Metacognitive function and fragmentation in schizophrenia: Relationship to cognition, self-experience and developing treatments

Bleuler suggested that fragmentation of thought, emotion and volition were the unifying feature of the disorders he termed schizophrenia. In this paper we review research seeking to measure some of the aspects of fragmentation related to the experience of the self and others described by Bleuler. We focus on work which uses the concept of metacognition to characterize and quantify alterations or decrements in the processes by which fragments or pieces of information are integrated into a coherent sense of self and others. We describe the rationale and support for one method for quantifying metacognition and its potential to study the fragmentation of a person\u27s sense of themselves, others and the relative place of themselves and others in the larger human community. We summarize research using that method which suggests that deficits in metacognition commonly occur in schizophrenia and are related to basic neurobiological indices of brain functioning. We also present findings indicating that the capacity for metacognition in schizophrenia is positively related to a broad range of aspects of psychological and social functioning when measured concurrently and prospectively. Finally, we discuss the evolution and study of one therapy that targets metacognitive capacity, Metacognitive Reflection and Insight Therapy (MERIT) and its potential to treat fragmentation and promote recovery

Metacognitive deficits and social functioning in schizophrenia across symptom profiles: A latent class analysis

Functional deficits are a hallmark of schizophrenia spectrum disorders, but much debate still exists over why and how they originate. One model suggests that disturbances in social functioning are a result of metacognitive deficits or a failure to integrate information to form more complex ideas of themselves and others. It is unclear if this social dysfunction is present across different symptom presentations. We examined the relationship of metacognition, symptoms, and social functioning among a sample of adults with schizophrenia spectrum disorders (N ¼ 334). A latent class analysis produced a four-class model. Groups were classified as follows: diffuse symptoms/moderately impaired social functioning (Class 1), positive and hostility symptoms/mildly impaired social functioning (Class 2), minimal symptoms/good social functioning (Class 3), and negative and cognitive symptoms/severely impaired social functioning (Class 4). Class 3 demonstrated better overall metacognitive capacity than both Classes 1 and 4 but did not differ significantly from Class 2. Classes 2 and 3 both demonstrated better interpersonal functioning than Classes 1 and 4. Together, these findings provide support for models of poor functioning that stem from fragmentation of an individual’s experience, leading to diminished abilities to form meaningful connections with others. Additional interpretations, limitations, and research implications are discussed

The Independent Relationships of Metacognition, Mindfulness, and Cognitive Insight to Self-Compassion in Schizophrenia

The debilitating nature of psychosis may be exacerbated by societal stigma and feelings of social isolation over and above positive (e.g., hallucinations) and negative (e.g., flat affect) symptoms. Thus, recovery may be facilitated by increasing self-compassion, the ability to respond with a nonjudgmental attitude of kindness toward oneself as a result of connecting with one's own inadequacies and suffering. We conducted a stepwise regression in individuals with schizophrenia-spectrum disorders (n = 92) to determine the unique contributions of cognitive variables in predicting self-compassion, such as metacognition (the ability to form complex and integrated ideas about oneself and others), mindfulness, and cognitive insight. Results indicated that increased metacognitive awareness of others and mindfulness uniquely predicted greater self-compassion (i.e., self-kindness), whereas increased cognitive insight predicted greater lack of self-compassion (i.e., self-judgment). These findings suggest the potential for mindfulness and metacognitive interventions to increase positive self-compassion and promote recovery in psychosis

Translational NLP: a new paradigm and general principles for natural language processing research

Natural language processing (NLP) research combines the study of universal principles, through basic science, with applied science targeting specific use cases and settings. However, the process of exchange between basic NLP and applications is often assumed to emerge naturally, resulting in many innovations going unapplied and many important questions left unstudied. We describe a new paradigm of Translational NLP, which aims to structure and facilitate the processes by which basic and applied NLP research inform one another. Translational NLP thus presents a third research paradigm, focused on understanding the challenges posed by application needs and how these challenges can drive innovation in basic science and technology design. We show that many significant advances in NLP research have emerged from the intersection of basic principles with application needs, and present a conceptual framework outlining the stakeholders and key questions in translational research. Our framework provides a roadmap for developing Translational NLP as a dedicated research area, and identifies general translational principles to facilitate exchange between basic and applied research

Dynamic Querying for Pattern Identification in Microarray and Genomic Data (2003)

Data sets involving linear ordered sequences are a recurring theme in bioinformatics. Dynamic query tools that support exploration of these data sets can be useful for identifying patterns of interest. This paper describes the use of one such tool TimeSearcher - to interactively explore linear sequence data sets taken from two bioinformatics problems. Microarray time course data sets involve expression levels for large numbers of genes over multiple time points. TimeSearcher can be used to interactively search these data sets for genes with expression profiles of interest. The occurrence frequencies of short sequences of DNA in aligned exons can be used to identify sequences that play a role in the pre-mRNA splicing. TimeSearcher can be used to search these data sets for candidate splicing signals

Computer-supported feedback message tailoring: Theory-informed adaptation of clinical audit and feedback for learning and behavior change

Background: Evidence shows that clinical audit and feedback can significantly improve compliance with desired practice, but it is unclear when and how it is effective. Audit and feedback is likely to be more effective when feedback messages can influence barriers to behavior change, but barriers to change differ across individual health-care providers, stemming from differences in providers' individual characteristics. Discussion: The purpose of this article is to invite debate and direct research attention towards a novel audit and feedback component that could enable interventions to adapt to barriers to behavior change for individual health-care providers: computer-supported tailoring of feedback messages. We argue that, by leveraging available clinical data, theory-informed knowledge about behavior change, and the knowledge of clinical supervisors or peers who deliver feedback messages, a software application that supports feedback message tailoring could improve feedback message relevance for barriers to behavior change, thereby increasing the effectiveness of audit and feedback interventions. We describe a prototype system that supports the provision of tailored feedback messages by generating a menu of graphical and textual messages with associated descriptions of targeted barriers to behavior change. Supervisors could use the menu to select messages based on their awareness of each feedback recipient's specific barriers to behavior change. We anticipate that such a system, if designed appropriately, could guide supervisors towards giving more effective feedback for health-care providers. Summary: A foundation of evidence and knowledge in related health research domains supports the development of feedback message tailoring systems for clinical audit and feedback. Creating and evaluating computer-supported feedback tailoring tools is a promising approach to improving the effectiveness of clinical audit and feedback

Definition drives design: disability models and mechanisms of bias in AI technologies

The increasing deployment of artificial intelligence (AI) tools to inform decision-making across diverse areas including healthcare, employment, social benefits, and government policy, presents a serious risk for disabled people, who have been shown to face bias in AI implementations. While there has been significant work on analysing and mitigating algorithmic bias, the broader mechanisms of how bias emerges in AI applications are not well understood, hampering efforts to address bias where it begins. In this article, we illustrate how bias in AI-assisted decision-making can arise from a range of specific design decisions, each of which may seem self-contained and non-biasing when considered separately. These design decisions include basic problem formulation, the data chosen for analysis, the use the AI technology is put to, and operational design elements in addition to the core algorithmic design. We draw on three historical models of disability common to different decision-making settings to demonstrate how differences in the definition of disability can lead to highly distinct decisions on each of these aspects of design, leading in turn to AI technologies with a variety of biases and downstream effects. We further show that the potential harms arising from inappropriate definitions of disability in fundamental design stages are further amplified by alack of transparency and disabled participation throughout the AI design process. Our analysis provides a framework for critically examining AI technologies in decision-making contexts and guiding the development of a design praxis for disability-related AI analytics. We put forth this article to provide key questions to facilitate disability-led design and participatory development to produce more fair and equitable AI technologies in disability-related contexts

TextEssence: a tool for interactive analysis of semantic shifts between corpora

Embeddings of words and concepts capture syntactic and semantic regularities of language; however, they have seen limited use as tools to study characteristics of different corpora and how they relate to one another. We introduce TextEssence, an interactive system designed to enable comparative analysis of corpora using embeddings. TextEssence includes visual, neighbor-based, and similarity-based modes of embedding analysis in a lightweight, web-based interface. We further propose a new measure of embedding confidence based on nearest neighborhood overlap, to assist in identifying high-quality embeddings for corpus analysis. A case study on COVID-19 scientific literature illustrates the utility of the system. TextEssence can be found at https://textessence.github.io

Piecing together fragments: Linguistic cohesion mediates the relationship between executive function and metacognition in schizophrenia

Speech disturbances are prevalent in psychosis. These may arise in part from executive function impairment, as research suggests that inhibition and monitoring are associated with production of cohesive discourse. However, it is not yet understood how linguistic and executive function impairments in psychosis interact with disrupted metacognition, or deficits in the ability to integrate information to form a complex sense of oneself and others and use that synthesis to respond to psychosocial challenges. Whereas discourse studies have historically employed manual hand-coding techniques, automated computational tools can characterize deep semantic structures that may be closely linked with metacognition. In the present study, we examined whether higher executive functioning promotes metacognition by way of altering linguistic cohesion. Ninety-four individuals with schizophrenia-spectrum disorders provided illness narratives and completed an executive function task battery (Delis-Kaplan Executive Function System). We assessed the narratives for linguistic cohesion (Coh-Metrix 3.0) and metacognitive capacity (Metacognition Assessment Scale – Abbreviated). Selected linguistic indices measured the frequency of connections between causal and intentional content (deep cohesion), word and theme overlap (referential cohesion), and unique word usage (lexical diversity). In path analyses using bootstrapped confidence intervals, we found that deep cohesion and lexical diversity independently mediated the relationship between executive functioning and metacognitive capacity. Findings suggest that executive control abilities support integration of mental experiences by way of increasing causal, goal-driven speech and word expression in individuals with schizophrenia. Metacognitive-based therapeutic interventions for psychosis may promote insight and recovery in part by scaffolding use of language that links ideas together

GiViP: A Visual Profiler for Distributed Graph Processing Systems

Analyzing large-scale graphs provides valuable insights in different application scenarios. While many graph processing systems working on top of distributed infrastructures have been proposed to deal with big graphs, the tasks of profiling and debugging their massive computations remain time consuming and error-prone. This paper presents GiViP, a visual profiler for distributed graph processing systems based on a Pregel-like computation model. GiViP captures the huge amount of messages exchanged throughout a computation and provides an interactive user interface for the visual analysis of the collected data. We show how to take advantage of GiViP to detect anomalies related to the computation and to the infrastructure, such as slow computing units and anomalous message patterns.Comment: Appears in the Proceedings of the 25th International Symposium on Graph Drawing and Network Visualization (GD 2017