–562.Google Scholar
Samejima, K., Ueda, Y., Doya, K., & Kimura, M. (2005). Representation of Action-Specific Reward Values in the Striatum. Science, 310, 1337–1340.Google Scholar
Schoenbaum, G., Chiba, A. A., & Gallagher, M. (1999). Neural Encoding in Orbitofrontal Cortex and Basolateral Amygdala during Olfactory Discrimination Learning. Journal of Neuroscience, 19, 1876–1884.Google Scholar
Schuck, N. W., Cai, M. B., Wilson, R. C., & Niv, Y. (2016). Human Orbitofrontal Cortex Represents a Cognitive Map of State Space. Neuron, 91, 1402–1412.Google Scholar
Schultz, W., Dayan, P., & Montague, P. R. (1997). A Neural Substrate of Prediction and Reward. Science, 275, 1593–1599.Google Scholar
Sejnowski, T. J., Churchland, P. S., & Movshon, J. A. (2014). Putting Big Data to Good Use in Neuroscience. Nature Neuroscience, 17, 1440–1441.Google Scholar
Simon, H. A. (1956). Rational Choice and the Structure of the Environment. Psychological Review., 63, 129–138.Google Scholar
Stalnaker, T. A., Cooch, N. K., & Schoenbaum, G. (2015). What the Orbitofrontal Cortex Does Not Do. Nature Neuroscience, 18, 620–627.Google Scholar
Stephan, K. E., Penny, W. D., Daunizeau, J., Moran, R. J., & Friston, K. J. (2009). Bayesian Model Selection for Group Studies. NeuroImage, 46, 1004–1017.Google Scholar
Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd edn.). Adaptive Computation and Machine Learning Series. Cambridge, MA: MIT Press.Google Scholar
Tobler, P. N., Dickinson, A., & Schultz, W. (2003). Coding of Predicted Reward Omission by Dopamine Neurons in a Conditioned Inhibition Paradigm. Journal of Neuroscience, 23, 10402–10410.Google Scholar
Tremblay, L., & Schultz, W. (1999). Relative Reward Preference in Primate Orbitofrontal Cortex. Nature, 398, 704–708.Google Scholar
Turner, B. M., Forstmann, B. U., Love, B. C., Palmeri, T. J., & Van Maanen, L. (2017). Approaches to Analysis in Model-Based Cognitive Neuroscience. Journal of Mathematical Psychology, Model-Based Cognitive Neuroscience, 76, 65–79.Google Scholar
Uttal, W. R. (2001). The New Phrenology: The Limits of Localizing Cognitive Processes in the Brain, The New Phrenology: The Limits of Localizing Cognitive Processes in the Brain. Cambridge, MA: MIT Press.Google Scholar
Uttal, W. R. (2014). Psychomythics: Sources of Artifacts and Misconceptions in Scientific Psychology (1st edn.). Hove: Psychology Press.Google Scholar
Vanyukov, P. M., Hallquist, M. N., Delgado, M. R., Szanto, K., & Dombrovski, A. Y. (2019). Neurocomputational Mechanisms of Adaptive Learning in Social Exchanges. Cognitive, Affective, & Behavioral Neuroscience, 19, 1‒13.Google Scholar
Vermunt, J. K. (2010). Latent Class Modeling with Covariates: Two Improved Three-Step Approaches. Political Analysis, 18, 450–469.Google Scholar
Waelti, P., Dickinson, A., & Schultz, W. (2001). Dopamine Responses Comply with Basic Assumptions of Formal Learning Theory. Nature, 412, 43–48.Google Scholar
Wagenmakers, E.-J., Morey, R. D., & Lee, M. D. (2016). Bayesian Benefits for the Pragmatic Researcher. Current Directions in Psychological Science, 25, 169–176.Google Scholar
Wagenmakers, E.-J., Ratcliff, R., Gomez, P., & Iverson, G. J. (2004). Assessing Model Mimicry Using the Parametric Bootstrap. Journal of Mathematical Psychology, 48, 28–50.Google Scholar
Wallis, J. D., & Kennerley, S. W. (2010). Heterogeneous Reward Signals in Prefrontal Cortex. Current Opinions in Neurobiology, 20, 191–198.Google Scholar
Walton, M. E., Behrens, T. E., Buckley, M. J., Rudebeck, P. H., Rushworth, M. F. (2010). Separable Learning Systems in the Macaque Brain and the Role of Orbitofrontal Cortex in Contingent Learning. Neuron, 65, 927–939.Google Scholar
Werbos, P. (1974). Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. Cambridge, MA: Harvard University Press.Google Scholar
Whittington, J. C. R., & Bogacz, R. (2019). Theories of Error Back-Propagation in the Brain. Trends in Cognitive Science, 23, 235‒250.Google Scholar
Wiecki, T. V., Poland, J., & Frank, M. J. (2015). Model-Based Cognitive Neuroscience Approaches to Computational Psychiatry Clustering and Classification. Clinical Psycholical Science, 3, 378–399.Google Scholar
Wimmer, G. E., Braun, E. K., Daw, N. D., & Shohamy, D. (2014). Episodic Memory Encoding Interferes with Reward Learning and Decreases Striatal Prediction Errors. Journal of Neuroscience, 34, 14901–14912.Google Scholar

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