The primate amygdala combines information about space and value

Peck, Christopher J.; Lau, Brian; Salzman, C. Daniel

doi:10.1038/nn.3328

Cited by 113 publications

(111 citation statements)

References 49 publications

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“…Although there has been a recent report of an interaction between spatial laterality and reward coding in the primate amygdala probed with lateralized reward cues (38), that effect appeared primarily as a difference in latency but not as the lateralization of reward-coding neurons to the reward-predicting cues. It will be interesting to investigate in future studies whether these findings with basic rewards (38) can be generalized to emotions or other salient stimuli.…”

Section: Discussionmentioning

confidence: 87%

Neurons in the human amygdala selective for perceived emotion

Wang

Tudusciuc

Mamelak³

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

124

114

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The human amygdala plays a key role in recognizing facial emotions and neurons in the monkey and human amygdala respond to the emotional expression of faces. However, it remains unknown whether these responses are driven primarily by properties of the stimulus or by the perceptual judgments of the perceiver. We investigated these questions by recording from over 200 single neurons in the amygdalae of 7 neurosurgical patients with implanted depth electrodes. We presented degraded fear and happy faces and asked subjects to discriminate their emotion by button press. During trials where subjects responded correctly, we found neurons that distinguished fear vs. happy emotions as expressed by the displayed faces. During incorrect trials, these neurons indicated the patients' subjective judgment. Additional analysis revealed that, on average, all neuronal responses were modulated most by increases or decreases in response to happy faces, and driven predominantly by judgments about the eye region of the face stimuli. Following the same analyses, we showed that hippocampal neurons, unlike amygdala neurons, only encoded emotions but not subjective judgment. Our results suggest that the amygdala specifically encodes the subjective judgment of emotional faces, but that it plays less of a role in simply encoding aspects of the image array. The conscious percept of the emotion shown in a face may thus arise from interactions between the amygdala and its connections within a distributed cortical network, a scheme also consistent with the long response latencies observed in human amygdala recordings.human single unit | medial temporal lobe | limbic system | hippocampus | intracranial

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Section: Discussionmentioning

confidence: 87%

Neurons in the human amygdala selective for perceived emotion

Wang

Tudusciuc

Mamelak³

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

124

114

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“…Similar multicomponent responses are seen with reward coding. Amygdala neurons initially detect a visual stimulus and may code its identity and then transition within 60 -300 ms to differential reward value coding (9,422,428). V1 and inferotemporal cortex responses show initial visual stimulus selectivity and only 50 -90 ms later distinguish reward values (371,557).…”

Section: Dopamine Event Detection Responsementioning

confidence: 99%

“…Reward affects neuronal activity differentiating between different movement parameters during the instruction, preparation and execution of action in prefrontal and premotor cortex (FIGURE 20, D AND E) (135,282,313,348,476,606,627), anterior and posterior cingulate cortex (354,542), parietal cortex (381,427,433), striatum (FIGURE 20F) (107,205,222,260,308), globus pallidus (178), substantia nigra pars reticulata (502), superior colliculus (243), and amygdala (428). By processing information about the forthcoming reward during the preparation or execution of action, these activities may reflect a representation of the reward before and during the movement toward the reward, which fulfills a crucial requirement for goal-directed behavior (133).…”

Section: Events Eliciting Reward Responsesmentioning

confidence: 99%

Neuronal Reward and Decision Signals: From Theories to Data

Schultz

2015

Physiological Reviews

894

765

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LSchultz W. Neuronal Reward and Decision Signals: From Theories to Data. Physiol Rev 95: 853-951, 2015. Published June 24, 2015 doi:10.1152/physrev.00023.2014.-Rewards are crucial objects that induce learning, approach behavior, choices, and emotions. Whereas emotions are difficult to investigate in animals, the learning function is mediated by neuronal reward prediction error signals which implement basic constructs of reinforcement learning theory. These signals are found in dopamine neurons, which emit a global reward signal to striatum and frontal cortex, and in specific neurons in striatum, amygdala, and frontal cortex projecting to select neuronal populations. The approach and choice functions involve subjective value, which is objectively assessed by behavioral choices eliciting internal, subjective reward preferences. Utility is the formal mathematical characterization of subjective value and a prime decision variable in economic choice theory. It is coded as utility prediction error by phasic dopamine responses. Utility can incorporate various influences, including risk, delay, effort, and social interaction. Appropriate for formal decision mechanisms, rewards are coded as object value, action value, difference value, and chosen value by specific neurons. Although all reward, reinforcement, and decision variables are theoretical constructs, their neuronal signals constitute measurable physical implementations and as such confirm the validity of these concepts. The neuronal reward signals provide guidance for behavior while constraining the free will to act.

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“…Midbrain dopamine neurons encode a reward prediction error (5-7) that is sufficient to cause learning (8,9). These neurons receive inputs from several brain areas that encode subjective value and project axons to every brain structure implicated in economic choice (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21). Therefore, dopamine neurons are ideally positioned to broadcast a teaching signal that directly updates economic values.…”

mentioning

confidence: 99%

Dopamine prediction error responses integrate subjective value from different reward dimensions

Lak

Stauffer

Schultz

2014

Proc. Natl. Acad. Sci. U.S.A.

218

246

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Prediction error signals enable us to learn through experience. These experiences include economic choices between different rewards that vary along multiple dimensions. Therefore, an ideal way to reinforce economic choice is to encode a prediction error that reflects the subjective value integrated across these reward dimensions. Previous studies demonstrated that dopamine prediction error responses reflect the value of singular reward attributes that include magnitude, probability, and delay. Obviously, preferences between rewards that vary along one dimension are completely determined by the manipulated variable. However, it is unknown whether dopamine prediction error responses reflect the subjective value integrated from different reward dimensions. Here, we measured the preferences between rewards that varied along multiple dimensions, and as such could not be ranked according to objective metrics. Monkeys chose between rewards that differed in amount, risk, and type. Because their choices were complete and transitive, the monkeys chose "as if" they integrated different rewards and attributes into a common scale of value. The prediction error responses of single dopamine neurons reflected the integrated subjective value inferred from the choices, rather than the singular reward attributes. Specifically, amount, risk, and reward type modulated dopamine responses exactly to the extent that they influenced economic choices, even when rewards were vastly different, such as liquid and food. This prediction error response could provide a direct updating signal for economic values.electrophysiology | primate | behavioral economics | reinforcement learning P rediction errors represent the difference between predicted and realized outcomes. As such they are an ideal way to learn through everyday experiences (1). These experiences include making value-based (economic) choices between different rewards and evaluating the outcome of such decisions. Some of the most common economic decisions we face are between rewards that lack a common quality for comparison. To facilitate consistent choices between them, such rewards should be evaluated on a common scale of value (2-4). Thus, an ideal way to facilitate and reinforce economic decisions is to encode the prediction error directly in terms of subjective value. Midbrain dopamine neurons encode a reward prediction error (5-7) that is sufficient to cause learning (8, 9). These neurons receive inputs from several brain areas that encode subjective value and project axons to every brain structure implicated in economic choice (10-21). Therefore, dopamine neurons are ideally positioned to broadcast a teaching signal that directly updates economic values.Economic preferences between alternatives that vary in one attribute are easily determined by the magnitude of the attribute (22). For instance, larger rewards are preferred over smaller ones, high reward probability is preferred over low reward probability, and reward delivered after a short delay is preferred over the same r...

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The primate amygdala combines information about space and value

Cited by 113 publications

References 49 publications

Neurons in the human amygdala selective for perceived emotion

Neurons in the human amygdala selective for perceived emotion

Neuronal Reward and Decision Signals: From Theories to Data

Dopamine prediction error responses integrate subjective value from different reward dimensions

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