A dynamic computational model of motivation based on self-determination theory and CANN

• Published in: Information sciences Vol. 476; pp. 319 - 336
• Main Authors: Ferreira Chame, Hendry, Pinto Mota, Fernanda, da Costa Botelho, Silvia Silva
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.02.2019; Elsevier
• Subjects: Artificial Intelligence; Behavioral robotics; CANN; Cognitive science; Computer science; Educational technology; HMIEM; Motivation; Neural networks; Self-determination theory; Tracking; Self-determination theory; CANN; Tracking; Behavioral robotics; Motivation; Neural networks; HMIEM; Educational technology
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/j.ins.2018.09.055

•We study the problem of motivation representation grounded in SDT and HMIEM.•We consider the dynamic interplay between motivational processes within life contexts.•We model motivation through CANNs from the self-determination continuum hypothesis.•We propose a computational implementation named DCMM capable of multimodal tracking.•We illustrate SDT human and artificial motivation research scenarios with DCMM. 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.