Sharon Chen ⁷ ☻

@sharon_chen_twt

Computational psychiatry & social/decision neuroscience PhD student . Researcher & admin [hiatus] . Analyst & developer .

Vrijeme pridruživanja: ožujak 2009.

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  1. proslijedio/la je Tweet
    29. sij

    Today I will be teaching my undergrad course "How to build a brain from scratch" for the 2nd year running. I've put the materials online - include a document with all lecture slides and notes, which is about as long as a decent novel. Enjoy!

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    There is no universal consensus on how to conduct and report Bayesian analyses. & colleagues present their views on the debate in a Comment, providing a thinking guideline for scientists wishing to employ Bayesian inference in their research

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    . et al, use large-scale multimodal data to elucidate key features and computational principles of face processing network, suggesting 3 core face processing streams.

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    Dopamine neuron populations don't just represent mean expected reward (scalar utility), but the expected distribution of reward (utility)

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  5. proslijedio/la je Tweet
    18. sij

    A thread of classifiers learning a decision rule. Dashed line is optimal boundary. Animations with by and . Logistic regression {stats::glm} with each class having normally distributed features. (1/n)

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  6. proslijedio/la je Tweet
    23. sij 2019.

    Twitter-sized history of neuroscience (biased by my interests).

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  7. proslijedio/la je Tweet
    16. sij

    Excited to announce that our new review paper (work with Uta Noppeney) is now out in , here:

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    All current successful quantum models for human cognition lack connections to neuroscience. Li et al. show that quantum reinforcement learning can explain value-based decision making at both the behavioural and neural levels.

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  9. proslijedio/la je Tweet
    20. sij

    Nature Communications Electrophysiological dynamics of antagonistic brain networks reflect attentional fluctuation

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  10. proslijedio/la je Tweet

    A Perspective in proposes a framework of information-seeking, whereby individuals decide to seek or avoid information based on combined estimates of the potential impact of information on their action, affect and cognition.

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  11. proslijedio/la je Tweet
    20. sij

    Cues that predict reward activate dopamine neurons but the function of this response remained unclear. This study demonstrates that inhibiting dopamine cue responses impairs second-order conditioning as predicted from TD learning theory. Elegant study!

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  12. proslijedio/la je Tweet
    20. sij

    and I used some style transfer AI to represent the Turtle Pond . We used the style of pioneering Brazilian modernist Tarsila do Amaral, in her piece "A Cuca" (1924)

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    How do people decide whether to seek information? Sharot and Sunstein propose a framework of information seeking that relies on estimates of the potential impact of information on action, affect, and cognition.

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  14. proslijedio/la je Tweet
    11. sij

    Dopamine Gates Visual Signals in Monkey Prefrontal Cortex Neurons

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  15. proslijedio/la je Tweet
    11. sij

    Very happy to have this out! Dopamine transients produce learning about antecedent cues, without making those cues valuable. Thread below summarizes our key points👇 Particularly proud as I spent experiment 3🤮 in a bin between squads while pregnant 😂

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  16. proslijedio/la je Tweet
    10. sij

    Nature Reviews Physics The physics of brain network structure, function and control

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  17. proslijedio/la je Tweet
    11. sij

    “..in learning situations appropriate for the appearance of a prediction error, dopamine transients support associative, rather than model-free, learning.” Dopamine transients do not act as model-free prediction errors during associative learning

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  18. proslijedio/la je Tweet

    Ten simple rules for the computational modeling of behavioral data from and

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  19. proslijedio/la je Tweet
    10. sij

    Striatal Dopamine D2 Receptors Regulate Cost Sensitivity and Behavioral Thrift

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  20. proslijedio/la je Tweet
    7. sij

    Very excited about our new paper: "Ventromedial prefrontal cortex compression during learning". vmPFC coding akin to goal-directed dimensionality reduction and tracks attention weights from learning models.

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