Neural Coding of Reward-Prediction Error Signals During Classical Conditioning With Attractive Faces

Bray, Signe; O’Doherty, John P.

doi:10.1152/jn.01211.2006

Cited by 154 publications

(124 citation statements)

References 56 publications

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“…We suggest that this maximum familiarity is developed ontogenetically and reflects a tuning property of neurons involved in the perceptual learning of facial identities. As such, we suggest that this asymptotic value is akin to the values calculated in the Rescorla-Wagner model in reinforcement learning 13,[54][55][56][57] and the Pearce-Hall 12 algorithm in attention-based error learning, which have been shown to be effective models of other types of learning.…”

Section: Methodssupporting

confidence: 52%

Predictive codes of familiarity and context during the perceptual learning of facial identities

Apps

Tsakiris

2013

Nat Commun

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Face recognition is a key component of successful social behaviour. However, the computational processes that underpin perceptual learning and recognition as faces transition from unfamiliar to familiar are poorly understood. In predictive coding, learning occurs through prediction errors that update stimulus familiarity, but recognition is a function of both stimulus and contextual familiarity. Here we show that behavioural responses on a two-option face recognition task can be predicted by the level of contextual and facial familiarity in a computational model derived from predictive-coding principles. Using fMRI, we show that activity in the superior temporal sulcus varies with the contextual familiarity in the model, whereas activity in the fusiform face area covaries with the prediction error parameter that updated facial familiarity. Our results characterize the key computations underpinning the perceptual learning of faces, highlighting that the functional properties of face-processing areas conform to the principles of predictive coding.

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Section: Methodssupporting

confidence: 52%

Predictive codes of familiarity and context during the perceptual learning of facial identities

Apps

Tsakiris

2013

Nat Commun

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“…Interestingly, it appears that the ventral striatum, an area that has been frequently associated with the processing of prediction errors in individual learning (23)(24)(25)(26), was involved in processing prediction errors related to actually experienced, individual reward in our task in a conventional sense (i.e., with increasing activity to positive individual reward prediction errors) but showed the inverse coding pattern for observational prediction errors. Although our task was not presented to participants as a game situation (and the behavior of the confederate in no way affected the possibility of participants receiving reward), this inverse reward prediction error coding for the confederate's outcomes is supported by previous research highlighting the role the ventral striatum plays in competitive social situations.…”

Section: Discussionmentioning

confidence: 92%

Neural mechanisms of observational learning

Burke

Tobler

Baddeley

et al. 2010

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

307

353

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Individuals can learn by interacting with the environment and experiencing a difference between predicted and obtained outcomes (prediction error). However, many species also learn by observing the actions and outcomes of others. In contrast to individual learning, observational learning cannot be based on directly experienced outcome prediction errors. Accordingly, the behavioral and neural mechanisms of learning through observation remain elusive. Here we propose that human observational learning can be explained by two previously uncharacterized forms of prediction error, observational action prediction errors (the actual minus the predicted choice of others) and observational outcome prediction errors (the actual minus predicted outcome received by others). In a functional MRI experiment, we found that brain activity in the dorsolateral prefrontal cortex and the ventromedial prefrontal cortex respectively corresponded to these two distinct observational learning signals.prediction error | reward | vicarious learning | dorsolateral prefrontal cortex | ventromedial prefrontal cortex I n uncertain and changing environments, flexible control of actions has individual and evolutionary advantages by allowing goaldirected and adaptive behavior. Flexible action control requires an understanding of how actions bring about rewarding or punishing outcomes. Through instrumental conditioning, individuals can use previous outcomes to modify future actions (1-4). However, individuals learn not only from their own actions and outcomes but also from those that are observed. One of the most illustrative examples of observational learning happens in Antarctica, where flocks of Adelie penguins often congregate at the water's edge to enter the sea and feed on krill. However, the main predator of the penguins, the leopard seal, is often lurking out of sight beneath the waves, making it a risky prospect to be the first one to take the plunge. As this waiting game develops, one of the animals often becomes so hungry that it jumps, and if no seal appears the rest of the group will all follow suit. The following penguins make a decision after observing the action and outcome of the first (5). This ability to learn from observed actions and outcomes is a pervasive feature of many species and can be absolutely crucial when the stakes are high. For example, predator avoidance techniques or the eating of a novel food item are better learned from another's experience rather than putting oneself at risk with trial-and-error learning. Although we know a fair amount about the neural mechanisms of individuals learning about their own actions and outcomes (6), almost nothing is known about the brain processes involved when individuals learn from observed actions and outcomes (7). This lack of knowledge is all the more surprising given that observational learning is such a wide-ranging phenomenon.In this study, 21 participants engaged in a novel observational learning task based on a simple two-armed bandit problem (Fig. 1A) while being scanne...

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“…This opens the possibility of separately examining neural activation during reward anticipation and in response to reward outcomes. For example, rewarding outcomes signaled by sensory cues, including cued pleasant odors (Gottfried et al, 2003) and attractive faces (Bray and O'Doherty, 2007), activate vmPFC. However, a number of studies using abstract rewards (ie, money), rather than sensory ones to measure gain outcomes, found that activation in the mPFC can be distinguished from the larger vmPFC area Knutson et al, 2003Knutson et al, , 2005Kuhnen and Knutson, 2005;Yacubian et al, 2006).…”

Section: Reward Processing In the Human Prefrontal Cortexmentioning

confidence: 99%

The Reward Circuit: Linking Primate Anatomy and Human Imaging

Haber

Knutson

2009

Neuropsychopharmacol

3,110

193

2,859

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Although cells in many brain regions respond to reward, the cortical-basal ganglia circuit is at the heart of the reward system. The key structures in this network are the anterior cingulate cortex, the orbital prefrontal cortex, the ventral striatum, the ventral pallidum, and the midbrain dopamine neurons. In addition, other structures, including the dorsal prefrontal cortex, amygdala, hippocampus, thalamus, and lateral habenular nucleus, and specific brainstem structures such as the pedunculopontine nucleus, and the raphe nucleus, are key components in regulating the reward circuit. Connectivity between these areas forms a complex neural network that mediates different aspects of reward processing. Advances in neuroimaging techniques allow better spatial and temporal resolution. These studies now demonstrate that human functional and structural imaging results map increasingly close to primate anatomy.

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Neural Coding of Reward-Prediction Error Signals During Classical Conditioning With Attractive Faces

Abstract: Bray S, O'Doherty J. Neural coding of reward-prediction error signals during classical conditioning with attractive faces.

Cited by 154 publications

References 56 publications

Predictive codes of familiarity and context during the perceptual learning of facial identities

Predictive codes of familiarity and context during the perceptual learning of facial identities

Neural mechanisms of observational learning

The Reward Circuit: Linking Primate Anatomy and Human Imaging

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