1. A dynamic computational model of motivation based on self-determination theory and CANN
- Author
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Fernanda P. Mota, Silvia Silva da Costa Botelho, Hendry Ferreira Chame, Institut de Recherche en Communications et en Cybernétique de Nantes (IRCCyN), Mines Nantes (Mines Nantes)-École Centrale de Nantes (ECN)-Ecole Polytechnique de l'Université de Nantes (EPUN), Université de Nantes (UN)-Université de Nantes (UN)-PRES Université Nantes Angers Le Mans (UNAM)-Centre National de la Recherche Scientifique (CNRS), Robotique, and Université de Nantes (UN)-Université de Nantes (UN)-PRES Université Nantes Angers Le Mans (UNAM)-Centre National de la Recherche Scientifique (CNRS)-Mines Nantes (Mines Nantes)-École Centrale de Nantes (ECN)-Ecole Polytechnique de l'Université de Nantes (EPUN)
- Subjects
Information Systems and Management ,Computer science ,media_common.quotation_subject ,[SCCO.COMP]Cognitive science/Computer science ,02 engineering and technology ,Field (computer science) ,Developmental robotics ,[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] ,Theoretical Computer Science ,Artificial Intelligence ,0202 electrical engineering, electronic engineering, information engineering ,Personality ,Representation (mathematics) ,ComputingMilieux_MISCELLANEOUS ,Self-determination theory ,media_common ,Cognitive science ,Artificial neural network ,05 social sciences ,Perspective (graphical) ,Educational technology ,050301 education ,Computer Science Applications ,Control and Systems Engineering ,020201 artificial intelligence & image processing ,0503 education ,Software - Abstract
The hierarchical model of intrinsic and extrinsic motivation (HMIEM) is a framework based on the principles of self-determination theory (SDT) which describes human motivation from a multilevel perspective, and integrates knowledge on personality and social psychological determinants of motivation and its consequences. Although over the last decades HMIEM has grounded numerous correlational studies in diverse fields, it is conceptually defined as a schematic representation of the dynamics of motivation, that is not suitable for human and artificial agents research based on tracking. In this work we propose an analytic description named dynamic computational model of motivation (DCMM), inspired by HMIEM and based on continuous attractor neural networks, which consists in a computational framework of motivation. In DCMM the motivation state is represented within a self-determination continuum with recurrent feedback connections, receiving inputs from heterogeneous layers. Through simulations we show the modeling of complete scenarios in DCMM. A field study with faculty subjects illustrates how DCMM can be provided with data from SDT constructs observations. We believe that DCMM is relevant for investigating unresolved issues in HMIEM, and potentially interesting to related fields, including psychology, artificial intelligence, behavioral and developmental robotics, and educational technology.
- Published
- 2019