Belief in conspiracy theories : the predictive role of schizotypy, machiavellianism, and primary psychopathy
- Authors: March, Evita , Springer
- Date: 2019
- Type: Text , Journal article
- Relation: PLoS ONE Vol. 14, no. 12 (2019), p.
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- Description: A conspiracy theory refers to an alternative explanation of an event involving a conspirator plot organised by powerful people or organisations. Belief in conspiracy theories is related to negative societal outcomes such as poor medical decisions and a decrease in prosocial behaviour. Given these negative outcomes, researchers have explored predictors of belief in conspiracy theories in an attempt to understand and possibly manage these beliefs. In the current study, we explored the utility of personality in predicting belief in conspiracy theories. The aim of the current study was to explore the utility of the odd beliefs/magical thinking subtype of schizotypy, Machiavellianism, grandiose narcissism, vulnerable narcissism, primary psychopathy, and secondary psychopathy in predicting belief in conspiracy theories. Participants (N = 230; 44.7% male, 55.3% female) completed an anonymous, confidential online questionnaire which comprised demographics and measures of personality traits and belief in conspiracy theories. The total regression model indicated odd beliefs/magical thinking, trait Machiavellianism, and primary psychopathy were significant, positive predictors of belief in conspiracy theories. No other predictors reached significance. Results of the current study highlight individuals who might be more susceptible to believing conspiracy theories. Specifically, these results indicate that the individual more likely to believe in conspiracy theories may have unusual patterns of thinking and cognitions, be strategic and manipulative, and display interpersonal and affective deficits. © 2019 March, Springer. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
- Authors: March, Evita , Springer
- Date: 2019
- Type: Text , Journal article
- Relation: PLoS ONE Vol. 14, no. 12 (2019), p.
- Full Text:
- Reviewed:
- Description: A conspiracy theory refers to an alternative explanation of an event involving a conspirator plot organised by powerful people or organisations. Belief in conspiracy theories is related to negative societal outcomes such as poor medical decisions and a decrease in prosocial behaviour. Given these negative outcomes, researchers have explored predictors of belief in conspiracy theories in an attempt to understand and possibly manage these beliefs. In the current study, we explored the utility of personality in predicting belief in conspiracy theories. The aim of the current study was to explore the utility of the odd beliefs/magical thinking subtype of schizotypy, Machiavellianism, grandiose narcissism, vulnerable narcissism, primary psychopathy, and secondary psychopathy in predicting belief in conspiracy theories. Participants (N = 230; 44.7% male, 55.3% female) completed an anonymous, confidential online questionnaire which comprised demographics and measures of personality traits and belief in conspiracy theories. The total regression model indicated odd beliefs/magical thinking, trait Machiavellianism, and primary psychopathy were significant, positive predictors of belief in conspiracy theories. No other predictors reached significance. Results of the current study highlight individuals who might be more susceptible to believing conspiracy theories. Specifically, these results indicate that the individual more likely to believe in conspiracy theories may have unusual patterns of thinking and cognitions, be strategic and manipulative, and display interpersonal and affective deficits. © 2019 March, Springer. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
A feature agnostic approach for glaucoma detection in OCT volumes
- Maetschke, Stefan, Antony, Bhavna, Ishikawa, Hiroshi, Wollstein, Gadi, Schuman, Joel, Garnavi, Rahil
- Authors: Maetschke, Stefan , Antony, Bhavna , Ishikawa, Hiroshi , Wollstein, Gadi , Schuman, Joel , Garnavi, Rahil
- Date: 2019
- Type: Text , Journal article
- Relation: PLoS One Vol. 14, no. 7 (2019), p. e0219126
- Full Text:
- Reviewed:
- Description: Optical coherence tomography (OCT) based measurements of retinal layer thickness, such as the retinal nerve fibre layer (RNFL) and the ganglion cell with inner plexiform layer (GCIPL) are commonly employed for the diagnosis and monitoring of glaucoma. Previously, machine learning techniques have relied on segmentation-based imaging features such as the peripapillary RNFL thickness and the cup-to-disc ratio. Here, we propose a deep learning technique that classifies eyes as healthy or glaucomatous directly from raw, unsegmented OCT volumes of the optic nerve head (ONH) using a 3D Convolutional Neural Network (CNN). We compared the accuracy of this technique with various feature-based machine learning algorithms and demonstrated the superiority of the proposed deep learning based method. Logistic regression was found to be the best performing classical machine learning technique with an AUC of 0.89. In direct comparison, the deep learning approach achieved a substantially higher AUC of 0.94 with the additional advantage of providing insight into which regions of an OCT volume are important for glaucoma detection. Computing Class Activation Maps (CAM), we found that the CNN identified neuroretinal rim and optic disc cupping as well as the lamina cribrosa (LC) and its surrounding areas as the regions significantly associated with the glaucoma classification. These regions anatomically correspond to the well established and commonly used clinical markers for glaucoma diagnosis such as increased cup volume, cup diameter, and neuroretinal rim thinning at the superior and inferior segments.
- Authors: Maetschke, Stefan , Antony, Bhavna , Ishikawa, Hiroshi , Wollstein, Gadi , Schuman, Joel , Garnavi, Rahil
- Date: 2019
- Type: Text , Journal article
- Relation: PLoS One Vol. 14, no. 7 (2019), p. e0219126
- Full Text:
- Reviewed:
- Description: Optical coherence tomography (OCT) based measurements of retinal layer thickness, such as the retinal nerve fibre layer (RNFL) and the ganglion cell with inner plexiform layer (GCIPL) are commonly employed for the diagnosis and monitoring of glaucoma. Previously, machine learning techniques have relied on segmentation-based imaging features such as the peripapillary RNFL thickness and the cup-to-disc ratio. Here, we propose a deep learning technique that classifies eyes as healthy or glaucomatous directly from raw, unsegmented OCT volumes of the optic nerve head (ONH) using a 3D Convolutional Neural Network (CNN). We compared the accuracy of this technique with various feature-based machine learning algorithms and demonstrated the superiority of the proposed deep learning based method. Logistic regression was found to be the best performing classical machine learning technique with an AUC of 0.89. In direct comparison, the deep learning approach achieved a substantially higher AUC of 0.94 with the additional advantage of providing insight into which regions of an OCT volume are important for glaucoma detection. Computing Class Activation Maps (CAM), we found that the CNN identified neuroretinal rim and optic disc cupping as well as the lamina cribrosa (LC) and its surrounding areas as the regions significantly associated with the glaucoma classification. These regions anatomically correspond to the well established and commonly used clinical markers for glaucoma diagnosis such as increased cup volume, cup diameter, and neuroretinal rim thinning at the superior and inferior segments.
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