Skip to Main Content

Advertisement

MIT Press Direct, home
header search
Search
Advanced Search
Skip Nav Destination

Learning to Become an Expert: Reinforcement Learning and the Acquisition of Perceptual Expertise

Journal of Cognitive Neuroscience (2009) 21 (9): 1833–1840.
This article has been cited by the following articles in journals that are participating in Crossref Cited-by Linking.
Stabilizing expectations when shifting from analytical to intuitive reasoning: The role of prediction errors in reasoning
  • Chad C. Williams
  • Cameron D. Hassall
  • Olave E. Krigolson
Cortex (2023) 161: 145.
Declarative Learning Mechanisms Support Declarative but Not Probabilistic Feedback-Based Learning in Children with Developmental Language Disorder (DLD)
  • Asiya Gul
  • Lauren S. Baron
  • Kelsey B. Black
  • Annika L. Schafer
  • Yael Arbel
Brain Sciences (2023) 13 (12): 1649.
The importance of ocular artifact removal in single-trial ERP analysis: The case of the N250 in face learning
  • Krzysztof Kotowski
  • Jeremi Ochab
  • Katarzyna Stapor
  • Werner Sommer
Biomedical Signal Processing and Control (2023) 79: 104115.
Change in Event-Related Potential accompanying View-invariant Object Discrimination Learning
  • Ridey Hsiao Wang
  • Jun-ya Okamura
  • Gang Wang
IEEJ Transactions on Electronics, Information and Systems (2022) 142 (5): 522.
Feedback Processing During Probabilistic Learning in Children With Developmental Language Disorder: An Event-Related Potential Study
  • Asiya Gul
  • Lauren Baron
  • Yael Arbel
Journal of Speech, Language, and Hearing Research (2022) 65 (6): 2272.
The Importance of Ocular Artifact Removal in Single-Trial ERP Analysis: The Case of the N250 in Face Learning
  • Krzysztof Kotowski
  • Jeremi Ochab
  • Katarzyna Stapor
  • Werner Sommer
SSRN Electronic Journal (2022)
Valence-dependent Neural Correlates of Augmented Feedback Processing in Extensive Motor Sequence Learning – Part I: Practice-related Changes of Feedback Processing
  • Linda Margraf
  • Daniel Krause
  • Matthias Weigelt
Neuroscience (2022) 486: 4.
A win-win situation: Does familiarity with a social robot modulate feedback monitoring and learning?
  • Abdulaziz Abubshait
  • Paul J. Beatty
  • Craig G. McDonald
  • Cameron D. Hassall
  • Olav E. Krigolson
  • Eva Wiese
Cognitive, Affective, & Behavioral Neuroscience (2021) 21 (4): 763.
Electrophysiological Examination of Feedback-Based Learning in 8–11-Year-Old Children
  • Yael Arbel
  • Annie B. Fox
Frontiers in Psychology (2021) 12
Examining Social Cognition with Embodied Robots: Does Prior Experience with a Robot Impact Feedback-associated Learning in a Gambling Task?
  • Abdulaziz Abubshait
  • Craig G. McDonald
  • Eva Wiese
Journal of Cognition (2021) 4 (1)
The aversion positivity: Mediofrontal cortical potentials reflect parametric aversive prediction errors and drive behavioral modification following negative reinforcement
  • Eric Rawls
  • Connie Lamm
Cortex (2021) 140: 26.
Learning With and Without Feedback in Children With Developmental Language Disorder
  • Yael Arbel
  • Isabel Fitzpatrick
  • Xinyi He
Journal of Speech, Language, and Hearing Research (2021) 64 (5): 1696.
What happens when right means wrong? The impact of conflict arising from competing feedback responses
  • Mathew R. Hammerstrom
  • Thomas D. Ferguson
  • Chad C. Williams
  • Olave E. Krigolson
Brain Research (2021) 1761: 147393.
Focused-attention meditation increases cognitive control during motor sequence performance: Evidence from the N2 cortical evoked potential
  • Russell W. Chan
  • Phillip M. Alday
  • Lena Zou-Williams
  • Kurt Lushington
  • Matthias Schlesewsky
  • Ina Bornkessel-Schlesewsky
  • Maarten A. Immink
Behavioural Brain Research (2020) 384: 112536.
An expert advantage in detecting unfamiliar visual signals in noise
  • Zahra Hussain
Proceedings of the National Academy of Sciences (2020) 117 (41): 25935.
Reward Prediction Errors Reflect an Underlying Learning Process That Parallels Behavioural Adaptations: A Trial-to-Trial Analysis
  • Chad C. Williams
  • Cameron D. Hassall
  • Talise Lindenbach
  • Olave E. Krigolson
Computational Brain & Behavior (2020) 3 (2): 189.
Non-invasive Neurophysiology in Learning and Training: Mechanisms and a SWOT Analysis
  • Angelica M. Tinga
  • Tycho T. de Back
  • Max M. Louwerse
Frontiers in Neuroscience (2020) 14
A tradeoff relationship between internal monitoring and external feedback during the dynamic process of reinforcement learning
  • Chunlei Liu
  • Zhenzhen Huo
International Journal of Psychophysiology (2020) 150: 11.
The impact of wellness on neural learning systems
  • Francisco L. Colino
  • Chad C. Williams
  • Cameron D. Hassall
  • Gordon Binsted
  • Olave E. Krigolson
Neuroscience Letters (2020) 714: 134537.
The effect of task difficulty on feedback processing in children
  • Y. Arbel
International Journal of Psychophysiology (2020) 153: 1.
Quantifying two-dimensional and three-dimensional stereoscopic learning in anatomy using electroencephalography
  • Sarah J. Anderson
  • Heather A. Jamniczky
  • Olave E. Krigolson
  • Sylvain P. Coderre
  • Kent G. Hecker
npj Science of Learning (2019) 4 (1)
High-intensity interval exercise impairs neuroelectric indices of reinforcement-learning
  • Jeremy J. Walsh
  • Francisco L. Colino
  • Olave E. Krigolson
  • Stephen Luehr
  • Brendon J. Gurd
  • Michael E. Tschakovsky
Physiology & Behavior (2019) 198: 18.
Long-Term Effects of Concussions on Psychomotor Speed and Cognitive Control Processes During Motor Sequence Learning
  • Christelle Beaulieu
  • Alexandre Turcotte-Giroux
  • Frédérike Carrier-Toutant
  • Benoit Brisson
  • Pierre Jolicoeur
  • Louis De Beaumont
Journal of Psychophysiology (2019) 33 (2): 96.
Hau-Kashyap approach for student’s level of expertise
  • Andino Maseleno
  • Miftachul Huda
  • Kamarul Azmi Jasmi
  • Bushrah Basiron
  • Ismail Mustari
  • Abdul Ghaffar Don
  • Roslee bin Ahmad
Egyptian Informatics Journal (2019) 20 (1): 27.
Non-invasive neurophysiological measures of learning: A meta-analysis
  • Angelica M. Tinga
  • Tycho T. de Back
  • Max M. Louwerse
Neuroscience & Biobehavioral Reviews (2019) 99: 59.
The application of reward learning in the real world: Changes in the reward positivity amplitude reflect learning in a medical education context
  • Chad C. Williams
  • Kent G. Hecker
  • Michael K. Paget
  • Sylvain P. Coderre
  • Kelly W. Burak
  • Bruce Wright
  • Olave E. Krigolson
International Journal of Psychophysiology (2018) 132: 236.
Alcohol hangover impacts learning and reward processing within the medial‐frontal cortex
  • Ashley D. Howse
  • Cameron D. Hassall
  • Chad C. Williams
  • Greg Hajcak
  • Olave E. Krigolson
Psychophysiology (2018) 55 (8)
Dynamics of error-related activity in deterministic learning - an EEG and fMRI study
  • Magda Gawlowska
  • Aleksandra Domagalik
  • Ewa Beldzik
  • Tadeusz Marek
  • Justyna Mojsa-Kaja
Scientific Reports (2018) 8 (1)
Developmental changes in the feedback related negativity from 8 to 14 years
  • Yael Arbel
  • Kayleigh N. McCarty
  • Mark Goldman
  • Emanuel Donchin
  • Ty Brumback
International Journal of Psychophysiology (2018) 132: 331.
A Reinforcement-Based Learning Paradigm Increases Anatomical Learning and Retention—A Neuroeducation Study
  • Sarah J. Anderson
  • Kent G. Hecker
  • Olave E. Krigolson
  • Heather A. Jamniczky
Frontiers in Human Neuroscience (2018) 12
Feedback Discounting in Probabilistic Categorization: Converging Evidence from EEG and Cognitive Modeling
  • David K. Sewell
  • Hayley A. Warren
  • Daniel Rosenblatt
  • Daniel Bennett
  • Maxwell Lyons
  • Stefan Bode
Computational Brain & Behavior (2018) 1 (2): 165.
Learning what matters: A neural explanation for the sparsity bias
  • Cameron D. Hassall
  • Patrick C. Connor
  • Thomas P. Trappenberg
  • John J. McDonald
  • Olave E. Krigolson
International Journal of Psychophysiology (2018) 127: 62.
Feedback information and the reward positivity
  • Jeffrey Cockburn
  • Clay B. Holroyd
International Journal of Psychophysiology (2018) 132: 243.
When theory and biology differ: The relationship between reward prediction errors and expectancy
  • Chad C. Williams
  • Cameron D. Hassall
  • Robert Trska
  • Clay B. Holroyd
  • Olave E. Krigolson
Biological Psychology (2017) 129: 265.
A Posteriori Ethical Intuitionism and the Problem of Cognitive Penetrability
  • Preston J. Werner
European Journal of Philosophy (2017) 25 (4): 1791.
Learning Anatomical Structures: a Reinforcement-Based Learning Approach
  • Sarah J. Anderson
  • Olave E. Krigolson
  • Heather A. Jamniczky
  • Kent G. Hecker
Medical Science Educator (2016) 26 (1): 123.
A Neurophysiological examination of quality of learning in a feedback-based learning task
  • Yael Arbel
  • Hao Wu
Neuropsychologia (2016) 93: 13.
Event-Related Potential Responses to Beloved and Familiar Faces in Different Marriage Styles: Evidence from Mosuo Subjects
  • Haiyan Wu
  • Li Luo
  • Junqiang Dai
  • Suyong Yang
  • Naiyi Wang
  • Yue-jia Luo
Frontiers in Psychology (2016) 7
Electrocortical signatures of detecting errors in the actions of others: An EEG study in pianists, non-pianist musicians and musically naïve people
  • M.S. Panasiti
  • E.F. Pavone
  • S.M. Aglioti
Neuroscience (2016) 318: 104.
The impact of cognitive load on reward evaluation
  • Olave E. Krigolson
  • Cameron D. Hassall
  • Jason Satel
  • Raymond M. Klein
Brain Research (2015) 1627: 225.
The Virtual Tray of Objects Task as a novel method to electrophysiologically measure visuo-spatial recognition memory
  • Francesco Amico
  • Ettore Ambrosini
  • François Guillem
  • Giovanni Mento
  • Dermot Power
  • Giulio Pergola
  • Antonino Vallesi
International Journal of Psychophysiology (2015) 98 (3): 477.
A biological mechanism for Bayesian feature selection: Weight decay and raising the LASSO
  • Patrick Connor
  • Paul Hollensen
  • Olav Krigolson
  • Thomas Trappenberg
Neural Networks (2015) 67: 121.
Rejection in Bargaining Situations: An Event-Related Potential Study in Adolescents and Adults
  • Kiki Zanolie
  • David de Cremer
  • Berna Güroğlu
  • Eveline A. Crone
  • Ruud van den Bos
PLOS ONE (2015) 10 (10): e0139953.
Reduced sensitivity to neutral feedback versus negative feedback in subjects with mild depression: Evidence from event-related potentials study
  • Peng Li
  • Xinxin Song
  • Jing Wang
  • Xiaoran Zhou
  • Jiayi Li
  • Fengtong Lin
  • Zhonghua Hu
  • Xinxin Zhang
  • Hewei Cui
  • Wenmiao Wang
  • Hong Li
  • Fengyu Cong
  • Debi Roberson
Brain and Cognition (2015) 100: 15.
Predicting expert–novice performance as serious games analytics with objective-oriented and navigational action sequences
  • Christian Sebastian Loh
  • Yanyan Sheng
  • I-Hung Li
Computers in Human Behavior (2015) 49: 147.
How We Learn to Make Decisions: Rapid Propagation of Reinforcement Learning Prediction Errors in Humans
  • Olav E. Krigolson
  • Cameron D. Hassall
  • Todd C. Handy
Journal of Cognitive Neuroscience (2014) 26 (3): 635.
On the Utility of Positive and Negative Feedback in a Paired-associate Learning Task
  • Yael Arbel
  • Anthony Murphy
  • Emanuel Donchin
Journal of Cognitive Neuroscience (2014) 26 (7): 1445.
Electrophysiological correlates of motor sequence learning
  • Christelle Beaulieu
  • Marie-Ève Bourassa
  • Benoit Brisson
  • Pierre Jolicoeur
  • Louis De Beaumont
BMC Neuroscience (2014) 15 (1)
Neural mechanisms and temporal dynamics of performance monitoring
  • Markus Ullsperger
  • Adrian G. Fischer
  • Roland Nigbur
  • Tanja Endrass
Trends in Cognitive Sciences (2014) 18 (5): 259.
Interaction between perceptual and cognitive processing well acknowledged in perceptual expertise research
  • Alan C.-N. Wong
  • Yetta K. Wong
Frontiers in Human Neuroscience (2014) 8

Advertisement

  • Online ISSN 1530-8898
  • Print ISSN 0898-929X
A product of The MIT Press
Newsletter sign up

MIT Press Direct

Information

MIT Press

Contact Us

© 2024 The MIT Press
Close Modal
This Feature Is Available To Subscribers Only

or Create an Account

Close Modal
Close Modal
This site uses cookies. By continuing to use our website, you are agreeing to our privacy policy. No content on this site may be used to train artificial intelligence systems without permission in writing from the MIT Press.
Accept