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Andreas Pedroni
Researcher at University of Zurich
Publications - 27
Citations - 1490
Andreas Pedroni is an academic researcher from University of Zurich. The author has contributed to research in topics: Eye tracking & Small-world network. The author has an hindex of 17, co-authored 25 publications receiving 1101 citations. Previous affiliations of Andreas Pedroni include University College London & University of Basel.
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Journal ArticleDOI
Risk preference shares the psychometric structure of major psychological traits
Renato Frey,Renato Frey,Andreas Pedroni,Andreas Pedroni,Rui Mata,Jörg Rieskamp,Ralph Hertwig +6 more
TL;DR: These findings offer a first step toward a general mapping of the construct risk preference, which encompasses both general and domain-specific components, and have implications for the assessment of risk preference in the laboratory and in the wild.
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Functional brain network efficiency predicts intelligence
Nicolas Langer,Andreas Pedroni,Andreas Pedroni,Lorena R. R. Gianotti,Jürgen Hänggi,Daria Knoch,Lutz Jäncke +6 more
TL;DR: This is the first study that substantiates the neural efficiency hypothesis as well as the Parieto‐Frontal Integration Theory (P‐FIT) of intelligence in the context of functional brain network characteristics and their relation to psychometric intelligence.
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Automagic: Standardized preprocessing of big EEG data
TL;DR: This examination suggests that applying a pipeline of algorithms to detect artifactual channels in combination with Multiple Artifact Rejection Algorithm (MARA), an independent component analysis (ICA)-based artifact correction method, is sufficient to reduce a large extent of artifacts.
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The risk elicitation puzzle
Andreas Pedroni,Andreas Pedroni,Renato Frey,Adrian Bruhin,Gilles Dutilh,Ralph Hertwig,Jörg Rieskamp +6 more
TL;DR: Examining the across-methods consistency of observed risk preferences in 1,507 healthy participants using six EMs suggests that risk preferences may be constructed when they are elicited, and different cognitive processes can lead to varying preferences.
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The problem of thresholding in small-world network analysis.
TL;DR: Potential consequences of the number of thresholds and non-independency of samples are demonstrated in two examples (using artificial data and EEG data) and alternative approaches are presented, which overcome these methodological issues.