The amygdala plays an important role in many aspects of social-cognition and reward-learning. Here we aimed to determine whether human amygdala neurons are involved in the computations necessary to implement learning through observation. We performed single-neuron recordings from the amygdalae of human neurosurgical patients (male and female) while they learned about the value of stimuli through observing the outcomes experienced by another agent interacting with those stimuli. We used a detailed computational modeling approach to describe patients' behavior in the task. Then, using both population-level decoding and single neuron analyses we found evidence to implicate amygdala neurons in two key computations relevant for observational-learning: tracking the expected future reward associated with a given stimulus, and in tracking outcome values received by oneself or other agents. Encoding and decoding analyses suggested observational value coding in amygdala neurons occurred in a different subset of neurons than experiential value coding. Collectively, these findings support a key role for the human amygdala in the computations underlying the capacity for learning through observation. Significance statement Single neuron studies of the human brain provide a unique window into the computational mechanisms of cognition. In this study, epilepsy patients implanted intracranially with depth microelectrodes performed an observational learning task. We measured activity bilaterally in the amygdala and found a representation for observational rewards as well as observational expected reward values. Additionally, the representation of self-experienced and observational values was performed by distinct subsets of amygdala neurons. This study thus provides a rare glimpse into the role of human amygdala neurons in social cognition.

Adaptive behavior in real-world environments demands that choices integrate over several variables, including the novelty of the options under consideration, their expected value, and uncertainty in value estimation. We recorded neurons from the human pre-supplementary motor area (preSMA), ventromedial prefrontal cortex (vmPFC) and dorsal anterior cingulate to probe how integration over decision variables occurs during decision-making. In contrast to the other areas, preSMA neurons not only represented separate pre-decision variables for each choice option, but also encoded an integrated utility signal and, subsequently, the decision itself. Conversely, post-decision related encoding of variables for the chosen option was more widely distributed and especially prominent in vmPFC. Our findings position the human preSMA as central to the implementation of value-based decisions.

Adaptive behavior in real-world environments demands that choices integrate over several variables, including the novelty of the options under consideration, their expected value, and uncertainty in value estimation. We recorded neurons from the human pre-supplementary motor area (preSMA), ventromedial prefrontal cortex (vmPFC) and dorsal anterior cingulate to probe how integration over decision variables occurs during decision-making. In contrast to the other areas, preSMA neurons not only represented separate pre-decision variables for each choice option, but also encoded an integrated utility signal and, subsequently, the decision itself. Conversely, post-decision related encoding of variables for the chosen option was more widely distributed and especially prominent in vmPFC. Our findings position the human preSMA as central to the implementation of value-based decisions.

No abstract

AbstractThe amygdala plays an important role in many aspects of social-cognition and reward-learning. Here we aimed to determine whether human amygdala neurons are involved in the computations necessary to implement learning through observation. We performed single-neuron recordings from the amygdalae of human neurosurgical patients (male and female) while they learned about the value of stimuli through observing the outcomes experienced by another agent interacting with those stimuli. We used a detailed computational modeling approach to describe patients’ behavior in the task. Then, using both population-level decoding and single neuron analyses we found evidence to implicate amygdala neurons in two key computations relevant for observational-learning: tracking the expected future reward associated with a given stimulus, and in tracking outcome values received by oneself or other agents. Encoding and decoding analyses suggested observational value coding in amygdala neurons occurred in a different subset of neurons than experiential value coding. Collectively, these findings support a key role for the human amygdala in the computations underlying the capacity for learning through observation.Significance statementSingle neuron studies of the human brain provide a unique window into the computational mechanisms of cognition. In this study, epilepsy patients implanted intracranially with depth microelectrodes performed an observational learning task. We measured activity bilaterally in the amygdala and found a representation for observational rewards as well as observational expected reward values. Additionally, the representation of self-experienced and observational values was performed by distinct subsets of amygdala neurons. This study thus provides a rare glimpse into the role of human amygdala neurons in social cognition.

Pavlovian conditioning is thought to involve the formation of learned associations between stimuli and values, and between stimuli and specific features of outcomes. Here we leveraged human single neuron recordings in ventromedial prefrontal, dorsomedial frontal, hippocampus and amygdala neurons while patients performed a sequential Pavlovian conditioning task containing both stimulus-value and stimulus-stimulus associations. Neurons in the ventromedial prefrontal cortex encoded predictive value along with the amygdala, but also encoded predictions about the identity of stimuli that would subsequently be presented, suggesting a role for neurons in this region in encoding predictive information beyond value. Unsigned error signals were found in dorsomedial prefrontal areas and hippocampus, potentially supporting learning of non-value related outcome features. Our findings implicate distinct human prefrontal and medial temporal neuronal populations in mediating predictive associations which could partially support model-based mechanisms during Pavlovian conditioning.