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Conditional reasoning premises can be systematically manipulated to elicit specific response patterns. This is useful for investigating the reasoning style of people who report clinical symptoms. We administered a standardized conditional reasoning task to 16 participants with a diagnosis of Asperger's syndrome (AS), 16 participants with a diagnosis of depersonalization disorder (DPD), and 32 intelligence-quotient-matched controls. Premises were manipulated for a) context, with some being embedded within extra statements, and b) content, neutral or emotional. Both the AS and DPD participants were less likely to incorporate exceptions to the given premises than the controls, indicating difficulties with mental flexibility, although this effect was less marked in the DPD group. It seems the AS participants were also less influenced than the controls by statements that highlight possible alternative consequences. However, this effect was less robust than that observed with statements detailing exceptions, suggesting it may be because of general problems with executive function rather than difficulties in processing contextual information. We did not observe the expected difference between the DPD participants and the controls when reasoning with emotional premises. Overall, these data suggest that the DPD and AS participants have distinct reasoning styles, which may be of use for interventions based on cognitive change.
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http://dx.doi.org/10.1097/NMD.0b013e318266ba2b | DOI Listing |
Diagnostics (Basel)
August 2025
Faculty of Mechanical Technology and Engineering, Universiti Teknikal Malaysia Melaka, Melaka 75450, Malaysia.
This study presents a survey-based evaluation of an explainable AI (Feature-Augmented) approach, which was designed to support the diagnosis of Alzheimer's disease (AD) by integrating clinical data, MMSE scores, and MRI scans. The approach combines rule-based reasoning and example-based visualization to improve the explainability of AI-generated decisions. Five doctors participated in the survey: two with 6 to 10 years of experience and three with more than 10 years of experience in the medical field and expertise in AD.
View Article and Find Full Text PDFEntropy (Basel)
July 2025
Adobe, San Francisco, CA 94103, USA.
We propose a novel interpretability framework that integrates instance-wise feature selection with causal reasoning to explain decisions made by black-box image classifiers. Instead of relying on feature importance or mutual information, our method identifies input regions that exert the greatest causal influence on model predictions. Causal influence is formalized using a structural causal model and quantified via a conditional mutual information term.
View Article and Find Full Text PDFTraffic Inj Prev
August 2025
School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, China.
Objective: This study aims to develop a knowledge graph (KG)-based framework to quantify and analyze the impact of hazardous driving behaviors on road transport safety.
Method: A top-down approach was adopted to construct a multilayered KG incorporating seven categories of hazardous behavior factors (C1-C7). Multisource accident datasets were integrated to map the relationships among hazardous behavior factors, accident types, and accident causes.
Introduction: As digital health and artificial intelligence (AI) become integral to medicine, there is a growing need for physicians to develop computational thinking skills. In vascular neurology, a specialty reliant on algorithmic decision-making and complex data interpretation, programming logic (PL) offers a powerful cognitive framework. This review argues that PL can enhance diagnostic precision, clinical efficiency, and data-driven reasoning.
View Article and Find Full Text PDFDigital conditional-logic functions are essential for decision-making algorithms and computational deductive reasoning. However, research on all-optical fiber-based conditional-logic devices is very scarce and generally proposes single-function nonlinear devices that are expensive and hard to manufacture. In this paper, we present the first, to our knowledge, numerical acquisition of a configurable conditional-logic multi-functional OR/AND-IMPLY logic gate using a linear optical-fiber-based device.
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