papers

An up-to-date list is available on Google Scholar.

preprints

2024

  1. Two routes to value-based decisions in Parkinsons disease: differentiating incremental reinforcement learning from episodic memory
    L Montaser-Kouhsari*, J Nicholas*, RT Gerraty, and D Shohamy
    (* denotes equal contribution), bioRxiv, 2024
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2023

  1. Proactive and reactive construction of memory-based preferences
    J Nicholas, ND Daw, and D Shohamy
    bioRxiv, 2023
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journal articles

2023

  1. The role of the cerebellum in learning to predict reward: evidence from cerebellar ataxia
    J Nicholas, C Amlang, CR Lin, L Montaser-Kouhsari, N Desai, M Pan, S Kuo, and D Shohamy
    The Cerebellum, 2023
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  2. Insights into the accuracy of social scientists’ forecasts of societal change
    The Forecasting Collaborative
    Nature Human Behaviour, 2023
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2022

  1. Uncertainty alters the balance between incremental learning and episodic memory
    J Nicholas, ND Daw, and D Shohamy
    eLife, 2022
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2021

  1. Linear and nonlinear profiles of weak behavioral and neural differentiation between numerical operations in children with math learning difficulties
    L Chen, T Iuculano, P Mistry, J Nicholas, Y Zhang, and V Menon
    Neuropsychologia, 2021
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2020

  1. Neural correlates of cognitive variability in childhood autism and relation to heterogeneity in decision-making dynamics
    T Iuculano, A Padmanabhan, L Chen, J Nicholas, S Mitsven, C Angeles, and V Menon
    Developmental Cognitive Neuroscience, 2020
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2018

  1. Uncovering hidden brain state dynamics that regulate performance and decision-making during cognition
    J Taghia, W Cai, S Ryali, J Kochalka, J Nicholas, T Chen, and V Menon
    Nature communications, 2018
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  2. Is Spatial Context Privileged in the Neural Representation of Events?
    HR Dimsdale-Zucker*, and J Nicholas*
    (* denotes equal contribution), Journal of Neuroscience, 2018
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2016

  1. Temporal dynamics and developmental maturation of salience, default and central-executive network interactions revealed by variational Bayes hidden Markov modeling
    S Ryali, K Supekar, T Chen, J Kochalka, W Cai, J Nicholas, A Padmanabhan, and V Menon
    PLoS computational biology, 2016
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