Human Dorsal Striatum Encodes Prediction Errors during Observational Learning of Instrumental Actions

The dorsal striatum plays a key role in the learning and expression of instrumental reward associations that are acquired through direct experience. However, not all learning about instrumental actions require direct experience. Instead, humans and other animals are also capable of acquiring instrumental actions by observing the experiences of others. In this study, we investigated the extent to which human dorsal striatum is involved in observational as well as experiential instrumental reward learning. Human participants were scanned with fMRI while they observed a confederate over a live video performing an instrumental conditioning task to obtain liquid juice rewards. Participants also performed a similar instrumental task for their own rewards. Using a computational model-based analysis, we found reward prediction errors in the dorsal striatum not only during the experiential learning condition but also during observational learning. These results suggest a key role for the dorsal striatum in learning instrumental associations, even when those associations are acquired purely by observing others

Insights from the application of computational neuroimaging to social neuroscience

A recent approach in social neuroscience has been the application of formal computational models for a particular social-cognitive process to neuroimaging data. Here we review preliminary findings from this nascent subfield, focusing on observational learning and strategic interactions. We present evidence consistent with the existence of three distinct learning systems that may contribute to social cognition: an observational-reward-learning system involved in updating expectations of future reward based on observing rewards obtained by others, an action-observational learning system involved in learning about the action tendencies of others, and a third system engaged when it is necessary to learn about the hidden mental-states or traits of another. These three systems appear to map onto distinct neuroanatomical substrates, and depend on unique computational signals

Contributions of the striatum to learning, motivation, and performance: an associative account

It has long been recognized that the striatum is composed of distinct functional sub-units that are part of multiple cortico-striatal-thalamic circuits. Contemporary research has focused on the contribution of striatal sub-regions to three main phenomena: learning of associations between stimuli, actions and rewards; selection between competing response alternatives; and motivational modulation of motor behavior. Recent proposals have argued for a functional division of the striatum along these lines, attributing, for example, learning to one region and performance to another. Here, we consider empirical data from human and animal studies, as well as theoretical notions from both the psychological and computational literatures, and conclude that striatal sub-regions instead differ most clearly in terms of the associations being encoded in each region

The Application of Computational Models to Social Neuroscience: Promises and Pitfalls

Interactions with conspecifics are key to any social species. In order to navigate this social world, it is crucial for individuals to learn from and about others. Whether it is learning a new skill by observing a parent perform it, avoiding negative outcomes, or making complex collective decisions, understanding the mechanisms underlying such social cognitive processes has been of considerable interest to psychologists and neuroscientists, particularly to studies of learning and decision-making. Here, we review studies that have used computational modelling techniques, combined with neuroimaging, to shed light on how people learn and make decisions in social contexts. As opposed to previous methods used in social neuroscience studies, the computational approach allows one to directly examine where in the brain particular computations, as estimated by models of behavior, are implemented. Similar to studies of experiential learning, findings suggest that learning from others can be implemented using several strategies: vicarious reward learning, where one learns from observing the reward outcomes of another agent; action imitation, which relies on encoding a prediction error between the expected and actual actions of the other agent; and social inference, where one learns by inferring the goals and intentions of others. These strategies rely on distinct neural networks, which may be recruited adaptively depending on task demands, the environment and other social factors

Autismo infantil: impacto do diagnóstico e repercussões nas relações familiares

Objetivo: Analisar o contexto da revelação do diagnóstico do autismo e o impacto deste nas relações familiares.Métodos: Trata-se de um estudo qualitativo, realizado com 10 familiares de crianças autistas, assistidas no Centro de Atenção Psicossocial Infanto Juvenil em um município da Paraíba. A coleta ocorreu entre julho e agosto de 2013 por meio de entrevista semiestruturada cujos dados foram interpretados pela análise de conteúdo na modalidade temática.Resultados: Identificou-se uma Unidade Temática Central com respectivas categorias: o impacto da revelação do diagnóstico de autismo para a família; características da revelação do diagnóstico: o local, o tempo e a relação dialógica entre o profissional e a família; alteração nas relações familiares e a sobrecarga materna no cuidado à criança autista.Conclusões: Há necessidade do profissional de saúde que noticiará o autismo saber preparar melhor a família para enfrentar as dificuldades impostas pela síndrome e para conquistar a autonomia no cuidado ao autista.Palavras-chave: Transtorno autístico. Diagnóstico. Relações familiares

Value-related neuronal responses in the human amygdala during observational learning

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. We found a significant proportion of amygdala neurons whose activity correlated with both expected rewards for oneself and others, and in tracking outcome values received by oneself or other agents. Additionally, a population decoding analysis suggests the presence of information for both observed and experiential outcomes in the amygdala. 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

The involvement of model-based but not model-free learning signals during observational reward learning in the absence of choice

A major open question is whether computational strategies thought to be used during experiential learning, specifically model-based and model-free reinforcement learning, also support observational learning. Furthermore, the question of how observational learning occurs when observers must learn about the value of options from observing outcomes in the absence of choice has not been addressed. In the present study we used a multi-armed bandit task that encouraged human participants to employ both experiential and observational learning while they underwent functional magnetic resonance imaging (fMRI). We found evidence for the presence of model-based learning signals during both observational and experiential learning in the intraparietal sulcus. However, unlike during experiential learning, model-free learning signals in the ventral striatum were not detectable during this form of observational learning. These results provide insight into the flexibility of the model-based learning system, implicating this system in learning during observation as well as from direct experience, and further suggest that the model-free reinforcement learning system may be less flexible with regard to its involvement in observational learning

Observational learning computations in neurons of the human anterior cingulate cortex

When learning from direct experience, neurons in the primate brain have been shown to encode a teaching signal used by algorithms in artificial intelligence: the reward prediction error (PE)—the difference between how rewarding an event is, and how rewarding it was expected to be. However, in humans and other species learning often takes place by observing other individuals. Here, we show that, when humans observe other players in a card game, neurons in their rostral anterior cingulate cortex (rACC) encode both the expected value of an observed choice, and the PE after the outcome was revealed. Notably, during the same task neurons recorded in the amygdala (AMY) and the rostromedial prefrontal cortex (rmPFC) do not exhibit this type of encoding. Our results suggest that humans learn by observing others, at least in part through the encoding of observational PEs in single neurons in the rACC