Measuring metacognitive performance: type 1 performance dependence and test-retest reliability

Research on metacognition-thinking about thinking-has grown rapidly and fostered our understanding of human cognition in healthy individuals and clinical populations. Of central importance is the concept of metacognitive performance, which characterizes the capacity of an individual to estimate and report the accuracy of primary (type 1) cognitive processes or actions ensuing from these processes. Arguably one of the biggest challenges for measures of metacognitive performance is their dependency on objective type 1 performance, although more recent methods aim to address this issue. The present work scrutinizes the most popular metacognitive performance measures in terms of two critical characteristics: independence of type 1 performance and test-retest reliability. Analyses of data from the Confidence Database (total N = 6912) indicate that no current metacognitive performance measure is independent of type 1 performance. The shape of this dependency is largely reproduced by extending current models of metacognition with a source of metacognitive noise. Moreover, the reliability of metacognitive performance measures is highly sensitive to the combination of type 1 performance and trial number. Importantly, trial numbers frequently employed in metacognition research are too low to achieve an acceptable level of test-retest reliability. Among common task characteristics, simultaneous choice and confidence reports most strongly improved reliability. Finally, general recommendations about design choices and analytical remedies for studies investigating metacognitive performance are provided

The Effect of Creative Culture on Aggressive Financial Reporting

Chief Executive Officers identify creativity as the leadership competency most desired in business today (IBM 2010). As companies recognize the benefits of creativity and innovation, managers are increasingly looking to build creative cultures within their organizations. However, research in psychology suggests that there may be unintended negative consequences to these attempts. In this study, I predict and find that innovative company culture primes creative thought and, in turn, leads to higher levels of real earnings management (REM) behaviors. Using construal level theories of psychological distance proposed by Trope and Liberman (2010), I design and test both a lower-level and a higher-level construal-based intervention to reduce real earnings management in these cultures. As I predict, a lower-level construal intervention reduces REM behaviors, but a higher-level construal-based intervention reduces REM behaviors to a greater extent. My findings have implications for diverse groups of business professionals. For example, identifying negative unintended consequences of creative corporate culture can help management more effectively assess risk across the organization. Also, the findings of this study could provide external auditors with information about client risk as early as the client acceptance stage of the audit. The study’s findings also inform boards of directors and audit regulators of a potential indicator of lower earnings quality. In addition, I contribute to the emerging accounting literature regarding real earnings management behaviors and to the psychology literature addressing the link between self-interested behavior and creativity, as well as to research examining the effects of construals on decision making under uncertainty

Reverse engineering of metacognition

The human ability to introspect on thoughts, perceptions or actions - metacognitive ability - has become a focal topic of both cognitive basic and clinical research. At the same time it has become increasingly clear that currently available quantitative tools are limited in their ability to make unconfounded inferences about metacognition. As a step forward, the present work intro-duces a comprehensive modeling framework of metacognition that allows for inferences about metacognitive noise and metacognitive biases during the readout of decision values or at the confi-dence reporting stage. The model assumes that confidence results from a continuous but noisy and potentially biased transformation of decision values, described by a confidence link function. A canonical set of metacognitive noise distributions is introduced which differ, amongst others, in their predictions about metacognitive sign flips of decision values. Successful recovery of model param-eters is demonstrated, and the model is validated on an empirical data set. In particular, it is shown that metacognitive noise and bias parameters correlate with conventional behavioral measures. Crucially, in contrast to these conventional measures, metacognitive noise parameters inferred from the model are shown to be independent of performance. This work is accompanied by a toolbox (ReMeta) that allows researchers to estimate key parameters of metacognition in confidence datasets

Using machine learning to resolve the neural basis of alcohol dependence

Alcohol dependence is a psychiatric disorder with a lifetime prevalence of over 10% and a leading cause of morbidity and premature death. A better understanding of the neural mechanisms underlying alcohol dependence to improve prevention, diagnosis and treatment is thus of great societal interest. Recent advancements in the analysis of neuroimaging data based on machine learning have opened new paths to a better quantitative understanding of the disorder. The present habilitation reviews both works with a focus on improving machine learning methodology and empirical works in which machine learning was applied to investigate the neural basis of alcohol dependence. The methodological works advanced several aspects of machine learning in neuroimaging. In particular, they introduced i) a novel classifier (weighted robust distance – WeiRD), which operates parameter-free, computationally efficient and enables a transparent inspection of feature importances, ii) a method to preprocess neuroimaging data based on multivariate noise normalization, which yielded a substantial improvement in classification performance compared to previous the state-of-the-art, and iii) a novel method to reintroduce meaningful graded information into discretized classification accuracies by utilizing classifier decision values. Drawing on a large neuroimaging dataset of alcohol-dependent patients and controls from the LeAD-study (www.lead-studie.de; clinical trial number: NCT01679145), machine learning methods were applied in empirical works to investigate structural and functional alterations in alcohol dependence. Structural damage associated with alcohol dependence were investigated from two conceptually different angles. A first study was aimed at providing the first quantitative evidence for a long-standing hypothesis about the damaging effects of alcohol – the premature aging hypothesis. To this end, a machine learning model was trained on the relationship between grey-matter pattern information and chronological age in a healthy control group and then applied to the sample of alcohol-dependent patients. The predicted ‘brain age’ of patients was found to be was several years higher than their chronological age, thus not only providing quantitative evidence for brain aging in alcohol dependence, but also showing that these aging effects are indeed substantial in relation to the human lifespan. The second study used machine learning to quantify the predictive accuracy of grey-matter pattern information for the diagnosis and a severity measure (lifetime consumption) of alcohol dependence. On average, machine learning models correctly predicted the diagnosis in three of four subjects and accurately estimated the amount of lifetime alcohol consumption. Closer inspection of the prediction model indicated an important role of dorsal anterior cingulate cortex. Comparison with an experienced radiologist, who, like the classifier, was provided with the structural brain scans of the subjects, demonstrated superior performance of computer-based classification and in addition a more effective consideration of demographic information (age and gender). Finally, a third study used functional magnetic resonance imaging to investigate a specific hypothesis about reduced goal-directed learning in alcohol dependence as well as its relation to relapse after detoxification. Computational modelling in combination with machine learning revealed that the interaction of model-based learning and high alcohol expectancies was predictive of diagnosis (patients versus controls) and treatment outcome (abstainers versus relapsers). This finding was paralleled by a signature of model-based learning in medial prefrontal cortex, which was reduced in patients relative to controls and in relapsers relative to abstainers. In sum, the works presented in this habilitation advance machine learning methods for neuroimaging and show that these methods yield novel insights into the neural basis of alcohol dependence. An emerging theme across the three empirical studies on alcohol dependence is the disturbance of executive frontal brain structure and function, supporting a top-down rather than bottom-up view for the aetiology of alcohol dependence

Immunologische Kreuzreaktivität zwischen dem Myelin-Oligodendrozyten-Glykoprotein (MOG) und Butyrophilin (BTN) bei Multipler Sklerose

The aetiology of multiple sclerosis (MS) is believed to involve environmental factors that disrupt self-tolerance to myelin autoantigens but their identity and mode of action are unknown. This study reports that the epitope specificity of autoantibodies to the myelin oligodendrocyte glycoprotein (MOG), an important candidate autoantigen in MS, is heterogeneous and that MOG exhibits extensive immunological cross-reactivity with the bovine milk protein butyrophilin (BTN), an ubiquitous dietary antigen. In a subset of MS patients this cross-reactive antibody response is significantly enhanced and are selectively sequestered in the central nervous system (CNS) suggesting that this cross-reactive immune response may play an active role in the pathogenesis of MS. Immunological cross-reactivity between MOG and BTN may modulate the composition and pathogenicity of the MOG-specific autoimmune response. In one scenario this response might be innocuous or even protective due to the induction of oral tolerance by BTN that cross-reacts with MOG. Alternatively, cross-reactive antibodies and pro-inflammatory Th1 T cell responses may target humoral and cellular effector mechanisms to attack white matter tracts in the CNS of susceptible patients.Es wird angenommen, dass an der Ätiologie der Multiplen Sklerose (MS) Umweltfaktoren beteiligt sind, die die Selbst-Toleranz gegen Auto-Antigene innerhalb des Myelins unterbrechen. Die Identität und Wirkungsweise dieser Umweltfaktoren sind aber unbekannt. Das Myelin-Oligodendrozyten-Protein (MOG) ist ein mögliches, wichtiges Auto-Antigen in der MS. Diese Arbeit zeigt, dass die Epitopspezifität von Auto-Antikörpern gegen MOG heterogen ist. Weiter wird gezeigt, dass MOG eine umfassende, immunologische Kreuzreaktivität mit dem bovinen Milch-Protein Butyrophilin (BTN) aufweist, einem ubiquitären, diätetischen Antigen. In einer Untergruppe von MS-Patienten ist diese kreuzreaktive Antikörper-Antwort signifikant erhöht und selektiv im zentralen Nervensystem (ZNS) sequestriert. Dieses Ergebnis weist darauf hin, dass diese kreuzreaktive Auto-Immunantwort eine aktive Rolle in der Pathogenese der MS spielen könnte. Möglicherweise moduliert immunologische Kreuzreaktivität zwischen MOG und BTN die Zusammensetzung und Pathogenität der MOG-spezifischen Auto-Immunantwort. In einem Szenario wäre diese Antwort harmlos oder sogar protektiv wegen der Induktion oraler Toleranz durch BTN, das mit MOG kreuzreagiert. Alternativ könnten kreuzreaktive Antikörper und pro-inflammatorische Th1-T-Zell-Antworten auf humorale und zelluläre Effektormechanismen gerichtet sein, die zu Angriffen auf Bahnen der weißen Substanz im ZNS von anfälligen Patienten führen