Effects of reward size and context on learning in macaque monkeys

Ferrucci, Lorenzo; Nougaret, Simon; Brunamonti, Emiliano; Genovesio, Aldo

doi:10.1016/j.bbr.2019.111983

Cited by 13 publications

(18 citation statements)

References 22 publications

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“…Possibly, the gradual removal of an aversive cue (i.e. volume reduction of trauma-script) led to greater reward value and thus more rapid learning ( Ferrucci et al, 2019 ). Another possibility is that the decrease (or increase) in the trauma-script volume affects the AmygEFP signal on its own, regardless of parallel reinforcement and regulation processes.…”

Section: Discussionmentioning

confidence: 99%

Amygdala electrical-finger-print (AmygEFP) NeuroFeedback guided by individually-tailored Trauma script for post-traumatic stress disorder: Proof-of-concept

Fruchtman-Steinbok

Keynan

Cohen

et al. 2021

NeuroImage: Clinical

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Highlights Randomized clinical trial with a novel self-neuromodulation training in PTSD. Demonstration of feasibility of an fMRI-informed EEG model of Amygdala modulation (AmygEFP). Individually-tailored trauma-related content as the training feedback interface. Results showed reduction of PTSD symptoms following AmygEFP trauma-related feedback training.

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Section: Discussionmentioning

confidence: 99%

Amygdala electrical-finger-print (AmygEFP) NeuroFeedback guided by individually-tailored Trauma script for post-traumatic stress disorder: Proof-of-concept

Fruchtman-Steinbok

Keynan

Cohen

et al. 2021

NeuroImage: Clinical

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“…According to the RW model, we expect to see a steeper learning curve in the high reward condition, as in the experiment of (28). However, SPE theory predicts that this difference between conditions should vanish if an appropriate pretraining is applied.…”

Section: Experimental Predictionsmentioning

confidence: 89%

Uncertainty-guided learning with scaled prediction errors in the basal ganglia

Moeller

Manohar

Bogacz

2022

Preprint

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To accurately predict rewards associated with states or actions, the variability of observations has to be taken into account. In particular, when the observations are noisy, the individual rewards should have less influence on tracking of average reward, and the estimate of the mean reward should be updated to a smaller extent after each observation. However, it is not known how the magnitude of the observation noise might be tracked and used to control prediction updates in the brain reward system. Here, we introduce a new model that uses simple, tractable learning rules that track the mean and standard deviation of reward, and leverages prediction errors scaled by uncertainty as the central feedback signal. We provide a normative analysis, comparing the performance of the new model with that of conventional models in a value tracking task. We find that the new model has an advantage over conventional models when tested across various levels of observation noise. Further, we propose a possible biological implementation of the model in the basal ganglia circuit. The scaled prediction error feedback signal is consistent with experimental findings concerning dopamine prediction error scaling relative to reward magnitude, and the update rules are found to be consistent with many features of striatal plasticity. Our results span across the levels of implementation, algorithm, and computation, and might have important implications for understanding the dopaminergic prediction error signal and its relation to adaptive and effective learning.

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“…Different cognitive functions investigated in neurophysiological experiments require pre-training to execute more than just single trials in isolation, although this is rarely mentioned. For example, different behavioral tasks used to study the generation of goal-directed rules and responses 60,61 , logical reasoning 62 , mapping of stimulus-response associations 63 , and one-trial learning 44,64 require monkeys to constantly take into account various task variables learned in previous trials and carry out subsequent trials during the session, so that the neural correlates involved can be studied. In addition, a common approach in electrophysiology consists of requiring the monkey to repeat the same behavior several times and recording the neural activity during this behavior.…”

Section: Discussionmentioning

confidence: 99%

Macaque monkeys learn and perform a non-match-to-goal task using an automated home cage training procedure

Sacchetti

Ceccarelli

Ferrucci

et al. 2021

Sci Rep

Self Cite

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In neurophysiology, nonhuman primates represent an important model for studying the brain. Typically, monkeys are moved from their home cage to an experimental room daily, where they sit in a primate chair and interact with electronic devices. Refining this procedure would make the researchers’ work easier and improve the animals’ welfare. To address this issue, we used home-cage training to train two macaque monkeys in a non-match-to-goal task, where each trial required a switch from the choice made in the previous trial to obtain a reward. The monkeys were tested in two versions of the task, one in which they acted as the agent in every trial and one in which some trials were completed by a “ghost agent”. We evaluated their involvement in terms of their performance and their interaction with the apparatus. Both monkeys were able to maintain a constant involvement in the task with good, stable performance within sessions in both versions of the task. Our study confirms the feasibility of home-cage training and demonstrates that even with challenging tasks, monkeys can complete a large number of trials at a high performance level, which is a prerequisite for electrophysiological studies of monkey behavior.

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Effects of reward size and context on learning in macaque monkeys

Cited by 13 publications

References 22 publications

Amygdala electrical-finger-print (AmygEFP) NeuroFeedback guided by individually-tailored Trauma script for post-traumatic stress disorder: Proof-of-concept

Amygdala electrical-finger-print (AmygEFP) NeuroFeedback guided by individually-tailored Trauma script for post-traumatic stress disorder: Proof-of-concept

Uncertainty-guided learning with scaled prediction errors in the basal ganglia

Macaque monkeys learn and perform a non-match-to-goal task using an automated home cage training procedure

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