@article{light_neurophysiological_2020,
	title = {Neurophysiological biomarkers for schizophrenia therapeutics},
	volume = {2},
	issn = {26661446},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S2666144620300022},
	doi = {10.1016/j.bionps.2020.100012},
	language = {en},
	urldate = {2020-10-06},
	journal = {Biomarkers in Neuropsychiatry},
	author = {Light, Gregory A. and Joshi, Yash B. and Molina, Juan L. and Bhakta, Savita G. and Nungaray, John A. and Cardoso, Lauren and Kotz, Juliana E. and Thomas, Michael L. and Swerdlow, Neal R.},
	month = jun,
	year = {2020},
	note = {ZSCC: 0000002},
	pages = {100012},
}

@article{fava_decline_2020,
	title = {The {Decline} of {Pluralism} in {Medicine}: {Dissent} {Is} {Welcome}},
	volume = {89},
	issn = {0033-3190, 1423-0348},
	shorttitle = {The {Decline} of {Pluralism} in {Medicine}},
	url = {https://www.karger.com/Article/FullText/505085},
	doi = {10.1159/000505085},
	language = {en},
	number = {1},
	urldate = {2020-04-24},
	journal = {Psychotherapy and Psychosomatics},
	author = {Fava, Giovanni A.},
	year = {2020},
	note = {ZSCC: 0000001},
	pages = {1--5},
}

@article{aftab_conceptual_2020,
	title = {Conceptual {Competence} in {Psychiatry}: {Recommendations} for {Education} and {Training}},
	issn = {1545-7230},
	shorttitle = {Conceptual {Competence} in {Psychiatry}},
	url = {https://doi.org/10.1007/s40596-020-01183-3},
	doi = {10.1007/s40596-020-01183-3},
	language = {en},
	urldate = {2020-03-23},
	journal = {Academic Psychiatry},
	author = {Aftab, Awais and Waterman, G. Scott},
	month = jan,
	year = {2020},
}

@article{han_tolerating_2019,
	title = {Tolerating uncertainty about conceptual models of uncertainty in health care},
	volume = {25},
	issn = {1365-2753},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/jep.13110},
	doi = {10.1111/jep.13110},
	language = {en},
	number = {2},
	urldate = {2020-10-24},
	journal = {Journal of Evaluation in Clinical Practice},
	author = {Han, Paul K. J. and Djulbegovic, Benjamin},
	year = {2019},
	note = {ZSCC: 0000004 
\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/jep.13110},
	pages = {183--185},
}

@article{borsboom_brain_2019,
	title = {Brain disorders? {Not} really: {Why} network structures block reductionism in psychopathology research},
	volume = {42},
	issn = {0140-525X, 1469-1825},
	shorttitle = {Brain disorders?},
	url = {https://www.cambridge.org/core/product/identifier/S0140525X17002266/type/journal_article},
	doi = {10.1017/S0140525X17002266},
	abstract = {In the past decades, reductionism has dominated both research directions and funding policies in clinical psychology and psychiatry. The intense search for the biological basis of mental disorders, however, has not resulted in conclusive reductionist explanations of psychopathology. Recently, network models have been proposed as an alternative framework for the analysis of mental disorders, in which mental disorders arise from the causal interplay between symptoms. In this target article, we show that this conceptualization can help explain why reductionist approaches in psychiatry and clinical psychology are on the wrong track. First, symptom networks preclude the identification of a common cause of symptomatology with a neurobiological condition; in symptom networks, there is no such common cause. Second, symptom network relations depend on the content of mental states and, as such, feature intentionality. Third, the strength of network relations is highly likely to depend partially on cultural and historical contexts as well as external mechanisms in the environment. Taken together, these properties suggest that, if mental disorders are indeed networks of causally related symptoms, reductionist accounts cannot achieve the level of success associated with reductionist disease models in modern medicine. As an alternative strategy, we propose to interpret network structures in terms of D. C. Dennett’s (1987) notion of real patterns, and suggest that, instead of being reducible to a biological basis, mental disorders feature biological and psychological factors that are deeply intertwined in feedback loops. This suggests that neither psychological nor biological levels can claim causal or explanatory priority, and that a holistic research strategy is necessary for progress in the study of mental disorders.},
	language = {en},
	urldate = {2020-07-06},
	journal = {Behavioral and Brain Sciences},
	author = {Borsboom, Denny and Cramer, Angélique O. J. and Kalis, Annemarie},
	year = {2019},
	note = {ZSCC: 0000103},
	pages = {e2},
}

In the past decades, reductionism has dominated both research directions and funding policies in clinical psychology and psychiatry. The intense search for the biological basis of mental disorders, however, has not resulted in conclusive reductionist explanations of psychopathology. Recently, network models have been proposed as an alternative framework for the analysis of mental disorders, in which mental disorders arise from the causal interplay between symptoms. In this target article, we show that this conceptualization can help explain why reductionist approaches in psychiatry and clinical psychology are on the wrong track. First, symptom networks preclude the identification of a common cause of symptomatology with a neurobiological condition; in symptom networks, there is no such common cause. Second, symptom network relations depend on the content of mental states and, as such, feature intentionality. Third, the strength of network relations is highly likely to depend partially on cultural and historical contexts as well as external mechanisms in the environment. Taken together, these properties suggest that, if mental disorders are indeed networks of causally related symptoms, reductionist accounts cannot achieve the level of success associated with reductionist disease models in modern medicine. As an alternative strategy, we propose to interpret network structures in terms of D. C. Dennett’s (1987) notion of real patterns, and suggest that, instead of being reducible to a biological basis, mental disorders feature biological and psychological factors that are deeply intertwined in feedback loops. This suggests that neither psychological nor biological levels can claim causal or explanatory priority, and that a holistic research strategy is necessary for progress in the study of mental disorders.

@article{rubin_classification_2018,
	title = {The {Classification} and {Statistical} {Manual} of {Mental} {Health} {Concerns}: {A} {Proposed} {Practical} {Scientific} {Alternative} to the \textit{{DSM}} and \textit{{ICD}}},
	volume = {58},
	issn = {0022-1678, 1552-650X},
	shorttitle = {The {Classification} and {Statistical} {Manual} of {Mental} {Health} {Concerns}},
	url = {http://journals.sagepub.com/doi/10.1177/0022167817718079},
	doi = {10.1177/0022167817718079},
	language = {en},
	number = {1},
	urldate = {2020-03-18},
	journal = {Journal of Humanistic Psychology},
	author = {Rubin, Jeffrey},
	month = jan,
	year = {2018},
	pages = {93--114},
}

@article{roulston_qualitative_2018,
	title = {Qualitative interviewing and epistemics},
	volume = {18},
	issn = {1468-7941, 1741-3109},
	url = {http://journals.sagepub.com/doi/10.1177/1468794117721738},
	doi = {10.1177/1468794117721738},
	abstract = {Work on epistemics in conversation analysis (CA) has demonstrated how speakers attend closely to the knowledge claims they and others make and how this shapes interaction. This paper uses work on epistemics in CA to explore how interviewers and interviewees orient to knowledge claims involving the asking and answering of questions. Since research participants are recruited to represent a category identified by the researcher, interviewees are assumed to have greater knowledge relative to the research topic as compared to interviewers, who typically work to demonstrate that they are eager learners about others’ experiences, perceptions and beliefs and so forth. This paper examines sequences from research interviews to focus on the fine-grained work involved in asking questions and making knowledge claims within interviews. Epistemics provides a powerful tool to examine how speakers’ orientations to others’ knowledge claims is central to the interactional work of conducting interviews.},
	language = {en},
	number = {3},
	urldate = {2020-03-12},
	journal = {Qualitative Research},
	author = {Roulston, Kathryn},
	month = jun,
	year = {2018},
	pages = {322--341},
}

Work on epistemics in conversation analysis (CA) has demonstrated how speakers attend closely to the knowledge claims they and others make and how this shapes interaction. This paper uses work on epistemics in CA to explore how interviewers and interviewees orient to knowledge claims involving the asking and answering of questions. Since research participants are recruited to represent a category identified by the researcher, interviewees are assumed to have greater knowledge relative to the research topic as compared to interviewers, who typically work to demonstrate that they are eager learners about others’ experiences, perceptions and beliefs and so forth. This paper examines sequences from research interviews to focus on the fine-grained work involved in asking questions and making knowledge claims within interviews. Epistemics provides a powerful tool to examine how speakers’ orientations to others’ knowledge claims is central to the interactional work of conducting interviews.

@article{borsboom_network_2017,
	title = {A network theory of mental disorders},
	volume = {16},
	issn = {2051-5545},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/wps.20375},
	doi = {10.1002/wps.20375},
	abstract = {In recent years, the network approach to psychopathology has been advanced as an alternative way of conceptualizing mental disorders. In this approach, mental disorders arise from direct interactions between symptoms. Although the network approach has led to many novel methodologies and substantive applications, it has not yet been fully articulated as a scientific theory of mental disorders. The present paper aims to develop such a theory, by postulating a limited set of theoretical principles regarding the structure and dynamics of symptom networks. At the heart of the theory lies the notion that symptoms of psychopathology are causally connected through myriads of biological, psychological and societal mechanisms. If these causal relations are sufficiently strong, symptoms can generate a level of feedback that renders them self-sustaining. In this case, the network can get stuck in a disorder state. The network theory holds that this is a general feature of mental disorders, which can therefore be understood as alternative stable states of strongly connected symptom networks. This idea naturally leads to a comprehensive model of psychopathology, encompassing a common explanatory model for mental disorders, as well as novel definitions of associated concepts such as mental health, resilience, vulnerability and liability. In addition, the network theory has direct implications for how to understand diagnosis and treatment, and suggests a clear agenda for future research in psychiatry and associated disciplines.},
	language = {en},
	number = {1},
	urldate = {2020-10-13},
	journal = {World Psychiatry},
	author = {Borsboom, Denny},
	year = {2017},
	note = {ZSCC: 0000706 
\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/wps.20375},
	keywords = {Psychopathology, diagnosis, mental disorders, mental health, network approach, resilience, symptom networks, treatment, vulnerability},
	pages = {5--13},
}

In recent years, the network approach to psychopathology has been advanced as an alternative way of conceptualizing mental disorders. In this approach, mental disorders arise from direct interactions between symptoms. Although the network approach has led to many novel methodologies and substantive applications, it has not yet been fully articulated as a scientific theory of mental disorders. The present paper aims to develop such a theory, by postulating a limited set of theoretical principles regarding the structure and dynamics of symptom networks. At the heart of the theory lies the notion that symptoms of psychopathology are causally connected through myriads of biological, psychological and societal mechanisms. If these causal relations are sufficiently strong, symptoms can generate a level of feedback that renders them self-sustaining. In this case, the network can get stuck in a disorder state. The network theory holds that this is a general feature of mental disorders, which can therefore be understood as alternative stable states of strongly connected symptom networks. This idea naturally leads to a comprehensive model of psychopathology, encompassing a common explanatory model for mental disorders, as well as novel definitions of associated concepts such as mental health, resilience, vulnerability and liability. In addition, the network theory has direct implications for how to understand diagnosis and treatment, and suggests a clear agenda for future research in psychiatry and associated disciplines.

@article{clark_three_2017,
	title = {Three {Approaches} to {Understanding} and {Classifying} {Mental} {Disorder}: {ICD}-11, {DSM}-5, and the {National} {Institute} of {Mental} {Health}’s {Research} {Domain} {Criteria} ({RDoC})},
	volume = {18},
	issn = {1529-1006},
	shorttitle = {Three {Approaches} to {Understanding} and {Classifying} {Mental} {Disorder}},
	url = {https://doi.org/10.1177/1529100617727266},
	doi = {10.1177/1529100617727266},
	abstract = {The diagnosis of mental disorder initially appears relatively straightforward: Patients present with symptoms or visible signs of illness; health professionals make diagnoses based primarily on these symptoms and signs; and they prescribe medication, psychotherapy, or both, accordingly. However, despite a dramatic expansion of knowledge about mental disorders during the past half century, understanding of their components and processes remains rudimentary. We provide histories and descriptions of three systems with different purposes relevant to understanding and classifying mental disorder. Two major diagnostic manuals?the International Classification of Diseases and the Diagnostic and Statistical Manual of Mental Disorders?provide classification systems relevant to public health, clinical diagnosis, service provision, and specific research applications, the former internationally and the latter primarily for the United States. In contrast, the National Institute of Mental Health?s Research Domain Criteria provides a framework that emphasizes integration of basic behavioral and neuroscience research to deepen the understanding of mental disorder. We identify four key issues that present challenges to understanding and classifying mental disorder: etiology, including the multiple causality of mental disorder; whether the relevant phenomena are discrete categories or dimensions; thresholds, which set the boundaries between disorder and nondisorder; and comorbidity, the fact that individuals with mental illness often meet diagnostic requirements for multiple conditions. We discuss how the three systems? approaches to these key issues correspond or diverge as a result of their different histories, purposes, and constituencies. Although the systems have varying degrees of overlap and distinguishing features, they share the goal of reducing the burden of suffering due to mental disorder.},
	number = {2},
	urldate = {2020-07-16},
	journal = {Psychological Science in the Public Interest},
	author = {Clark, Lee Anna and Cuthbert, Bruce and Lewis-Fernández, Roberto and Narrow, William E. and Reed, Geoffrey M.},
	month = nov,
	year = {2017},
	note = {ZSCC: NoCitationData[s0] 
Publisher: SAGE Publications Inc},
	pages = {72--145},
}

The diagnosis of mental disorder initially appears relatively straightforward: Patients present with symptoms or visible signs of illness; health professionals make diagnoses based primarily on these symptoms and signs; and they prescribe medication, psychotherapy, or both, accordingly. However, despite a dramatic expansion of knowledge about mental disorders during the past half century, understanding of their components and processes remains rudimentary. We provide histories and descriptions of three systems with different purposes relevant to understanding and classifying mental disorder. Two major diagnostic manuals?the International Classification of Diseases and the Diagnostic and Statistical Manual of Mental Disorders?provide classification systems relevant to public health, clinical diagnosis, service provision, and specific research applications, the former internationally and the latter primarily for the United States. In contrast, the National Institute of Mental Health?s Research Domain Criteria provides a framework that emphasizes integration of basic behavioral and neuroscience research to deepen the understanding of mental disorder. We identify four key issues that present challenges to understanding and classifying mental disorder: etiology, including the multiple causality of mental disorder; whether the relevant phenomena are discrete categories or dimensions; thresholds, which set the boundaries between disorder and nondisorder; and comorbidity, the fact that individuals with mental illness often meet diagnostic requirements for multiple conditions. We discuss how the three systems? approaches to these key issues correspond or diverge as a result of their different histories, purposes, and constituencies. Although the systems have varying degrees of overlap and distinguishing features, they share the goal of reducing the burden of suffering due to mental disorder.

@incollection{hunsley_controversial_2015,
	address = {New York, NY, US},
	title = {Controversial and questionable assessment techniques},
	isbn = {978-1-4625-1789-3 978-1-4625-1751-0 978-1-4625-1759-6},
	abstract = {The past decade has seen many important developments in the field of clinical assessment. These include (1) statistical approaches for exploring consistency and variability in reliability estimates, (2) theoretical and methodological advances in conceptualizing construct validity, (3) a renewed focus on the utility of assessment data in the clinical enterprise, (4) a compelling, empirically based rationale for routinely monitoring the impact of clinical interventions, and (5) initial attempts to delineate the nature and implications of an evidence-based approach to assessment. Despite this progress, there is widespread use of clinical assessment practices and instruments that lack a strong scientific foundation. In this chapter, we first provide introductory comments on key scientific elements of clinical assessment, and then we examine a subset of commonly used instruments whose use is not justified by scientific evidence. (PsycInfo Database Record (c) 2020 APA, all rights reserved)},
	booktitle = {Science and pseudoscience in clinical psychology, 2nd ed},
	publisher = {The Guilford Press},
	author = {Hunsley, John and Lee, Catherine M. and Wood, James M. and Taylor, Whitney},
	year = {2015},
	note = {ZSCC: 0000145},
	keywords = {Best Practices, Evidence Based Practice, Intervention, Psychological Assessment, Sciences},
	pages = {42--82},
}

The past decade has seen many important developments in the field of clinical assessment. These include (1) statistical approaches for exploring consistency and variability in reliability estimates, (2) theoretical and methodological advances in conceptualizing construct validity, (3) a renewed focus on the utility of assessment data in the clinical enterprise, (4) a compelling, empirically based rationale for routinely monitoring the impact of clinical interventions, and (5) initial attempts to delineate the nature and implications of an evidence-based approach to assessment. Despite this progress, there is widespread use of clinical assessment practices and instruments that lack a strong scientific foundation. In this chapter, we first provide introductory comments on key scientific elements of clinical assessment, and then we examine a subset of commonly used instruments whose use is not justified by scientific evidence. (PsycInfo Database Record (c) 2020 APA, all rights reserved)

@incollection{probst_epistemological_2015,
	address = {Cham},
	title = {Epistemological {Issues} in {Diagnosis} and {Assessment}},
	isbn = {978-3-319-17773-1 978-3-319-17774-8},
	url = {http://link.springer.com/10.1007/978-3-319-17774-8_2},
	abstract = {This book begins with a thoughtful exploration of two fundamental questions that underlie all clinical decisions. First, what exactly is a “mental disorder,” as opposed to other kinds of suffering or maladaptive behavior that we would call non-mental disorders? What makes a disorder specifically mental? And second, on what do we base these definitions and distinctions? What do we consider reliable (and unreliable) sources of knowledge, and what are some of the pitfalls in our assumptions about what we “know” and how we’ve come to “know” it? Common cognitive errors are explored, along with their consequences. These include circular reasoning, the difficulty of determining threshold or cut-off point, assumptions about causality, and the problems inherent in mental heuristics such as anchoring and availability. The chapter then explores the role of labels and labeling theory, the aims and limitations of classification systems such as the DSM, and the challenge of trying to develop a way to think about mental disorder that is useful for both general purposes (to make predictions based on shared characteristics) and specific aims (to understand and help particular individuals).},
	language = {en},
	urldate = {2020-03-12},
	booktitle = {Critical {Thinking} in {Clinical} {Assessment} and {Diagnosis}},
	publisher = {Springer International Publishing},
	author = {Probst, Barbara},
	editor = {Probst, Barbara},
	year = {2015},
	doi = {10.1007/978-3-319-17774-8_2},
	pages = {15--44},
}

This book begins with a thoughtful exploration of two fundamental questions that underlie all clinical decisions. First, what exactly is a “mental disorder,” as opposed to other kinds of suffering or maladaptive behavior that we would call non-mental disorders? What makes a disorder specifically mental? And second, on what do we base these definitions and distinctions? What do we consider reliable (and unreliable) sources of knowledge, and what are some of the pitfalls in our assumptions about what we “know” and how we’ve come to “know” it? Common cognitive errors are explored, along with their consequences. These include circular reasoning, the difficulty of determining threshold or cut-off point, assumptions about causality, and the problems inherent in mental heuristics such as anchoring and availability. The chapter then explores the role of labels and labeling theory, the aims and limitations of classification systems such as the DSM, and the challenge of trying to develop a way to think about mental disorder that is useful for both general purposes (to make predictions based on shared characteristics) and specific aims (to understand and help particular individuals).

@article{whooley_nosological_2014,
	title = {Nosological {Reflections}: {The} {Failure} of \textit{{DSM}} -5, the {Emergence} of {RDoC}, and the {Decontextualization} of {Mental} {Distress}},
	volume = {4},
	issn = {2156-8693, 2156-8731},
	shorttitle = {Nosological {Reflections}},
	url = {http://journals.sagepub.com/doi/10.1177/2156869313519114},
	doi = {10.1177/2156869313519114},
	abstract = {Since the establishment of the symptoms-based categories in the Diagnostic and Statistical Manual of Mental Disorders (DSM), Third Edition, sociologists have raised concerns about the DSM’s failure to appreciate social, contextual factors when defining mental disorders. The author describes recent developments in psychiatric nosology—the DSM-5 revision process and the emergence of the Research Domain Criteria (RDoC)—and then considers their implications for decontextualization. Drawing on in-depth interviews with psychiatrists involved in the DSM-5 controversy and a content analysis of key documents, the author first recounts the ambitious DSM-5 revisions, illuminating the DSM-5 Task Force’s embrace of dimensionalization as a solution to the problem of validity and the ultimate rejection of this ‘‘paradigm shift’’ by psychiatrists. The Task Force’s failures prompted the National Institute of Mental Health to promote RDoC as an alternative nosological framework that eschews DSM categories altogether. Next, the author explores the ramifications of these events for decontextualization, which neither DSM-5 nor RDoC explicitly addresses, demonstrating how RDoC is poised to escalate decontextualization through its brain-centric conceptualization of mental disorders. To counteract these developments, sociologists should continue to promote ways of defining mental distress that underscore its social embeddedness.},
	language = {en},
	number = {2},
	urldate = {2020-03-18},
	journal = {Society and Mental Health},
	author = {Whooley, Owen},
	month = jul,
	year = {2014},
	pages = {92--110},
}

Since the establishment of the symptoms-based categories in the Diagnostic and Statistical Manual of Mental Disorders (DSM), Third Edition, sociologists have raised concerns about the DSM’s failure to appreciate social, contextual factors when defining mental disorders. The author describes recent developments in psychiatric nosology—the DSM-5 revision process and the emergence of the Research Domain Criteria (RDoC)—and then considers their implications for decontextualization. Drawing on in-depth interviews with psychiatrists involved in the DSM-5 controversy and a content analysis of key documents, the author first recounts the ambitious DSM-5 revisions, illuminating the DSM-5 Task Force’s embrace of dimensionalization as a solution to the problem of validity and the ultimate rejection of this ‘‘paradigm shift’’ by psychiatrists. The Task Force’s failures prompted the National Institute of Mental Health to promote RDoC as an alternative nosological framework that eschews DSM categories altogether. Next, the author explores the ramifications of these events for decontextualization, which neither DSM-5 nor RDoC explicitly addresses, demonstrating how RDoC is poised to escalate decontextualization through its brain-centric conceptualization of mental disorders. To counteract these developments, sociologists should continue to promote ways of defining mental distress that underscore its social embeddedness.

@article{bagdasaryan_experiencing_2013,
	title = {Experiencing your brain: neurofeedback as a new bridge between neuroscience and phenomenology},
	volume = {7},
	issn = {1662-5161},
	shorttitle = {Experiencing your brain},
	url = {https://www.frontiersin.org/articles/10.3389/fnhum.2013.00680/full},
	doi = {10.3389/fnhum.2013.00680},
	abstract = {Neurophenomenology is a scientific research programme aimed to combine neuroscience with phenomenology in order to study human experience. Nevertheless, despite several explicit implementations, the integration of first-person data into the experimental protocols of cognitive neuroscience still faces a number of epistemological and methodological challenges. Notably, the difficulties to simultaneously acquire phenomenological and neuroscientific data have limited its implementation into research projects. In our paper, we propose that neurofeedback paradigms, in which subjects learn to self-regulate their own neural activity, may offer a new pragmatic way to integrate first-person and third-person descriptions. Here, information from first- and third-person perspectives are braided together in the iterative causal closed loop, creating experimental situations in which they reciprocally constrain each other. In real-time, the subject is not only actively involved in the process of data acquisition, but also assisted to directly influence the neural data through conscious experience. Thus, neurofeedback may help to gain a deeper phenomenological-physiological understanding of downward causations whereby conscious activities have direct causal effects on neuronal patterns. We discuss possible mechanisms that could mediate such effects and indicate a number of directions for future research.},
	language = {English},
	urldate = {2020-10-06},
	journal = {Frontiers in Human Neuroscience},
	author = {Bagdasaryan, Juliana and Le Van Quyen, Michel},
	year = {2013},
	note = {ZSCC: 0000054 
Publisher: Frontiers},
	keywords = {Neurofeedback, downward causation, multiscale neural dynamics, neurophenomenology, voluntary action},
}

Neurophenomenology is a scientific research programme aimed to combine neuroscience with phenomenology in order to study human experience. Nevertheless, despite several explicit implementations, the integration of first-person data into the experimental protocols of cognitive neuroscience still faces a number of epistemological and methodological challenges. Notably, the difficulties to simultaneously acquire phenomenological and neuroscientific data have limited its implementation into research projects. In our paper, we propose that neurofeedback paradigms, in which subjects learn to self-regulate their own neural activity, may offer a new pragmatic way to integrate first-person and third-person descriptions. Here, information from first- and third-person perspectives are braided together in the iterative causal closed loop, creating experimental situations in which they reciprocally constrain each other. In real-time, the subject is not only actively involved in the process of data acquisition, but also assisted to directly influence the neural data through conscious experience. Thus, neurofeedback may help to gain a deeper phenomenological-physiological understanding of downward causations whereby conscious activities have direct causal effects on neuronal patterns. We discuss possible mechanisms that could mediate such effects and indicate a number of directions for future research.

@article{thomas_anomalies_2012,
	title = {The anomalies of evidence‐based medicine in psychiatry: time to rethink the basis of mental health practice},
	volume = {17},
	issn = {1361-9322},
	shorttitle = {The anomalies of evidence‐based medicine in psychiatry},
	url = {https://www.emerald.com/insight/content/doi/10.1108/13619321211287265/full/html},
	doi = {10.1108/13619321211287265},
	abstract = {Purpose – Evidence-based medicine (EBM) is a technical and scientific paradigm in clinical practice that has delivered major improvements in the outcome of care in medicine and surgery. However, its value in psychiatry is much less clear. The purpose of the paper is thus to examine its value by subjecting empirical evidence from EBM to a conceptual analysis using the philosophy of Thomas Kuhn. Design/methodology/approach – The authors examine evidence drawn from meta-analyses of RCTs investigating the efficacy of specific treatments for depression in the form of antidepressant drugs and CBT. This shows that the non-specific aspects of treatment, the placebo effect and the quality of the therapeutic alliance as seen by the patient, are more important in determining outcome than the specific elements (active drug, specific therapeutic elements of CBT).},
	language = {en},
	number = {3},
	urldate = {2020-03-18},
	journal = {Mental Health Review Journal},
	author = {Thomas, Philip and Bracken, Pat and Timimi, Sami},
	month = sep,
	year = {2012},
	keywords = {clinical medicine, depression, evidence-based medicine, mental illness, recovery},
	pages = {152--162},
}

Purpose – Evidence-based medicine (EBM) is a technical and scientific paradigm in clinical practice that has delivered major improvements in the outcome of care in medicine and surgery. However, its value in psychiatry is much less clear. The purpose of the paper is thus to examine its value by subjecting empirical evidence from EBM to a conceptual analysis using the philosophy of Thomas Kuhn. Design/methodology/approach – The authors examine evidence drawn from meta-analyses of RCTs investigating the efficacy of specific treatments for depression in the form of antidepressant drugs and CBT. This shows that the non-specific aspects of treatment, the placebo effect and the quality of the therapeutic alliance as seen by the patient, are more important in determining outcome than the specific elements (active drug, specific therapeutic elements of CBT).

@article{simmons_false-positive_2011,
	title = {False-{Positive} {Psychology}: {Undisclosed} {Flexibility} in {Data} {Collection} and {Analysis} {Allows} {Presenting} {Anything} as {Significant}},
	volume = {22},
	issn = {0956-7976, 1467-9280},
	shorttitle = {False-{Positive} {Psychology}},
	url = {http://journals.sagepub.com/doi/10.1177/0956797611417632},
	doi = {10.1177/0956797611417632},
	abstract = {In this article, we accomplish two things. First, we show that despite empirical psychologists’ nominal endorsement of a low rate of false-positive findings (≤ .05), flexibility in data collection, analysis, and reporting dramatically increases actual false-positive rates. In many cases, a researcher is more likely to falsely find evidence that an effect exists than to correctly find evidence that it does not. We present computer simulations and a pair of actual experiments that demonstrate how unacceptably easy it is to accumulate (and report) statistically significant evidence for a false hypothesis. Second, we suggest a simple, low-cost, and straightforwardly effective disclosure-based solution to this problem. The solution involves six concrete requirements for authors and four guidelines for reviewers, all of which impose a minimal burden on the publication process.},
	language = {en},
	number = {11},
	urldate = {2020-04-02},
	journal = {Psychological Science},
	author = {Simmons, Joseph P. and Nelson, Leif D. and Simonsohn, Uri},
	month = nov,
	year = {2011},
	note = {ZSCC: 0004478},
	pages = {1359--1366},
}

In this article, we accomplish two things. First, we show that despite empirical psychologists’ nominal endorsement of a low rate of false-positive findings (≤ .05), flexibility in data collection, analysis, and reporting dramatically increases actual false-positive rates. In many cases, a researcher is more likely to falsely find evidence that an effect exists than to correctly find evidence that it does not. We present computer simulations and a pair of actual experiments that demonstrate how unacceptably easy it is to accumulate (and report) statistically significant evidence for a false hypothesis. Second, we suggest a simple, low-cost, and straightforwardly effective disclosure-based solution to this problem. The solution involves six concrete requirements for authors and four guidelines for reviewers, all of which impose a minimal burden on the publication process.