Author
Monika A. Waszczuk
Other affiliations: University of Oxford, Rosalind Franklin University of Medicine and Science, University at Albany, SUNY ...read more
Bio: Monika A. Waszczuk is an academic researcher from Stony Brook University. The author has contributed to research in topics: Psychopathology & Anxiety. The author has an hindex of 20, co-authored 50 publications receiving 2487 citations. Previous affiliations of Monika A. Waszczuk include University of Oxford & Rosalind Franklin University of Medicine and Science.
Topics: Psychopathology, Anxiety, Medicine, Population, Psychology
Papers
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Stony Brook University1, University of Minnesota2, University of Notre Dame3, University of Vermont4, University of Toronto5, Boston University6, University of Maryland, Baltimore7, Duke University8, University of Kansas9, King's College London10, Columbia University11, Broad Institute12, Purdue University13, University of Iowa14, University of Georgia15, Texas A&M University16, Oklahoma State University–Stillwater17, University of Groningen18, Florida State University19, Uniformed Services University of the Health Sciences20, Bryn Mawr College21, University of North Texas22, University of Otago23, University at Buffalo24, University of Arizona25, University of New South Wales26, Northwestern University27, Emory University28, University of Kentucky29, University of Pittsburgh30, Brown University31
TL;DR: The HiTOP promises to improve research and clinical practice by addressing the aforementioned shortcomings of traditional nosologies and provides an effective way to summarize and convey information on risk factors, etiology, pathophysiology, phenomenology, illness course, and treatment response.
Abstract: The reliability and validity of traditional taxonomies are limited by arbitrary boundaries between psychopathology and normality, often unclear boundaries between disorders, frequent disorder co-occurrence, heterogeneity within disorders, and diagnostic instability. These taxonomies went beyond evidence available on the structure of psychopathology and were shaped by a variety of other considerations, which may explain the aforementioned shortcomings. The Hierarchical Taxonomy Of Psychopathology (HiTOP) model has emerged as a research effort to address these problems. It constructs psychopathological syndromes and their components/subtypes based on the observed covariation of symptoms, grouping related symptoms together and thus reducing heterogeneity. It also combines co-occurring syndromes into spectra, thereby mapping out comorbidity. Moreover, it characterizes these phenomena dimensionally, which addresses boundary problems and diagnostic instability. Here, we review the development of the HiTOP and the relevant evidence. The new classification already covers most forms of psychopathology. Dimensional measures have been developed to assess many of the identified components, syndromes, and spectra. Several domains of this model are ready for clinical and research applications. The HiTOP promises to improve research and clinical practice by addressing the aforementioned shortcomings of traditional nosologies. It also provides an effective way to summarize and convey information on risk factors, etiology, pathophysiology, phenomenology, illness course, and treatment response. This can greatly improve the utility of the diagnosis of mental disorders. The new classification remains a work in progress. However, it is developing rapidly and is poised to advance mental health research and care significantly as the relevant science matures. (PsycINFO Database Record
1,635 citations
University of Minnesota1, Stony Brook University2, University of Notre Dame3, Macquarie University4, University of North Texas5, University at Buffalo6, University of Kentucky7, University of Vermont8, University of Toronto9, University of South Florida10, University of Maryland, Baltimore11, Southern Methodist University12, University of Hawaii13, College of William & Mary14, Ghent University15, University of Utah16, University of Michigan17, Columbia University18, University of Kansas19, Pennsylvania State University20, University of California, Davis21, Georgia State University22, University of Iowa23, University of Georgia24, Texas A&M University25, Oklahoma State University–Stillwater26, University of Groningen27, University of Liverpool28, Florida State University29, Uniformed Services University of the Health Sciences30, Maastricht University31, Bryn Mawr College32, Purdue University33, University of Otago34, University of Maryland, College Park35, University of Arizona36, University of New South Wales37, Northwestern University38, Emory University39, Oak Ridge National Laboratory40, University of Pittsburgh41, Vanderbilt University42
TL;DR: The aims and current foci of the HiTOP Consortium, a group of 70 investigators working together to study empirical classification of psychopathology, are described, which pertain to continued research on the empirical organization of psychopathological constructs; the connection between personality and psychopathology; the utility of empirically based psychopathology constructs in both research and the clinic.
308 citations
College of William & Mary1, Macquarie University2, University of Kansas3, University of Amsterdam4, Pennsylvania State University5, University at Albany, SUNY6, Oklahoma State University–Stillwater7, University of Maryland, College Park8, University of Arizona9, Purdue University10, University of New South Wales11, Vanderbilt University12, Université de Montréal13, University of South Florida14, University of Utah15, University of Minnesota16, University of Liverpool17, Northwestern University18, King's College London19, Maastricht University20, Emory University21, University of Pittsburgh22, University of Kassel23, University of Toronto24, Southern Methodist University25, University of Hawaii at Manoa26, University of Notre Dame27, Medical Research Council28, University of California, Davis29, University of Vermont30, Georgia State University31, Florida State University32, University of North Texas33, Stony Brook University34
TL;DR: The Hierarchical Taxonomy of Psychopathology (HiTOP) as discussed by the authors is based on empirical patterns of co-occurrence among psychological symptoms, and it has the potential to accelerate and improve research on mental health problems as well as efforts to more effectively assess, prevent, and treat mental illness.
Abstract: For more than a century, research on psychopathology has focused on categorical diagnoses. Although this work has produced major discoveries, growing evidence points to the superiority of a dimensional approach to the science of mental illness. Here we outline one such dimensional system-the Hierarchical Taxonomy of Psychopathology (HiTOP)-that is based on empirical patterns of co-occurrence among psychological symptoms. We highlight key ways in which this framework can advance mental-health research, and we provide some heuristics for using HiTOP to test theories of psychopathology. We then review emerging evidence that supports the value of a hierarchical, dimensional model of mental illness across diverse research areas in psychological science. These new data suggest that the HiTOP system has the potential to accelerate and improve research on mental-health problems as well as efforts to more effectively assess, prevent, and treat mental illness.
225 citations
University of California, Davis1, Stony Brook University2, University of Minnesota3, University of Notre Dame4, University of Kentucky5, University of Vermont6, Syracuse University7, Region Zealand8, University of Toronto9, Harvard University10, University of South Florida11, Southern Methodist University12, University of Hawaii at Manoa13, College of William & Mary14, Ghent University15, University of Utah16, Texas A&M University17, University of Kansas18, Zürcher Fachhochschule19, Dresden University of Technology20, University of British Columbia21, Albany Medical College22, Purdue University23, University of Iowa24, University of Georgia25, Oklahoma State University–Stillwater26, University of Groningen27, Florida State University28, Pennsylvania State University29, University of North Texas30, University of Otago31, University of New South Wales32, Northwestern University33, University of Missouri34, McGill University35, Emory University36, University of Tennessee37, University of Pittsburgh38, Marian University39, Vanderbilt University40
TL;DR: Author(s): Hopwood, Christopher J; Kotov, Roman; Krueger, Robert F; Watson, David; Widiger, Thomas A; Widinger,Thomas A; Althoff, Robert R; Ansell, Emily B; Bach, Bo; Michael Bagby, R; Blais, Mark A; Bornovalova, Marina A; Chmielewski, Michael; Cicero, David C; Conway, Christopher; De Clercq, Barbara;
Abstract: Author(s): Hopwood, Christopher J; Kotov, Roman; Krueger, Robert F; Watson, David; Widiger, Thomas A; Althoff, Robert R; Ansell, Emily B; Bach, Bo; Michael Bagby, R; Blais, Mark A; Bornovalova, Marina A; Chmielewski, Michael; Cicero, David C; Conway, Christopher; De Clercq, Barbara; De Fruyt, Filip; Docherty, Anna R; Eaton, Nicholas R; Edens, John F; Forbes, Miriam K; Forbush, Kelsie T; Hengartner, Michael P; Ivanova, Masha Y; Leising, Daniel; John Livesley, W; Lukowitsky, Mark R; Lynam, Donald R; Markon, Kristian E; Miller, Joshua D; Morey, Leslie C; Mullins-Sweatt, Stephanie N; Hans Ormel, J; Patrick, Christopher J; Pincus, Aaron L; Ruggero, Camilo; Samuel, Douglas B; Sellbom, Martin; Slade, Tim; Tackett, Jennifer L; Thomas, Katherine M; Trull, Timothy J; Vachon, David D; Waldman, Irwin D; Waszczuk, Monika A; Waugh, Mark H; Wright, Aidan GC; Yalch, Mathew M; Zald, David H; Zimmermann, Johannes
196 citations
Stony Brook University1, University of Minnesota2, University of Notre Dame3, University of North Texas4, Fordham University5, Macquarie University6, University of North Carolina at Chapel Hill7, Georgia State University8, Oklahoma State University–Stillwater9, State University of New York System10, Emory University11, Rosalind Franklin University of Medicine and Science12, University of Pittsburgh13
TL;DR: The Hierarchical Taxonomy of Psychopathology (HiTOP) consortium proposed a model based on structural evidence to address problems of diagnostic heterogeneity, comorbidity, and unreliability.
Abstract: Traditional diagnostic systems went beyond empirical evidence on the structure of mental health Consequently, these diagnoses do not depict psychopathology accurately, and their validity in research and utility in clinicalpractice are therefore limited The Hierarchical Taxonomy of Psychopathology (HiTOP) consortium proposed a model based on structural evidence It addresses problems of diagnostic heterogeneity, comorbidity, and unreliability We review the HiTOP model, supporting evidence, and conceptualization of psychopathology in this hierarchical dimensional framework The system is not yet comprehensive, and we describe the processes for improving and expanding it We summarize data on the ability of HiTOP to predict and explain etiology (genetic, environmental, and neurobiological), risk factors, outcomes, and treatment response We describe progress in the development of HiTOP-based measures and in clinical implementation of the system Finally, we review outstanding challenges and the research agenda HiTOP is of practical utility already, and its ongoing development will produce a transformative map of psychopathology
149 citations
Cited by
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TL;DR: This target article critically examines this "hierarchical prediction machine" approach, concluding that it offers the best clue yet to the shape of a unified science of mind and action.
Abstract: Brains, it has recently been argued, are essentially prediction machines. They are bundles of cells that support perception and action by constantly attempting to match incoming sensory inputs with top-down expectations or predictions. This is achieved using a hierarchical generative model that aims to minimize prediction error within a bidirectional cascade of cortical processing. Such accounts offer a unifying model of perception and action, illuminate the functional role of attention, and may neatly capture the special contribution of cortical processing to adaptive success. This target article critically examines this "hierarchical prediction machine" approach, concluding that it offers the best clue yet to the shape of a unified science of mind and action. Sections 1 and 2 lay out the key elements and implications of the approach. Section 3 explores a variety of pitfalls and challenges, spanning the evidential, the methodological, and the more properly conceptual. The paper ends (sections 4 and 5) by asking how such approaches might impact our more general vision of mind, experience, and agency.
3,640 citations
01 Jan 2000
TL;DR: In this article, the authors propose a method to use the information of the user's interaction with the system to improve the performance of the system. But they do not consider the impact of the interaction on the overall system.
Abstract: Статья посвящена вопросам влияния власти на поведение человека. Авторы рассматривают данные различных источников, в которых увеличение власти связывается с напористостью, а ее уменьшение - с подавленностью. Конкретно, власть ассоциируется с: а) позитивным аффектом; б) вниманием к вознаграждению и к свойствам других, удовлетворяющим личные цели; в) автоматической переработкой информации и резкими суждениями; г) расторможенным социальным поведением. Уменьшение власти, напротив, ассоциируется с: а) негативным аффектом; б) вниманием к угрозам и наказаниям, к интересам других и к тем характеристикам я, которые отвечают целям других; в) контролируемой переработкой информации и совещательным типом рассуждений; г) подавленным социальным поведением. Обсуждаются также последствия этих паттернов поведения, связанных с властью, и потенциальные модераторы.
2,293 citations
Stony Brook University1, University of Minnesota2, University of Notre Dame3, University of Vermont4, University of Toronto5, Boston University6, University of Maryland, Baltimore7, Duke University8, University of Kansas9, King's College London10, Columbia University11, Broad Institute12, Purdue University13, University of Iowa14, University of Georgia15, Texas A&M University16, Oklahoma State University–Stillwater17, University of Groningen18, Florida State University19, Uniformed Services University of the Health Sciences20, Bryn Mawr College21, University of North Texas22, University of Otago23, University at Buffalo24, University of Arizona25, University of New South Wales26, Northwestern University27, Emory University28, University of Kentucky29, University of Pittsburgh30, Brown University31
TL;DR: The HiTOP promises to improve research and clinical practice by addressing the aforementioned shortcomings of traditional nosologies and provides an effective way to summarize and convey information on risk factors, etiology, pathophysiology, phenomenology, illness course, and treatment response.
Abstract: The reliability and validity of traditional taxonomies are limited by arbitrary boundaries between psychopathology and normality, often unclear boundaries between disorders, frequent disorder co-occurrence, heterogeneity within disorders, and diagnostic instability. These taxonomies went beyond evidence available on the structure of psychopathology and were shaped by a variety of other considerations, which may explain the aforementioned shortcomings. The Hierarchical Taxonomy Of Psychopathology (HiTOP) model has emerged as a research effort to address these problems. It constructs psychopathological syndromes and their components/subtypes based on the observed covariation of symptoms, grouping related symptoms together and thus reducing heterogeneity. It also combines co-occurring syndromes into spectra, thereby mapping out comorbidity. Moreover, it characterizes these phenomena dimensionally, which addresses boundary problems and diagnostic instability. Here, we review the development of the HiTOP and the relevant evidence. The new classification already covers most forms of psychopathology. Dimensional measures have been developed to assess many of the identified components, syndromes, and spectra. Several domains of this model are ready for clinical and research applications. The HiTOP promises to improve research and clinical practice by addressing the aforementioned shortcomings of traditional nosologies. It also provides an effective way to summarize and convey information on risk factors, etiology, pathophysiology, phenomenology, illness course, and treatment response. This can greatly improve the utility of the diagnosis of mental disorders. The new classification remains a work in progress. However, it is developing rapidly and is poised to advance mental health research and care significantly as the relevant science matures. (PsycINFO Database Record
1,635 citations
01 Jan 2008
TL;DR: This work reviews the literature regarding short sleep duration as an independent risk factor for obesity and weight gain and suggests sleep deprivation may influence weight through effects on appetite, physical activity, and/or thermoregulation.
Abstract: Objective: The recent obesity epidemic has been accompanied by a parallel growth in chronic sleep deprivation. Physiologic studies suggest sleep deprivation may influence weight through effects on appetite, physical activity, and/or thermoregulation. This work reviews the literature regarding short sleep duration as an independent risk factor for obesity and weight gain.
1,172 citations
TL;DR: A large international community sample was recruited to complete measures of self-perceived risk of contracting COVID-19, fear of the virus, moral foundations, political orientation, and behavior change in response to the pandemic, and the only predictor of positive behavior change was fear of COVID -19, with no effect of politically relevant variables.
Abstract: In the current context of the global pandemic of coronavirus disease-2019 (COVID-19), health professionals are working with social scientists to inform government policy on how to slow the spread of the virus. An increasing amount of social scientific research has looked at the role of public message framing, for instance, but few studies have thus far examined the role of individual differences in emotional and personality-based variables in predicting virus-mitigating behaviors. In this study, we recruited a large international community sample (N = 324) to complete measures of self-perceived risk of contracting COVID-19, fear of the virus, moral foundations, political orientation, and behavior change in response to the pandemic. Consistently, the only predictor of positive behavior change (e.g., social distancing, improved hand hygiene) was fear of COVID-19, with no effect of politically relevant variables. We discuss these data in relation to the potentially functional nature of fear in global health crises.
913 citations