Learning sequences of actions : infant experiments and neural network models

  • In our daily life, we carry out lots of tasks like typing, playing tennis, and playing the piano, without even noticing there is sequence learning involved. No matter how simple or complex they are, these tasks require the sequential planning and execution of a series of movements. As an ability of primary importance in one’s life, and an ability that everyone manages to learn, action-sequence learning has been studied by researchers from different fields: psychologists, neurophysiologists as well as roboticists. In the concept of sequence learning, perceptual learning and motor learning, implicit and explicit learning have been studied and discussed independently. We are interested in infancy research, because infants, with underdeveloped brain functions and with limited motor ability, have little experience with the world and not yet built internal models as presumption of how to interpret the world. A series of infant experiments in the 1980s provided evidence that infants can rapidly develop anticipatory eye movements for visual events. Even when infants have no control of those spatial-temporal patterns, they can respond actually prior to the onset of the visual event, referred as "Anticipation". In this work, we applied a gaze-contingent paradigm using real-time eye tracking to put 6- and 8-month-old infants in direct control of their visual surroundings. This paradigm allows the infant to change an image on a screen by looking at a peripheral red disc, which functions as a switch. We found that infants quickly learn to perform eye movements to trigger the appearance of new stimuli and that they anticipate the consequences of their actions in an early stage of the experiment. Attention-shift from learning one stimulus to the next novel stimulus is important in sequence learning. In the test phase of infant visual habituation with two objects, we propose a new theory of explaining the familiarity-to-novelty shift. In our opinion an infant’s interest in a stimulus is related to its learning progress, the improvement of performance. As a consequence, infants prefer the stimulus which their current learning progress is maximal for, naturally giving rise to a familiarity-to-novelty shift in certain situations. Our network model predicts that the familiarity-to-novelty-shift only emerges for complex stimuli that produce bell-shaped learning curves after brief familiarization, but does not emerge for simple stimuli that produce exponentially decreasing learning curves or for long familiarization time, which is consistent with experimental results. This research suggests the infant's interest in a stimulus may be related to its current learning progress. This can give rise to a dynamic familiarity-to-novelty shift depending on both the infant's learning efficiency and the task complexity. We know that for both infants and adults, the performance on certain motor-sequence tasks can be improved through practice. However, adults usually have to perform complex tasks in complicated environments; for example, learning multiple tasks is unavoidable in our daily life. In existing research, learning multiple tasks showed puzzling and seemingly contradictory results. On the one hand, a wide variety of proactive and retroactive interference effects have been observed when multiple tasks have to be learned. On the other hand, some studies have reported facilitation and transfer of learning between different tasks. In order to find out the interaction between multiple-task learning, and to find an optimal training schedule, we use a recurrent neural network to model a series of experiments on movement sequence learning. The network model learns to carry out the correct movement sequences through training and reproduces differences between training schedules such as blocked training vs. random training in psychophysics experiments. The network model also shows striking similarity to human performance, and makes prediction for tasks similarity and different training schedules. In conclusion, the thesis presents learning sequences of actions in infants and recurrent neural networks. We carried out a gaze-contingent experiment to study infants’ rapid anticipation of their own action outcomes, and we also constructed two recurrent neural network models, with one model explaining infant attention shift in visual habituation, and the other model directing to task similarity and training schedule in motor sequence control in adults.
  • Die meisten Aufgaben, die ein Mensch Tag für Tag bewältigen muss, bestehen aus einer Abfolge mehrerer Aktionen. Einen Kaffee zu machen erfordert zum Beispiel das Finden und Bereitstellen einer Tasse und das Befüllen und Bedienen der Maschine, jeweils bestehend aus mehreren einzelnen Handgriffen und Aufmerksamkeitsmustern. Obwohl jeder Mensch und auch die meisten Tiere ohne Probleme lernen, solch sequentielle Aufgaben zu meistern, ist der dahinter stehende Mechanismus weitgehend unbekannt. Klar ist, dass sowohl Lernen auf Konzept- wie auch auf Motorebene benötigt wird und sowohl ein intaktes implizites wie auch explizites Gedächtnis vorausgesetzt wird. Auf algorithmischer Ebene ist bisher aber sogar das Lernen dieser einzelnen Komponenten ein schwer zu lösendes Problem, zum Beispiel einem humanoiden Roboter beizubringen, wie ein Objekt gegriffen und platziert werden kann. ...

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Author:Quan Wang
Referee:Jochen TrieschORCiD, Constantin RothkopfORCiDGND, Monika KnopfORCiDGND
Advisor:Jochen Triesch
Document Type:Doctoral Thesis
Date of Publication (online):2014/02/28
Year of first Publication:2013
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Granting Institution:Johann Wolfgang Goethe-Universität
Date of final exam:2013/07/26
Release Date:2014/03/03
Page Number:136
Diese Dissertation steht außerhalb der Universitätsbibliothek leider (aus urheberrechtlichen Gründen) nicht im Volltext zur Verfügung, die CD-ROM kann (auch über Fernleihe) bei der UB Frankfurt am Main ausgeliehen werden.
Institutes:Physik / Physik
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 53 Physik / 530 Physik
Licence (German):License LogoArchivex. zur Lesesaalplatznutzung § 52b UrhG