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Syllogisms delivered in an angry voice lead to improved performance and engagement of a different neural system compared to neutral voice.

ABSTRACT: Despite the fact that most real-world reasoning occurs in some emotional context, very little is known about the underlying behavioral and neural implications of such context. To further understand the role of emotional context in logical reasoning we scanned 15 participants with fMRI while they engaged in logical reasoning about neutral syllogisms presented through the auditory channel in a sad, angry, or neutral tone of voice. Exposure to angry voice led to improved reasoning performance compared to exposure to sad and neutral voice. A likely explanation for this effect is that exposure to expressions of anger increases selective attention toward the relevant features of target stimuli, in this case the reasoning task. Supporting this interpretation, reasoning in the context of angry voice was accompanied by activation in the superior frontal gyrus-a region known to be associated with selective attention. Our findings contribute to a greater understanding of the neural processes that underlie reasoning in an emotional context by demonstrating that two emotional contexts, despite being of the same (negative) valence, have different effects on reasoning.

SUBMITTER: Smith KW

PROVIDER: S-EPMC4428274 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Syllogisms delivered in an angry voice lead to improved performance and engagement of a different neural system compared to neutral voice.

Smith Kathleen W KW Balkwill Laura-Lee LL Vartanian Oshin O Goel Vinod V

Frontiers in human neuroscience 20150512

Despite the fact that most real-world reasoning occurs in some emotional context, very little is known about the underlying behavioral and neural implications of such context. To further understand the role of emotional context in logical reasoning we scanned 15 participants with fMRI while they engaged in logical reasoning about neutral syllogisms presented through the auditory channel in a sad, angry, or neutral tone of voice. Exposure to angry voice led to improved reasoning performance compa ...[more]

PMID: 26029089

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Project description:ImportanceMillions of clinicians rely daily on automated preliminary electrocardiogram (ECG) interpretation. Critical comparisons of machine learning-based automated analysis against clinically accepted standards of care are lacking.ObjectiveTo use readily available 12-lead ECG data to train and apply an explainability technique to a convolutional neural network (CNN) that achieves high performance against clinical standards of care.Design, setting, and participantsThis cross-sectional study was conducted using data from January 1, 2003, to December 31, 2018. Data were obtained in a commonly available 12-lead ECG format from a single-center tertiary care institution. All patients aged 18 years or older who received ECGs at the University of California, San Francisco, were included, yielding a total of 365 009 patients. Data were analyzed from January 1, 2019, to March 2, 2021.ExposuresA CNN was trained to predict the presence of 38 diagnostic classes in 5 categories from 12-lead ECG data. A CNN explainability technique called LIME (Linear Interpretable Model-Agnostic Explanations) was used to visualize ECG segments contributing to CNN diagnoses.Main outcomes and measuresArea under the receiver operating characteristic curve (AUC), sensitivity, and specificity were calculated for the CNN in the holdout test data set against cardiologist clinical diagnoses. For a second validation, 3 electrophysiologists provided consensus committee diagnoses against which the CNN, cardiologist clinical diagnosis, and MUSE (GE Healthcare) automated analysis performance was compared using the F1 score; AUC, sensitivity, and specificity were also calculated for the CNN against the consensus committee.ResultsA total of 992 748 ECGs from 365 009 adult patients (mean [SD] age, 56.2 [17.6] years; 183 600 women [50.3%]; and 175 277 White patients [48.0%]) were included in the analysis. In 91 440 test data set ECGs, the CNN demonstrated an AUC of at least 0.960 for 32 of 38 classes (84.2%). Against the consensus committee diagnoses, the CNN had higher frequency-weighted mean F1 scores than both cardiologists and MUSE in all 5 categories (CNN frequency-weighted F1 score for rhythm, 0.812; conduction, 0.729; chamber diagnosis, 0.598; infarct, 0.674; and other diagnosis, 0.875). For 32 of 38 classes (84.2%), the CNN had AUCs of at least 0.910 and demonstrated comparable F1 scores and higher sensitivity than cardiologists, except for atrial fibrillation (CNN F1 score, 0.847 vs cardiologist F1 score, 0.881), junctional rhythm (0.526 vs 0.727), premature ventricular complex (0.786 vs 0.800), and Wolff-Parkinson-White (0.800 vs 0.842). Compared with MUSE, the CNN had higher F1 scores for all classes except supraventricular tachycardia (CNN F1 score, 0.696 vs MUSE F1 score, 0.714). The LIME technique highlighted physiologically relevant ECG segments.Conclusions and relevanceThe results of this cross-sectional study suggest that readily available ECG data can be used to train a CNN algorithm to achieve comparable performance to clinical cardiologists and exceed the performance of MUSE automated analysis for most diagnoses, with some exceptions. The LIME explainability technique applied to CNNs highlights physiologically relevant ECG segments that contribute to the CNN's diagnoses.

Dataset Information

Syllogisms delivered in an angry voice lead to improved performance and engagement of a different neural system compared to neutral voice.

Publications

Syllogisms delivered in an angry voice lead to improved performance and engagement of a different neural system compared to neutral voice.

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