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Neural signatures of model-free learning when avoiding harm to self and other

https://doi.org/10.1101/718106
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Cardinal Welfare, Individualistic Ethics, and Interpersonal Comparisons of Utility
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Amygdala and Ventral Striatum Make Distinct Contributions to Reinforcement Learning
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WEIRD languages have misled us, too [Comment on Henrich et al.]
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Stimulation in the human somatosensory thalamus can reproduce both the affective and sensory dimensions of previously experienced pain
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Prosocial apathy for helping others when effort is required
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Disruption of the right temporoparietal junction with transcranial magnetic stimulation reduces the role of beliefs in moral judgments
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Working-memory capacity protects model-based learning from stress
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Harm to others outweighs harm to self in moral decision making
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Striatal structure and function predict individual biases in learning to avoid pain
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Diminishing Reciprocal Fairness by Disrupting the Right Prefrontal Cortex
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inferredcites →https://doi.org/10.1126/science.288.5472.1769openalex-snapshot
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inferredcites →https://doi.org/10.1146/annurev-neuro-080317-061450openalex-snapshot
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Model-based and model-free pain avoidance learning
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Neurocomputational mechanisms underlying subjective valuation of effort costs
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Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Deformed probability estimation in the goal-directed reinforcement learning model explains anxious-depression dimension of psychiatric disorders
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Breaking human social decision making into multiple components and then putting them together again
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The Anterior Cingulate Cortex Predicts Future States to Mediate Model-Based Action Selection
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Computational modelling of social cognition and behaviour—a reinforcement learning primer
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Using reinforcement learning models in social neuroscience: frameworks, pitfalls and suggestions of best practices
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tDCS effect on prosocial behavior: a meta-analytic review
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Anterior cingulate cortex represents action-state predictions and causally mediates model-based reinforcement learning in a two-step decision task
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