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  1. AI-Completeness: Using Deep Learning to Eliminate the Human Factor.Kristina Šekrst - 2020 - In Sandro Skansi (ed.), Guide to Deep Learning Basics. Springer. pp. 117-130.
    Computational complexity is a discipline of computer science and mathematics which classifies computational problems depending on their inherent difficulty, i.e. categorizes algorithms according to their performance, and relates these classes to each other. P problems are a class of computational problems that can be solved in polynomial time using a deterministic Turing machine while solutions to NP problems can be verified in polynomial time, but we still do not know whether they can be solved in polynomial time as well. A (...)
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  • Decoding Emotions in Expressive Music Performances: A Multi-Lab Replication and Extension Study.Jessica Akkermans, Renee Schapiro, Daniel Müllensiefen, Kelly Jakubowski, Daniel Shanahan, David Baker, Veronika Busch, Kai Lothwesen, Paul Elvers, Timo Fischinger, Kathrin Schlemmer & Klaus Frieler - 2018 - Cognition and Emotion 33 (6):1099-1118.
    ABSTRACTWith over 560 citations reported on Google Scholar by April 2018, a publication by Juslin and Gabrielsson presented evidence supporting performers’ abilities to communicate, with hig...
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  • Model‐Based Wisdom of the Crowd for Sequential Decision‐Making Tasks.Bobby Thomas, Jeff Coon, Holly A. Westfall & Michael D. Lee - 2021 - Cognitive Science 45 (7):e13011.
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  • A Model-Based Approach to the Wisdom of the Crowd in Category Learning.Irina Danileiko & Michael D. Lee - 2018 - Cognitive Science 42 (S3):861-883.
    We apply the “wisdom of the crowd” idea to human category learning, using a simple approach that combines people's categorization decisions by taking the majority decision. We first show that the aggregated crowd category learning behavior found by this method performs well, learning categories more quickly than most or all individuals for 28 previously collected datasets. We then extend the approach so that it does not require people to categorize every stimulus. We do this using a model-based method that predicts (...)
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  • Safe Online Ethical Code for and by the “Net Generation”: Themes Emerging From School Students’ Wisdom of the Crowd.Amit Lavie Dinur, Matan Aharoni & Yuval Karniel - 2021 - Journal of Information, Communication and Ethics in Society 19 (1):129-145.
    Purpose Children are becoming heavy users of communication and information technologies from an early age. These technologies carry risks to which children may be exposed. In collaboration with the Israel Ministry of Education, the authors launched a week-long safe online awareness program for school children in 257 elementary and middle schools in Israel. Each class independently composed a safe and ethical code of online behavior following two classroom debate sessions. The purpose of this study was to analyze these codes and (...)
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  • Can You Read My Mindprint?Lisa S. Pearl & Igii Enverga - 2014 - Interaction Studies. Social Behaviour and Communication in Biological and Artificial Systemsinteraction Studies / Social Behaviour and Communication in Biological and Artificial Systemsinteraction Studies 15 (3):359-387.
    Humans routinely transmit and interpret subtle information about their mental states through the language they use, even when only the language text is available. This suggests humans can utilize the linguistic signature of a mental state, comprised of features in the text. Once the relevant features are identified, mindprints can be used to automatically identify mental states communicated via language. We focus on the mindprints of eight mental states resulting from intentions, attitudes, and emotions, and present a mindprint-based machine learning (...)
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