Introduction: There is substantial evidence that patients with delusions exhibit a reasoning bias--known as the "jumping to conclusions" (JTC) bias--which leads them to accept hypotheses as correct on the basis of less evidence than controls. We address three questions concerning the JTC bias that require clarification. Firstly, what is the best measure of the JTC bias? Second, is the JTC bias correlated specifically with delusions, or only with the symptomatology of schizophrenia? And third, is the bias enhanced by emotionally salient material?
Methods: To address these questions, we conducted a series of meta-analyses of studies that used the Beads task to compare the probabilistic reasoning styles of individuals with and without delusions.
Results: We found that only one of four measures of the JTC bias--"draws to decision"--reached significance. The JTC bias exhibited by delusional subjects-as measured by draws to decision--did not appear to be solely an epiphenomenal effect of schizophrenic symptomatology, and was not amplified by emotionally salient material.
Conclusions: A tendency to gather less evidence in the Beads task is reliably associated with the presence of delusional symptomatology. In contrast, certainty on the task, and responses to contradictory evidence, do not discriminate well between those with and without delusions. The implications for the underlying basis of the JTC bias, and its role in the formation and maintenance of delusions, are discussed.
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http://dx.doi.org/10.1080/13546800600750597 | DOI Listing |
Psychiatry Res
December 2024
Department of Psychology, Universidad Camilo José Cela, Madrid, Spain.
The Jumping to Conclusions bias (JTC), characterized by hasty decision-making with insufficient information, is associated with delusion development. The beads task, a standard measure for JTC, assesses information-gathering behaviour but may yield inconsistent results due to comprehension difficulties and limited engagement. To address these shortcomings, we developed the Real-Life Paradigm -two novel tasks simulating social scenarios, tested alongside the beads task under three experimental conditions (baseline, time pressure and cost-benefit), along with measures of psychotic experiences, emotional states, and task appraisal.
View Article and Find Full Text PDFPsychol Med
November 2024
Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, CIBERSAM, School of Medicine, Universidad Complutense, Madrid, Spain.
Background: Cannabis use and familial vulnerability to psychosis have been associated with social cognition deficits. This study examined the potential relationship between cannabis use and cognitive biases underlying social cognition and functioning in patients with first episode psychosis (FEP), their siblings, and controls.
Methods: We analyzed a sample of 543 participants with FEP, 203 siblings, and 1168 controls from the EU-GEI study using a correlational design.
Brief Bioinform
September 2024
Medical Imaging Research Center, University Hospitals Leuven, Herestraat 49, 3000 Leuven, Belgium.
Unsupervised learning, particularly clustering, plays a pivotal role in disease subtyping and patient stratification, especially with the abundance of large-scale multi-omics data. Deep learning models, such as variational autoencoders (VAEs), can enhance clustering algorithms by leveraging inter-individual heterogeneity. However, the impact of confounders-external factors unrelated to the condition, e.
View Article and Find Full Text PDFSchizophr Res
July 2024
Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany. Electronic address:
The jumping to conclusions (JTC) bias has been linked to the formation and maintenance of delusions across the psychosis spectrum. However, it remains unclear whether this bias reflects a primary cognitive deviation or is secondary to other cognitive processes. To this end, we investigated the relationship between JTC, risk-taking, impulsivity, and sensation seeking in individuals with psychotic-like experiences (PLEs) and controls.
View Article and Find Full Text PDFCell
May 2024
Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA. Electronic address:
Human tissue, which is inherently three-dimensional (3D), is traditionally examined through standard-of-care histopathology as limited two-dimensional (2D) cross-sections that can insufficiently represent the tissue due to sampling bias. To holistically characterize histomorphology, 3D imaging modalities have been developed, but clinical translation is hampered by complex manual evaluation and lack of computational platforms to distill clinical insights from large, high-resolution datasets. We present TriPath, a deep-learning platform for processing tissue volumes and efficiently predicting clinical outcomes based on 3D morphological features.
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