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Adversarial vulnerabilities of human decision-making

Adversarial examples are carefully crafted input patterns that are surprisingly poorly classified by artificial and/or natural neural networks. Here we examine adversarial vulnerabilities in the processes responsible for learning and choice in humans. Building upon recent recurrent neural network mo...

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Detalles Bibliográficos
Autores principales: Dezfouli, Amir, Nock, Richard, Dayan, Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7682379/
https://www.ncbi.nlm.nih.gov/pubmed/33148802
http://dx.doi.org/10.1073/pnas.2016921117
Descripción
Sumario:Adversarial examples are carefully crafted input patterns that are surprisingly poorly classified by artificial and/or natural neural networks. Here we examine adversarial vulnerabilities in the processes responsible for learning and choice in humans. Building upon recent recurrent neural network models of choice processes, we propose a general framework for generating adversarial opponents that can shape the choices of individuals in particular decision-making tasks toward the behavioral patterns desired by the adversary. We show the efficacy of the framework through three experiments involving action selection, response inhibition, and social decision-making. We further investigate the strategy used by the adversary in order to gain insights into the vulnerabilities of human choice. The framework may find applications across behavioral sciences in helping detect and avoid flawed choice.