A Neuro-computational Account of Arbitration between Choice Imitation and Goal Emulation during Human Observational Learning

When individuals learn from observing the behavior of others, they deploy at least two distinct strategies. Choice imitation involves repeating other agents’ previous actions, whereas emulation proceeds from inferring their goals and intentions. Despite the prevalence of observational learning in humans and other social animals, a fundamental question remains unaddressed: how does the brain decide which strategy to use in a given situation? In two fMRI studies (the second a pre-registered replication of the first), we identify a neuro-computational mechanism underlying arbitration between choice imitation and goal emulation. Computational modeling, combined with a behavioral task that dissociated the two strategies, revealed that control over behavior was adaptively and dynamically weighted toward the most reliable strategy. Emulation reliability, the model’s arbitration signal, was represented in the ventrolateral prefrontal cortex, temporoparietal junction, and rostral cingulate cortex. Our replicated findings illuminate the computations by which the brain decides to imitate or emulate others

A Review of Energy Models. No. 3 (Special Issue on Soviet Models)

The experience of the USSR in the field of energy systems development modeling reveals certain patterns and principles that influence the structure and use of energy models, principally: -- The need to use mainly optimization models since, for planning purposes, optimal solutions must be found; -- The need to coordinate individual models in order to obtain the country's objectives; -- The existing organizational structure of planning which must be taken into account; -- The dependence of models on time aspects of planning (annual, 5-year, 15-year); -- The elaboration of corresponding methods for providing necessary input data. This has required the development of a special concept for optimizing energy systems development with the use of mathematical models. It is based on consideration of the energy industries of the country as complex with a hierarchical structure of energy systems of various territorial and branch levels. At the same time, the differentiation of aims at different times during the planning period have been taken into account. This concept is given here in its existing state (it is continuously developed and perfected) for better understanding of the energy models described. In particular, we show the role of the system of models for optimization of the energy supply system as a whole, and that of more detailed branch models (oil, gas, coal, electricity production systems). For optimal energy strategy evaluation, the most important models are those used on the highest levels of the energy systems hierarchy, i.e. the general (aggregate) energy systems of the country and of economic regions, and branch energy systems. Only these models are described here; models used on lower levels for solving some technical problems are far more diverse and numerous, and it is impossible to consider them all in a single review

A Review of Energy Models No.4 - July 1978

This review is the fourth in the IIASA series A REVIEW OF ENERGY MODELS (RR-74-10, No. 1 of May 1974 as revised in September 1976; RR-75-30, No. 2 of July 1975; and RR-77-13, No. 3, Special Issue of Soviet Models), which aims at wider diffusion of energy modeling work in progress at other institutions. Fourteen models are described in this issue and again classified in terms of substance and geographical applicability with further subdivision into groups corresponding to model user requirements: the majority of the models focus on the energy problem; they are mostly national ones involving either one or several kinds of fuel; six other models, both international and national, combine energy and overall economic aspects; they may be of particular interest for a more global consideration of energy problems

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

L'Institut de Recherches sur les Fruits et Agrumes 1942-1984 : de l'IFAC à l'IRFA

Ce rapport fournit quelques éléments pour un bilan et ravive la mémoire par un rappel historique et géographique de la naissance de l'IFAC, de sa croissance, puis de l'ultime phase de dépendance croissante à son intégration dans un organisme de plus grande dimension. Tout au long de ses 40 années d'existence, l'activité de l'IRFA a été répartie en quatre programmes consacré aux agrumes, à l'ananas, aux bananiers et aux plantains et aux autres fruitier