Computational Models of Brain-Wide Neuromodulation

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^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference} The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}...
^{2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference} The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}...
^{3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference} The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}...
^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference} The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}...
^{2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference} The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}...
^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}0 The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}1...
^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}2 The recent extension to distributional reinforcement learning represents a significant theoretical advance. Rather than encoding a single scalar prediction error, different dopamine neurons may encode prediction errors relative to different quantiles of the value distribution, collectively representing the full distribution of expected outcomes ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}3. ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}4 noted that the seminal reward...
^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}5 The recent extension to distributional reinforcement learning represents a significant theoretical advance. Rather than encoding a single scalar prediction error, different dopamine neurons may encode prediction errors relative to different quantiles of the value distribution, collectively representing the full distribution of expected outcomes ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}6. ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}7 noted that the seminal reward...
^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}8 The recent extension to distributional reinforcement learning represents a significant theoretical advance. Rather than encoding a single scalar prediction error, different dopamine neurons may encode prediction errors relative to different quantiles of the value distribution, collectively representing the full distribution of expected outcomes ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}9. ^{2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}0 noted that the seminal reward...
^{2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}1 Critically, the RPE framework’s success has shaped research allocation in ways that constrain the broader field. Dopamine’s computational role has been modeled with a precision that other modulators have not approached ^{2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}2. ^{2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}3 evaluated findings in the context of relevant computational models related to dopamine, serotonin, acetylcholine, and noradrenalin, highlighting the asymmetric d...
^{2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}4 Critically, the RPE framework’s success has shaped research allocation in ways that constrain the broader field. Dopamine’s computational role has been modeled with a precision that other modulators have not approached ^{2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}5. ^{2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}6 evaluated findings in the context of relevant computational models related to dopamine, serotonin, acetylcholine, and noradrenalin, highlighting the asymmetric d...
^{2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}7 Critically, the RPE framework’s success has shaped research allocation in ways that constrain the broader field. Dopamine’s computational role has been modeled with a precision that other modulators have not approached ^{2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}8. ^{2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}9 evaluated findings in the context of relevant computational models related to dopamine, serotonin, acetylcholine, and noradrenalin, highlighting the asymmetric d...
^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}0 Critically, the RPE framework’s success has shaped research allocation in ways that constrain the broader field. Dopamine’s computational role has been modeled with a precision that other modulators have not approached ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}1. ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}2 evaluated findings in the context of relevant computational models related to dopamine, serotonin, acetylcholine, and noradrenalin, highlighting the asymmetric d...
^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}3 Critically, the RPE framework’s success has shaped research allocation in ways that constrain the broader field. Dopamine’s computational role has been modeled with a precision that other modulators have not approached ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}4. ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}5 evaluated findings in the context of relevant computational models related to dopamine, serotonin, acetylcholine, and noradrenalin, highlighting the asymmetric d...
^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}6 Critically, the RPE framework’s success has shaped research allocation in ways that constrain the broader field. Dopamine’s computational role has been modeled with a precision that other modulators have not approached ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}7. ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}8 evaluated findings in the context of relevant computational models related to dopamine, serotonin, acetylcholine, and noradrenalin, highlighting the asymmetric d...
^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}9 Critically, the RPE framework’s success has shaped research allocation in ways that constrain the broader field. Dopamine’s computational role has been modeled with a precision that other modulators have not approached ^{3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}0. ^{3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}1 evaluated findings in the context of relevant computational models related to dopamine, serotonin, acetylcholine, and noradrenalin, highlighting the asymmetric d...
^{3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}2 Three-factor learning rules — in which Hebbian co-activation is gated by a dopaminergic eligibility trace — represent the primary mechanism by which RPE signals are thought to drive synaptic plasticity ^{3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}3. ^{3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}4 presented a computational model in which acetylcholine, released by striatal cholinergic interneurons, acts as a channel-specific gating signal that restricts...
^{3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}5 Three-factor learning rules — in which Hebbian co-activation is gated by a dopaminergic eligibility trace — represent the primary mechanism by which RPE signals are thought to drive synaptic plasticity ^{3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}6. ^{3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}7 presented a computational model in which acetylcholine, released by striatal cholinergic interneurons, acts as a channel-specific gating signal that restricts...
^{3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}8 Three-factor learning rules — in which Hebbian co-activation is gated by a dopaminergic eligibility trace — represent the primary mechanism by which RPE signals are thought to drive synaptic plasticity ^{3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}9. ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}0 presented a computational model in which acetylcholine, released by striatal cholinergic interneurons, acts as a channel-specific gating signal that restricts...
^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}1 Three-factor learning rules — in which Hebbian co-activation is gated by a dopaminergic eligibility trace — represent the primary mechanism by which RPE signals are thought to drive synaptic plasticity ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}2. ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}3 presented a computational model in which acetylcholine, released by striatal cholinergic interneurons, acts as a channel-specific gating signal that restricts...
^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}4 Beyond the RPE framework’s core predictions, computational models have been applied to increasingly complex decision-making scenarios that test the framework’s generality. ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}5 showed that decision making occurs in dynamic contexts in which individual and reward attributes change, requiring models that go beyond static RPE signals to capture the temporal evolution of value estimates. ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}6 analyzed t...
^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}7 Beyond the RPE framework’s core predictions, computational models have been applied to increasingly complex decision-making scenarios that test the framework’s generality. ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}8 showed that decision making occurs in dynamic contexts in which individual and reward attributes change, requiring models that go beyond static RPE signals to capture the temporal evolution of value estimates. ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}9 analyzed t...
^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}0 Beyond the RPE framework’s core predictions, computational models have been applied to increasingly complex decision-making scenarios that test the framework’s generality. ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}1 showed that decision making occurs in dynamic contexts in which individual and reward attributes change, requiring models that go beyond static RPE signals to capture the temporal evolution of value estimates. ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}2 analyzed t...
^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}3 Trial-by-trial dopamine signals track not only reward prediction errors but also social and non-reward variables, complicating the pure RPE account. ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}4 found that regardless of social context, relative changes in dopamine tracked trial-by-trial changes in reward expectations. ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}5 demonstrated that momentary happiness is associated with reward prediction error signals, extending the RPE framework be...
^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}6 Trial-by-trial dopamine signals track not only reward prediction errors but also social and non-reward variables, complicating the pure RPE account. ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}7 found that regardless of social context, relative changes in dopamine tracked trial-by-trial changes in reward expectations. ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}8 demonstrated that momentary happiness is associated with reward prediction error signals, extending the RPE framework be...
^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}9 Trial-by-trial dopamine signals track not only reward prediction errors but also social and non-reward variables, complicating the pure RPE account. ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}0 found that regardless of social context, relative changes in dopamine tracked trial-by-trial changes in reward expectations. ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}1 demonstrated that momentary happiness is associated with reward prediction error signals, extending the RPE framework be...
^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}2 Trial-by-trial dopamine signals track not only reward prediction errors but also social and non-reward variables, complicating the pure RPE account. ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}3 found that regardless of social context, relative changes in dopamine tracked trial-by-trial changes in reward expectations. ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}4 demonstrated that momentary happiness is associated with reward prediction error signals, extending the RPE framework be...
^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}5 Trial-by-trial dopamine signals track not only reward prediction errors but also social and non-reward variables, complicating the pure RPE account. ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}6 found that regardless of social context, relative changes in dopamine tracked trial-by-trial changes in reward expectations. ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}7 demonstrated that momentary happiness is associated with reward prediction error signals, extending the RPE framework be...
^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}8 Trial-by-trial dopamine signals track not only reward prediction errors but also social and non-reward variables, complicating the pure RPE account. ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}9 found that regardless of social context, relative changes in dopamine tracked trial-by-trial changes in reward expectations. ^{2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}0 demonstrated that momentary happiness is associated with reward prediction error signals, extending the RPE framework be...
^{2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}1 Trial-by-trial dopamine signals track not only reward prediction errors but also social and non-reward variables, complicating the pure RPE account. ^{2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}2 found that regardless of social context, relative changes in dopamine tracked trial-by-trial changes in reward expectations. ^{2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}3 demonstrated that momentary happiness is associated with reward prediction error signals, extending the RPE framework be...
^{2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}4 An influential normative framework assigns specific computational roles to acetylcholine and norepinephrine within Bayesian inference: acetylcholine signals expected uncertainty (the known unreliability of the current environment), while norepinephrine signals unexpected uncertainty (the detection that the environment has fundamentally changed) ^{2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}5. This framework is mathema...
^{2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}6 An influential normative framework assigns specific computational roles to acetylcholine and norepinephrine within Bayesian inference: acetylcholine signals expected uncertainty (the known unreliability of the current environment), while norepinephrine signals unexpected uncertainty (the detection that the environment has fundamentally changed) ^{2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}7. This framework is mathema...
^{2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}8 An influential normative framework assigns specific computational roles to acetylcholine and norepinephrine within Bayesian inference: acetylcholine signals expected uncertainty (the known unreliability of the current environment), while norepinephrine signals unexpected uncertainty (the detection that the environment has fundamentally changed) ^{2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}9. This framework is mathema...
^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}0 An influential normative framework assigns specific computational roles to acetylcholine and norepinephrine within Bayesian inference: acetylcholine signals expected uncertainty (the known unreliability of the current environment), while norepinephrine signals unexpected uncertainty (the detection that the environment has fundamentally changed) ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}1. This framework is mathema...
^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}2 An influential normative framework assigns specific computational roles to acetylcholine and norepinephrine within Bayesian inference: acetylcholine signals expected uncertainty (the known unreliability of the current environment), while norepinephrine signals unexpected uncertainty (the detection that the environment has fundamentally changed) ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}3. This framework is mathema...
^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}4 Recent work has begun to address this gap from the norepinephrine side. ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}5 provided quantitative evidence that frontal norepinephrine acts as a threat prediction error signal, fitting cue-evoked NE to a Rescorla-Wagner model with adjusted R² = 0.6869 for acquisition and adjusted R² = 0.6911 for extinction trials. Critically, NE poorly predicted behavioral freezing (adjusted R² = 0.0124), suggesting that NE e...
^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}6 Recent work has begun to address this gap from the norepinephrine side. ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}7 provided quantitative evidence that frontal norepinephrine acts as a threat prediction error signal, fitting cue-evoked NE to a Rescorla-Wagner model with adjusted R² = 0.6869 for acquisition and adjusted R² = 0.6911 for extinction trials. Critically, NE poorly predicted behavioral freezing (adjusted R² = 0.0124), suggesting that NE e...
^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}8 Recent work has begun to address this gap from the norepinephrine side. ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}9 provided quantitative evidence that frontal norepinephrine acts as a threat prediction error signal, fitting cue-evoked NE to a Rescorla-Wagner model with adjusted R² = 0.6869 for acquisition and adjusted R² = 0.6911 for extinction trials. Critically, NE poorly predicted behavioral freezing (adjusted R² = 0.0124), suggesting that NE e...
^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}00 Pharmacological studies in humans have provided additional evidence consistent with, but not uniquely predicted by, the Bayesian uncertainty framework. ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}01 used a double-blind, counterbalanced design with healthy adults to examine how pharmacological manipulation affects EEG signatures of prediction error processing, finding modulator-specific effects on neural responses to expectation violation. [Meijer2026...
^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}02 Pharmacological studies in humans have provided additional evidence consistent with, but not uniquely predicted by, the Bayesian uncertainty framework. ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}03 used a double-blind, counterbalanced design with healthy adults to examine how pharmacological manipulation affects EEG signatures of prediction error processing, finding modulator-specific effects on neural responses to expectation violation. [Meijer2026...
^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}04 Pharmacological studies in humans have provided additional evidence consistent with, but not uniquely predicted by, the Bayesian uncertainty framework. ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}05 used a double-blind, counterbalanced design with healthy adults to examine how pharmacological manipulation affects EEG signatures of prediction error processing, finding modulator-specific effects on neural responses to expectation violation. [Meijer2026...
^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}06 The active inference and predictive processing frameworks offer an alternative formalization in which neuromodulators regulate the precision (inverse variance) of predictions and prediction errors at different levels of the cortical hierarchy ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}07. ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}08 described how active inference frames Bayes-optimal behaviour as motivated by minimisation of variational...
^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}09 The active inference and predictive processing frameworks offer an alternative formalization in which neuromodulators regulate the precision (inverse variance) of predictions and prediction errors at different levels of the cortical hierarchy ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}10. ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}11 described how active inference frames Bayes-optimal behaviour as motivated by minimisation of variational...
^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}12 The active inference and predictive processing frameworks offer an alternative formalization in which neuromodulators regulate the precision (inverse variance) of predictions and prediction errors at different levels of the cortical hierarchy ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}13. ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}14 described how active inference frames Bayes-optimal behaviour as motivated by minimisation of variational...
^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}15 The active inference and predictive processing frameworks offer an alternative formalization in which neuromodulators regulate the precision (inverse variance) of predictions and prediction errors at different levels of the cortical hierarchy ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}16. ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}17 described how active inference frames Bayes-optimal behaviour as motivated by minimisation of variational...
^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}18 The active inference and predictive processing frameworks offer an alternative formalization in which neuromodulators regulate the precision (inverse variance) of predictions and prediction errors at different levels of the cortical hierarchy ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}19. ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}20 described how active inference frames Bayes-optimal behaviour as motivated by minimisation of variational...
^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}21 The active inference and predictive processing frameworks offer an alternative formalization in which neuromodulators regulate the precision (inverse variance) of predictions and prediction errors at different levels of the cortical hierarchy ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}22. ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}23 described how active inference frames Bayes-optimal behaviour as motivated by minimisation of variational...
^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}24 The active inference and predictive processing frameworks offer an alternative formalization in which neuromodulators regulate the precision (inverse variance) of predictions and prediction errors at different levels of the cortical hierarchy ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}25. ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}26 described how active inference frames Bayes-optimal behaviour as motivated by minimisation of variational...
^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}27 Multiple competing frameworks assign computational roles to norepinephrine: adaptive gain theory positions NE as optimizing the explore-exploit trade-off via gain modulation; the Bayesian uncertainty account treats NE as signaling unexpected uncertainty; and arousal optimization models describe NE as setting global arousal state along a Yerkes-Dodson curve ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}28. These framework...
^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}29 Multiple competing frameworks assign computational roles to norepinephrine: adaptive gain theory positions NE as optimizing the explore-exploit trade-off via gain modulation; the Bayesian uncertainty account treats NE as signaling unexpected uncertainty; and arousal optimization models describe NE as setting global arousal state along a Yerkes-Dodson curve ^{1Citationpaper:paper-09423488f618The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 2Citationpaper:paper-f4d141e3533bThe TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference, 3Citationpaper:paper-f79a5c0e1e09The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...content/11_computational_models.md:line 7Open reference}30. These framework...
... 96 additional anchors in refs_json

References

[Mah2024] paper:paper-09423488f618 “The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...”
[Qu2025] paper:paper-f4d141e3533b “The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...”
[Batten2024] paper:paper-f79a5c0e1e09 “The TD learning framework for dopamine remains the most quantitatively validated computational account of any neuromodulatory system. The core hypothesis — that phasic dopamine neuron firing encodes a reward prediction error (RPE) signal used to update value estimates — has accumulated converging evidence from electrophysiology, voltammetry, and computational modeling over three decades [Mah2024, Qu2025, Batten2024]...”
[Dabas2025] paper:paper-8b8a877ebd17 “The recent extension to distributional reinforcement learning represents a significant theoretical advance. Rather than encoding a single scalar prediction error, different dopamine neurons may encode prediction errors relative to different quantiles of the value distribution, collectively representing the full distribution of expected outcomes [Dabas2025, Mahajan2025b]. [Mahajan2025b] noted that the seminal reward...”
[Mahajan2025b] paper:paper-daa8021082e6 “The recent extension to distributional reinforcement learning represents a significant theoretical advance. Rather than encoding a single scalar prediction error, different dopamine neurons may encode prediction errors relative to different quantiles of the value distribution, collectively representing the full distribution of expected outcomes [Dabas2025, Mahajan2025b]. [Mahajan2025b] noted that the seminal reward...”
[Mahajan2025a] paper:paper-67f321895aff “Critically, the RPE framework's success has shaped research allocation in ways that constrain the broader field. Dopamine's computational role has been modeled with a precision that other modulators have not approached [Mahajan2025a, Udden2010]. [Udden2010] evaluated findings in the context of relevant computational models related to dopamine, serotonin, acetylcholine, and noradrenalin, highlighting the asymmetric d...”
[Udden2010] paper:paper-ffa878fbd516 “Critically, the RPE framework's success has shaped research allocation in ways that constrain the broader field. Dopamine's computational role has been modeled with a precision that other modulators have not approached [Mahajan2025a, Udden2010]. [Udden2010] evaluated findings in the context of relevant computational models related to dopamine, serotonin, acetylcholine, and noradrenalin, highlighting the asymmetric d...”
[Peters2021] paper:paper-3ff32b278cb1 “Critically, the RPE framework's success has shaped research allocation in ways that constrain the broader field. Dopamine's computational role has been modeled with a precision that other modulators have not approached [Mahajan2025a, Udden2010]. [Udden2010] evaluated findings in the context of relevant computational models related to dopamine, serotonin, acetylcholine, and noradrenalin, highlighting the asymmetric d...”
[Courtiol2021] paper:paper-0b909024fe21 “Critically, the RPE framework's success has shaped research allocation in ways that constrain the broader field. Dopamine's computational role has been modeled with a precision that other modulators have not approached [Mahajan2025a, Udden2010]. [Udden2010] evaluated findings in the context of relevant computational models related to dopamine, serotonin, acetylcholine, and noradrenalin, highlighting the asymmetric d...”
[Akhmirov2024] paper:paper-5ce142154d20 “Critically, the RPE framework's success has shaped research allocation in ways that constrain the broader field. Dopamine's computational role has been modeled with a precision that other modulators have not approached [Mahajan2025a, Udden2010]. [Udden2010] evaluated findings in the context of relevant computational models related to dopamine, serotonin, acetylcholine, and noradrenalin, highlighting the asymmetric d...”
[GonzalezRedondo2025] paper:paper-c2d0ce558597 “Three-factor learning rules — in which Hebbian co-activation is gated by a dopaminergic eligibility trace — represent the primary mechanism by which RPE signals are thought to drive synaptic plasticity [GonzalezRedondo2025, Chalmers2024]. [GonzalezRedondo2025] presented a computational model in which acetylcholine, released by striatal cholinergic interneurons, acts as a channel-specific gating signal that restricts...”
[Chalmers2024] paper:paper-28c831814947 “Three-factor learning rules — in which Hebbian co-activation is gated by a dopaminergic eligibility trace — represent the primary mechanism by which RPE signals are thought to drive synaptic plasticity [GonzalezRedondo2025, Chalmers2024]. [GonzalezRedondo2025] presented a computational model in which acetylcholine, released by striatal cholinergic interneurons, acts as a channel-specific gating signal that restricts...”
[Lile2026] paper:paper-d678ff9b10b7 “Beyond the RPE framework's core predictions, computational models have been applied to increasingly complex decision-making scenarios that test the framework's generality. [Lile2026] showed that decision making occurs in dynamic contexts in which individual and reward attributes change, requiring models that go beyond static RPE signals to capture the temporal evolution of value estimates. [DiasMaile2025] analyzed t...”
[DiasMaile2025] paper:paper-ffd90a5c0719 “Beyond the RPE framework's core predictions, computational models have been applied to increasingly complex decision-making scenarios that test the framework's generality. [Lile2026] showed that decision making occurs in dynamic contexts in which individual and reward attributes change, requiring models that go beyond static RPE signals to capture the temporal evolution of value estimates. [DiasMaile2025] analyzed t...”
[Benrimoh2025] paper:paper-81238db98786 “Beyond the RPE framework's core predictions, computational models have been applied to increasingly complex decision-making scenarios that test the framework's generality. [Lile2026] showed that decision making occurs in dynamic contexts in which individual and reward attributes change, requiring models that go beyond static RPE signals to capture the temporal evolution of value estimates. [DiasMaile2025] analyzed t...”
[Blain2020] paper:paper-0f7293e51a8a “Trial-by-trial dopamine signals track not only reward prediction errors but also social and non-reward variables, complicating the pure RPE account. [Batten2024] found that regardless of social context, relative changes in dopamine tracked trial-by-trial changes in reward expectations. [Blain2020] demonstrated that momentary happiness is associated with reward prediction error signals, extending the RPE framework be...”
[Batten2025] paper:paper-8ce6e601bbe2 “Trial-by-trial dopamine signals track not only reward prediction errors but also social and non-reward variables, complicating the pure RPE account. [Batten2024] found that regardless of social context, relative changes in dopamine tracked trial-by-trial changes in reward expectations. [Blain2020] demonstrated that momentary happiness is associated with reward prediction error signals, extending the RPE framework be...”
[Raduner2026] paper:paper-bce377142589 “Trial-by-trial dopamine signals track not only reward prediction errors but also social and non-reward variables, complicating the pure RPE account. [Batten2024] found that regardless of social context, relative changes in dopamine tracked trial-by-trial changes in reward expectations. [Blain2020] demonstrated that momentary happiness is associated with reward prediction error signals, extending the RPE framework be...”
[Kurtenbach2024] paper:paper-da9e247fb33f “An influential normative framework assigns specific computational roles to acetylcholine and norepinephrine within Bayesian inference: acetylcholine signals expected uncertainty (the known unreliability of the current environment), while norepinephrine signals unexpected uncertainty (the detection that the environment has fundamentally changed) [Kurtenbach2024, Howlett2025, OCallaghan2025]. This framework is mathema...”
[Howlett2025] paper:paper-05290535bd0c “An influential normative framework assigns specific computational roles to acetylcholine and norepinephrine within Bayesian inference: acetylcholine signals expected uncertainty (the known unreliability of the current environment), while norepinephrine signals unexpected uncertainty (the detection that the environment has fundamentally changed) [Kurtenbach2024, Howlett2025, OCallaghan2025]. This framework is mathema...”
[OCallaghan2025] paper:paper-5a368cbe614a “An influential normative framework assigns specific computational roles to acetylcholine and norepinephrine within Bayesian inference: acetylcholine signals expected uncertainty (the known unreliability of the current environment), while norepinephrine signals unexpected uncertainty (the detection that the environment has fundamentally changed) [Kurtenbach2024, Howlett2025, OCallaghan2025]. This framework is mathema...”
[Basu2024] paper:paper-02ec8298a8e4 “Recent work has begun to address this gap from the norepinephrine side. [Basu2024] provided quantitative evidence that frontal norepinephrine acts as a threat prediction error signal, fitting cue-evoked NE to a Rescorla-Wagner model with adjusted R² = 0.6869 for acquisition and adjusted R² = 0.6911 for extinction trials. Critically, NE poorly predicted behavioral freezing (adjusted R² = 0.0124), suggesting that NE e...”
[Pandian2025] paper:paper-e55eff024574 “Recent work has begun to address this gap from the norepinephrine side. [Basu2024] provided quantitative evidence that frontal norepinephrine acts as a threat prediction error signal, fitting cue-evoked NE to a Rescorla-Wagner model with adjusted R² = 0.6869 for acquisition and adjusted R² = 0.6911 for extinction trials. Critically, NE poorly predicted behavioral freezing (adjusted R² = 0.0124), suggesting that NE e...”
[Zhou2026a] paper:paper-8563c1876fe1 “Pharmacological studies in humans have provided additional evidence consistent with, but not uniquely predicted by, the Bayesian uncertainty framework. [Zhou2026a] used a double-blind, counterbalanced design with healthy adults to examine how pharmacological manipulation affects EEG signatures of prediction error processing, finding modulator-specific effects on neural responses to expectation violation. [Meijer2026...”
[Meijer2026] paper:paper-17cdac9d2b53 “Pharmacological studies in humans have provided additional evidence consistent with, but not uniquely predicted by, the Bayesian uncertainty framework. [Zhou2026a] used a double-blind, counterbalanced design with healthy adults to examine how pharmacological manipulation affects EEG signatures of prediction error processing, finding modulator-specific effects on neural responses to expectation violation. [Meijer2026...”
[Feng2024b] paper:paper-5967f4943309 “Pharmacological studies in humans have provided additional evidence consistent with, but not uniquely predicted by, the Bayesian uncertainty framework. [Zhou2026a] used a double-blind, counterbalanced design with healthy adults to examine how pharmacological manipulation affects EEG signatures of prediction error processing, finding modulator-specific effects on neural responses to expectation violation. [Meijer2026...”
[Limanowski2024] paper:paper-503339b6717e “The active inference and predictive processing frameworks offer an alternative formalization in which neuromodulators regulate the precision (inverse variance) of predictions and prediction errors at different levels of the cortical hierarchy [Limanowski2024, Krupnik2025, Taghva2026, Usler2025]. [Limanowski2024] described how active inference frames Bayes-optimal behaviour as motivated by minimisation of variational...”
[Krupnik2025] paper:paper-6d103bdc6c4e “The active inference and predictive processing frameworks offer an alternative formalization in which neuromodulators regulate the precision (inverse variance) of predictions and prediction errors at different levels of the cortical hierarchy [Limanowski2024, Krupnik2025, Taghva2026, Usler2025]. [Limanowski2024] described how active inference frames Bayes-optimal behaviour as motivated by minimisation of variational...”
[Taghva2026] paper:paper-ee76dc04e2d2 “The active inference and predictive processing frameworks offer an alternative formalization in which neuromodulators regulate the precision (inverse variance) of predictions and prediction errors at different levels of the cortical hierarchy [Limanowski2024, Krupnik2025, Taghva2026, Usler2025]. [Limanowski2024] described how active inference frames Bayes-optimal behaviour as motivated by minimisation of variational...”
[Usler2025] paper:paper-d644c0f6a512 “The active inference and predictive processing frameworks offer an alternative formalization in which neuromodulators regulate the precision (inverse variance) of predictions and prediction errors at different levels of the cortical hierarchy [Limanowski2024, Krupnik2025, Taghva2026, Usler2025]. [Limanowski2024] described how active inference frames Bayes-optimal behaviour as motivated by minimisation of variational...”
[Alotaibi2026] paper:paper-f82cd8eab690 “The active inference and predictive processing frameworks offer an alternative formalization in which neuromodulators regulate the precision (inverse variance) of predictions and prediction errors at different levels of the cortical hierarchy [Limanowski2024, Krupnik2025, Taghva2026, Usler2025]. [Limanowski2024] described how active inference frames Bayes-optimal behaviour as motivated by minimisation of variational...”
[Beerendonk2025] paper:paper-efe50912e36d “Multiple competing frameworks assign computational roles to norepinephrine: adaptive gain theory positions NE as optimizing the explore-exploit trade-off via gain modulation; the Bayesian uncertainty account treats NE as signaling unexpected uncertainty; and arousal optimization models describe NE as setting global arousal state along a Yerkes-Dodson curve [Beerendonk2025, Goodridge2025, Nassar2024]. These framework...”
[Goodridge2025] paper:paper-1645985cece8 “Multiple competing frameworks assign computational roles to norepinephrine: adaptive gain theory positions NE as optimizing the explore-exploit trade-off via gain modulation; the Bayesian uncertainty account treats NE as signaling unexpected uncertainty; and arousal optimization models describe NE as setting global arousal state along a Yerkes-Dodson curve [Beerendonk2025, Goodridge2025, Nassar2024]. These framework...”
[Nassar2024] paper:paper-0dd8418bc616 “Multiple competing frameworks assign computational roles to norepinephrine: adaptive gain theory positions NE as optimizing the explore-exploit trade-off via gain modulation; the Bayesian uncertainty account treats NE as signaling unexpected uncertainty; and arousal optimization models describe NE as setting global arousal state along a Yerkes-Dodson curve [Beerendonk2025, Goodridge2025, Nassar2024]. These framework...”

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