H2020Индивидуална стипендия2016–2018

PREDACTION · Prediction and Anticipation of Actions: Modelling How We Foresee the Others

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период
2016-02-01 → 2018-01-31
Финансиране от ЕС
165 599 €
Участници
1
Схема
MSCA-IF-EF-ST

Линиите свързват координатора с партньорите.

Накратко на български

Способността ни да предвиждаме действията на другите, например да разберем намерението на човек, преди да е завършил движението си, е в центъра на анализа. Това помага за създаването на по-ефективни роботи и по-доброто разбиране на състояния като аутизма.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Prediction and Anticipation of Actions: Modelling How We Foresee the Others

The unparalleled social abilities proper to human beings are mostly due to our capacity to understand each other and attribute a meaning to simple actions. This faculty develops in parallel with our motoric and cognitive skills and is deeply grounded into them. The reiterated exposure to a certain action and its consequence, creates a bond between the two that is very hard to change. As such, sometimes we do not need to see an actions performed to the end to read the actual intention of its actor. However, we encounter a strong surprise effect in the moment when the action develops in an ‘unpredictable’ way, meaning, its outcome does not match what we expected to see. Two important questions that would add valuable insights to the understanding of social cognition are: 1, What information is actually used to predict someone else’s action? And 2, How do humans deal with surprise and what this surprise effect is cause by? Answers to these questions can find important and multidisciplinary practical applications. The first question will inspire technological development, namely brain computer interface and robotics. In a digital era like the one we live now, machines and technology are expected to be predictive more than reactive to human commands. This will shorten their response making them more efficient and the human-machine interaction more fluid and ‘natural’. Social robots are nowadays a reality: they are mostly used for elderly, to keep them company and accompanying them in basic errands in the house. Making the interaction smoother means also make people more willing to accept this new form of help. The second question might as well be of great importance in the clinical domain. Big part of the literature links diseases like autism to an inherent inability to relate to other people. In particular, people with autism might have difficulty in understanding the meaning behind people’s actions. This project will give new insights about the possibility that predictive skills are actually jeopardized in this population. This said, the literature does not provide yet a clear explanation of the cortical mechanisms behind our predictive ability, nor this surprise effect. Shedding light on them is the main objective of this project. The overall aims of the study are: - to investigate how different features impact the ability to predict someone’s action - to unravel the mechanisms behind the surprise effect and what triggers it The first aim was achieved by considering two main cues commonly related to action prediction and understanding: familiarity with the object and attention orientation of the actor. When we are familiar with an object and its use, it is easy to assume why someone is using it. Same is true when someone orients his attention towards an object. This is an important signal that he is willing to interact with it. The second aim was achieved by creating a learning paradigm in which originally unpredictable actions become more predictable by making our participants repeatedly exposed to them.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

The ability to understand and predict the actions of others is critical for human social interaction but how the brain accomplishes this function is still poorly understood. This project will explore this fundamental question using an original and innovative approach, by studying how we anticipate the actions of others before they even start to act. The final aim will be to develop a model that integrates different forms of information available before the action starts with kinematic features available once the action has started. To achieve this aim, we will use a combination of EEG/ERP and fMRI that will be integrated through the use of sophisticated source localization software and analysis to obtain a complete picture of when and where each mechanism becomes informative in the brain. This project will shed new light on the study of social cognition by unraveling little studied but crucial aspects of action understanding and prediction. In addition to opening new grounds in the field of social neuroscience and adding theoretical and methodological expertise to my skill-set, the findings and the resultant model of action prediction and social interaction will be useful also beyond social neuroscience. The project could contribute to technological development in the brain computer interface and social robotics fields (e.g. building robots able to smoother interactions), as well as be applied to the understanding of social deficits in psychiatric disorders (i.e. autism and schizophrenic patients). The Social Brain Lab at the Netherlands Institute for Neuroscience (NIN) represents the most suitable environment for this project by providing: world expert in the field of action observation with prominent expertise in the fMRI and EEG techniques (C.Keysers and V. Gazzola); all the technical facilities necessary for the success of the project; a world leading neuroscience community present at the NIN that will contribute to my further training and project development.

Оригинален текст от CORDIS (на английски).

Участници

  • KONINKLIJKE NEDERLANDSE AKADEMIE VAN WETENSCHAPPEN - KNAW · AMSTERDAMКоординаторНидерландия

Връзки

Данни: CORDIS, © Европейски съюз