Rules for aversive learning and decision-making

Yau, Joanna Oi-Yue; McNally, Gavan P.

doi:10.1016/j.cobeha.2018.08.006

Cited by 8 publications

(3 citation statements)

References 53 publications

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“…This appeared to be driven by a greater expectancy-related modulation of the P3 to shock feedback (see Table 1; Figure 4), which fits with prior work suggesting that the P3 is sensitive to the motivational salience of stimuli (Hajcak & Foti, 2020;Nieuwenhuis, 2011). Moreover, these results fit with prediction error theories, which have suggested that unexpected negative outcomes might be especially important to learning (Rescorla & Wagner, 1972;Yau & McNally, 2019). More broadly, it is likely that the P3 will affect the amplitude of the RewP when the RewP is not separated out from other components (see Figure 2).…”

Section: Discussionsupporting

confidence: 84%

Reinforcement learning and the reward positivity with aversive outcomes

Bauer,

Watanabe,

MacNamara

2023

Psychophysiology

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The reinforcement learning (RL) theory of the reward positivity (RewP), an event‐related potential (ERP) component that measures reward responsivity, suggests that the RewP should be largest when positive outcomes are unexpected and has been supported by work using appetitive outcomes (e.g., money). However, the RewP can also be elicited by the absence of aversive outcomes (e.g., shock). The limited work to‐date that has manipulated expectancy while using aversive outcomes has not supported the predictions of RL theory. Nonetheless, this work has been difficult to reconcile with the appetitive literature because the RewP was not observed as a reward signal in these studies, which used passive tasks that did not involve participant choice. Here, we tested the predictions of the RL theory by manipulating expectancy in an active/choice‐based threat‐of‐shock doors task that was previously found to elicit the RewP as a reward signal. Moreover, we used principal components analysis to isolate the RewP from overlapping ERP components. Eighty participants viewed pairs of doors surrounded by a red or green border; shock delivery was expected (80%) following red‐bordered doors and unexpected (20%) following green‐bordered doors. The RewP was observed as a reward signal (i.e., no shock > shock) that was not potentiated for unexpected feedback. In addition, the RewP was larger overall for unexpected (vs expected) feedback. Therefore, the RewP appears to reflect the additive (not interactive) effects of reward and expectancy, challenging the RL theory of the RewP, at least when reward is defined as the absence of an aversive outcome.

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Section: Discussionsupporting

confidence: 84%

Reinforcement learning and the reward positivity with aversive outcomes

Bauer,

Watanabe,

MacNamara

2023

Psychophysiology

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“…Although both extinction and safety involve learning about the absence of an expected aversive event and both are initiated by a negative fear prediction error at the moments of nonreinforcement (McNally et al, 2011; Yau & McNally, 2018, 2019), the contents of the learning established and the associative mechanisms through which they inhibit fear are quite different (Rescorla, 1969). Extinction is specific to the CS–US association, likely due to the formation of an inhibitory association between a fear CS and the US that competes and conflicts with the original CS–US association.…”

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confidence: 99%

The activity of ventral tegmental area dopamine neurons during shock omission predicts safety learning.

Yau¹,

McNally²

2022

Behavioral Neuroscience

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We studied the role of dopamine [tyrosine hydroxylase (TH)] neurons in the rat ventral tegmental area (VTA) in safety learning. First, we used an AX+/BX-discrimination procedure to establish conditioned stimulus (CS) B as a learned safety signal that passed both summation and retardation tests of conditioned inhibition. Then, we combined this procedure with fiber photometry in TH-Cre rats to study the activity of VTA dopamine neurons during safety learning. We show that whereas footshock is associated with calcium transients in TH neurons across the VTA, shock omission during safety learning is selectively associated with calcium transients in dopamine neurons in the medial but not lateral VTA. Moreover, the magnitude of medial VTA calcium transients during shock omission accurately predicts the amount of safety that is learned and expressed during summation testing. Our findings are consistent with a common medial VTA dopamine mechanism contributing to the learned inhibition of fear in extinction and safety.

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“…Animals, including humans, use prediction error to guide learning about danger in the world (Fanselow, 1998;Fanselow and Poulos, 2005;Li and McNally, 2013;Yau and McNally, 2018a;Yau and McNally, 2018b). Prediction error determines how effective aversive events are in supporting fear learning.…”

Section: Significance Statementmentioning

confidence: 99%

Basolateral amygdala parvalbumin neurons report aversive prediction error to constrain fear learning

Yau¹,

Chaichim²,

Power³

et al. 2020

Preprint

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Animals, including humans, use prediction error to guide learning about danger in the environment. The basolateral amygdala (BLA) is obligatory for this learning and BLA excitatory projection neurons are instructed by aversive prediction error to form fear associations. Complex networks of inhibitory interneurons, dominated by parvalbumin (PV) expressing GABAergic neurons, form the intrinsic microcircuitry of the BLA to control projection neuron activity. Whether BLA PV interneurons are also sensitive to prediction error and how they use this error to control fear learning remains unknown. We used PV cell-type specific recording and manipulation approaches in male transgenic PV-Cre rats to address these issues. We show that BLA PV neurons control fear learning about aversive events but not learning about their omission. Furthermore, during fear learning BLA PV neurons express the activity signatures of aversive prediction error: greater activity to unexpected than expected aversive events and greater activity to better rather than poorer predictors of these events. Crucially, we show that BLA PV neurons act to limit fear learning across these variations in prediction error. Together, this demonstrates that prediction error instructs and regulates BLA fear association formation in a cell-type specific manner. Whereas BLA projection neurons use prediction error signals to form and store fear associations, BLA PV interneurons use prediction error signals to constrain fear association formation.

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Rules for aversive learning and decision-making

Cited by 8 publications

References 53 publications

Reinforcement learning and the reward positivity with aversive outcomes

Reinforcement learning and the reward positivity with aversive outcomes

The activity of ventral tegmental area dopamine neurons during shock omission predicts safety learning.

Basolateral amygdala parvalbumin neurons report aversive prediction error to constrain fear learning

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