A computational account of threat-related attentional bias
Author
Abstract
Suggested Citation
DOI: 10.1371/journal.pcbi.1007341
Download full text from publisher
References listed on IDEAS
- Athina Tzovara & Christoph W Korn & Dominik R Bach, 2018. "Human Pavlovian fear conditioning conforms to probabilistic learning," PLOS Computational Biology, Public Library of Science, vol. 14(8), pages 1-21, August.
- repec:cup:judgdm:v:3:y:2008:i::p:396-403 is not listed on IDEAS
- Archy O. de Berker & Robb B. Rutledge & Christoph Mathys & Louise Marshall & Gemma F. Cross & Raymond J. Dolan & Sven Bestmann, 2016. "Computations of uncertainty mediate acute stress responses in humans," Nature Communications, Nature, vol. 7(1), pages 1-11, April.
- Germain Lefebvre & Maël Lebreton & Florent Meyniel & Sacha Bourgeois-Gironde & Stefano Palminteri, 2017. "Behavioural and neural characterization of optimistic reinforcement learning," Nature Human Behaviour, Nature, vol. 1(4), pages 1-9, April.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Payam Piray & Nathaniel D Daw, 2020. "A simple model for learning in volatile environments," PLOS Computational Biology, Public Library of Science, vol. 16(7), pages 1-26, July.
- Corgnet, Brice & Hernán-González, Roberto & Kujal, Praveen, 2020.
"On booms that never bust: Ambiguity in experimental asset markets with bubbles,"
Journal of Economic Dynamics and Control, Elsevier, vol. 110(C).
- Brice Corgnet & Roberto Hernán-González & Praveen Kujal, 2018. "On Booms That Never Bust: Ambiguity in Experimental Asset Markets with Bubbles," Working Papers 1825, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
- Brice Corgnet & Roberto Hernán-Gonzalez & Praveen Kujal, 2020. "On booms that never bust: Ambiguity in experimental asset markets with bubbles," Post-Print halshs-03031385, HAL.
- Brice Corgnet & Roberto Hernán-Gonzalez & Praveen Kujal, 2018. "On Booms That Never Bust: Ambiguity in Experimental Asset Markets with Bubbles," Working Papers halshs-01898435, HAL.
- Brice Corgnet & Roberto Hernán-González & Praveen Kujal, 2018. "On Booms That Never Bust: Ambiguity in Experimental Asset Markets with Bubbles," Working Papers 18-15, Chapman University, Economic Science Institute.
- Daniel S Kluger & Nico Broers & Marlen A Roehe & Moritz F Wurm & Niko A Busch & Ricarda I Schubotz, 2020. "Exploitation of local and global information in predictive processing," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-17, April.
- Daniel J. Benjamin, 2018.
"Errors in Probabilistic Reasoning and Judgment Biases,"
NBER Working Papers
25200, National Bureau of Economic Research, Inc.
- Daniel J. Benjamin, 2018. "Errors in Probabilistic Reasoning and Judgment Biases," GRU Working Paper Series GRU_2018_023, City University of Hong Kong, Department of Economics and Finance, Global Research Unit.
- Brice Corgnet & Simon Gaechter & Roberto Hernan Gonzalez, 2020.
"Working Too Much for Too Little: Stochastic Rewards Cause Work Addiction,"
Discussion Papers
2020-03, The Centre for Decision Research and Experimental Economics, School of Economics, University of Nottingham.
- Brice Corgnet & Simon Gaechter & Roberto Hernán González, 2020. "Working Too Much for Too Little: Stochastic Rewards Cause Work Addiction," Working Papers 20-04, Chapman University, Economic Science Institute.
- Brice Corgnet & Simon Gaechter & Roberto Hernán González, 2020. "Working too much for too little: stochastic rewards cause work addiction," Working Papers halshs-02483337, HAL.
- Brice Corgnet & Simon Gaechter & Roberto Hernán González, 2020. "Working too much for too little: stochastic rewards cause work addiction," Working Papers 2007, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
- Corgnet, Brice & Gächter, Simon & González, Roberto Hernán, 2020. "Working Too Much for Too Little: Stochastic Rewards Cause Work Addiction," IZA Discussion Papers 12992, Institute of Labor Economics (IZA).
- Filip Gesiarz & Donal Cahill & Tali Sharot, 2019. "Evidence accumulation is biased by motivation: A computational account," PLOS Computational Biology, Public Library of Science, vol. 15(6), pages 1-15, June.
- Stefano Palminteri & Germain Lefebvre & Emma J Kilford & Sarah-Jayne Blakemore, 2017. "Confirmation bias in human reinforcement learning: Evidence from counterfactual feedback processing," PLOS Computational Biology, Public Library of Science, vol. 13(8), pages 1-22, August.
- Johann Lussange & Boris Gutkin, 2023. "Order book regulatory impact on stock market quality: a multi-agent reinforcement learning perspective," Papers 2302.04184, arXiv.org.
- Xinyi Li & Yinchuan Li & Yuancheng Zhan & Xiao-Yang Liu, 2019. "Optimistic Bull or Pessimistic Bear: Adaptive Deep Reinforcement Learning for Stock Portfolio Allocation," Papers 1907.01503, arXiv.org.
- Aurélien Nioche & Basile Garcia & Germain Lefebvre & Thomas Boraud & Nicolas P. Rougier & Sacha Bourgeois-Gironde, 2019.
"Coordination over a unique medium of exchange under information scarcity,"
Palgrave Communications, Palgrave Macmillan, vol. 5(1), pages 1-11, December.
- Aurélien Nioche & Basile Garcia & Germain Lefebvre & Thomas Boraud & Nicolas P. Rougier & Sacha Bourgeois-Gironde, 2019. "Coordination over a unique medium of exchange under information scarcity," Post-Print hal-02356248, HAL.
- Candace M. Raio & Benjamin B. Lu & Michael Grubb & Grant S. Shields & George M. Slavich & Paul Glimcher, 2022. "Cumulative lifetime stressor exposure assessed by the STRAIN predicts economic ambiguity aversion," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
- Riccardo Bruni & Alessandro Gioffré & Maria Marino, 2022.
""In-group bias in preferences for redistribution: a survey experiment in Italy","
IREA Working Papers
202223, University of Barcelona, Research Institute of Applied Economics, revised Nov 2023.
- Riccardo Bruni & Alessandro Gioffré & Maria Marino, 2023. "In-Group Bias in Preferences for Redistribution: A Survey Experiment in Italy," CESifo Working Paper Series 10785, CESifo.
- R Becket Ebitz & Brianna J Sleezer & Hank P Jedema & Charles W Bradberry & Benjamin Y Hayden, 2019. "Tonic exploration governs both flexibility and lapses," PLOS Computational Biology, Public Library of Science, vol. 15(11), pages 1-37, November.
- Payam Piray & Nathaniel D. Daw, 2024. "Computational processes of simultaneous learning of stochasticity and volatility in humans," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
- Filip Melinscak & Dominik R Bach, 2020. "Computational optimization of associative learning experiments," PLOS Computational Biology, Public Library of Science, vol. 16(1), pages 1-23, January.
- C. A. Tapia Cortez & J. Coulton & C. Sammut & S. Saydam, 2018. "Determining the chaotic behaviour of copper prices in the long-term using annual price data," Palgrave Communications, Palgrave Macmillan, vol. 4(1), pages 1-13, December.
- Payam Piray & Nathaniel D. Daw, 2021. "A model for learning based on the joint estimation of stochasticity and volatility," Nature Communications, Nature, vol. 12(1), pages 1-16, December.
- Johann Lussange & Stefano Vrizzi & Stefano Palminteri & Boris Gutkin, 2024. "Modelling crypto markets by multi-agent reinforcement learning," Papers 2402.10803, arXiv.org.
- Johann Lussange & Stefano Vrizzi & Stefano Palminteri & Boris Gutkin, 2024. "Mesoscale effects of trader learning behaviors in financial markets: A multi-agent reinforcement learning study," Post-Print hal-04790290, HAL.
- Chih-Chung Ting & Nahuel Salem-Garcia & Stefano Palminteri & Jan B. Engelmann & Maël Lebreton, 2023.
"Neural and computational underpinnings of biased confidence in human reinforcement learning,"
Nature Communications, Nature, vol. 14(1), pages 1-18, December.
- Chih-Chung Ting & Nahuel Salem-Garcia & Stefano Palminteri & Jan Engelmann & Maël Lebreton, 2023. "Neural and computational underpinnings of biased confidence in human reinforcement learning," PSE-Ecole d'économie de Paris (Postprint) halshs-04409145, HAL.
- Chih-Chung Ting & Nahuel Salem-Garcia & Stefano Palminteri & Jan Engelmann & Maël Lebreton, 2023. "Neural and computational underpinnings of biased confidence in human reinforcement learning," Post-Print halshs-04409145, HAL.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1007341. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.