Intrinsic Motivation Model Based on Reward Gating

2021 ◽  
pp. 688-699
Author(s):  
Matej Pecháč ◽  
Igor Farkaš
Keyword(s):  
Intrinsic Motivation ◽  
Model Based ◽  
Motivation Model

Short-term and long-term learners’ motivation modeling in Web-based educational systems

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shahzad Shabbir ◽  
Muhammad Adnan Ayub ◽  
Farman Ali Khan ◽  
Jeffrey Davis
Keyword(s):  
Real Time ◽  
Learning Process ◽  
Educational Systems ◽  
Short Term ◽  
Web Based ◽  
Learner Motivation ◽  
Content Type ◽  
Model Based ◽  
Motivation Model

Purpose Short-term motivation encompasses specific, challenging and attainable goals that develop in the limited timespan. On the other hand, long-term motivation indicates a sort of continuing commitment that is required to complete assigned task. As short-term motivational problems span for a limited period of time, such as a session, therefore, they need to be addressed in real time to keep the learner engaged in the learning process. Similarly, long-term learners’ motivation plays an equally important role to retain the learner in the long run and minimize the risk of dropout. Therefore, the purpose of this study is to incorporate a comprehensive learner motivation model that is based on short-term and long-term aspects of the learners' motivation. This approach enables Web-based educational systems to identify the real-time motivational state of the learner and provide personalized interventions to keep the learners engaged in learning process. Design/methodology/approach Recent research regarding personalized Web-based educational systems demonstrates learner’s motivation to be an essential component of the learning model. This is because of the fact that low motivation results in either students’ less engagement or complete drop out from the learning activities. A learner motivation model is considered to be a set of perceptions and beliefs that the system has developed about a learner. This includes both short-term and long-term motivations of leaners. Findings This study proposed a framework of a domain independent learners’ motivation model based on firm educational theories. The proposed framework consists of two modules. The primary module deals with real-time identification of motivation and logging off activities such as login, forum participation and adherence to assessment deadline. Secondary module maintains the profile of leaners associated with both short-term and long-term motivation. A study was conducted to verify the impact of learners’ motivation model and personalized interventional strategies based on proposed model, using Systematical Information Education Method assessment standards. The results show an increase in motivational index and the characteristics associated with motivation during the conducted study. Originality/value Motivational diagnosis is important for both traditional classrooms and Web-based education systems. It is one of the major elements that contribute in the success of the learning process. However, dropout rate among online students is very high, which leads to incorporate motivational elements in more personalized way because motivated students will retain the course until they successfully complete it. Hence, identifying learner’s motivation, updating learners’ motivation model based on this identification and providing personalized interventions are the key for the success of Web-based educational systems.

scholarly journals Intrinsic motivation in virtual assistant interaction for fostering spontaneous interactions

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250326
Author(s):  
Chang Li ◽  
Hideyoshi Yanagisawa
Keyword(s):  
Artificial Intelligence ◽  
Intrinsic Motivation ◽  
Free Choice ◽  
High Expectation ◽  
Hypothetical Model ◽  
User Interactions ◽  
Two Factors ◽  
Choice Period ◽  
Motivation Model ◽  
User Motivation

With the growing utility of today’s conversational virtual assistants, the importance of user motivation in human–artificial intelligence interactions is becoming more obvious. However, previous studies in this and related fields, such as human–computer interaction, scarcely discussed intrinsic motivation (the motivation to interact with the assistants for fun). Previous studies either treated motivation as an inseparable concept or focused on non-intrinsic motivation (the motivation to interact with the assistant for utilitarian purposes). The current study aims to cover intrinsic motivation by taking an affective engineering approach. A novel motivation model is proposed, in which intrinsic motivation is affected by two factors that derive from user interactions with virtual assistants: expectation of capability and uncertainty. Experiments in which these two factors are manipulated by making participants believe they are interacting with the smart speaker “Amazon Echo” are conducted. Intrinsic motivation is measured both by using questionnaires and by covertly monitoring a five-minute free-choice period in the experimenter’s absence, during which the participants could decide for themselves whether to interact with the virtual assistants. Results of the first experiment showed that high expectation engenders more intrinsically motivated interaction compared with low expectation. However, the results did not support our hypothesis that expectation and uncertainty have an interaction effect on intrinsic motivation. We then revised our hypothetical model of action selection accordingly and conducted a verification experiment of the effects of uncertainty. Results of the verification experiment showed that reducing uncertainty encourages more interactions and causes the motivation behind these interactions to shift from non-intrinsic to intrinsic.

scholarly journals Exploring the intention to use M-payment in India

2019 ◽  
Vol 13 (3/4) ◽  
pp. 276-305
Author(s):  
Sushil S. Chaurasia ◽  
Surabhi Verma ◽  
Vibhav Singh
Keyword(s):  
Intrinsic Motivation ◽  
Behavioral Intention ◽  
Extrinsic Motivation ◽  
Positive Relationship ◽  
Future Research ◽  
Intention To Use ◽  
Content Type ◽  
Starting Point ◽  
Motivational Model ◽  
Motivation Model

Purpose The purpose of this study is to develop a model that explains a user’s attitude toward M-payments in India, based on the motivational model and awareness about demonetization policy. The study also investigates the validity and differential predictive power of four different M-payment usage models, by considering the effect of improved awareness regarding demonetization policy on the core construct of the motivational model. Design/methodology/approach The model was tested with survey data from 362 M-payment users using partial least squares. Respondents were M-payment users with significant usage experience. Findings This study empirically determined that the motivation model and awareness about demonetization policy in M-payment usage after demonetization are connected. As hypothesized, the study found: a positive relationship between extrinsic motivation and intention to use M-payment, positive relationship between awareness about demonetization policy and behavioral intention to use M-payment, positive relationship between intrinsic motivation and extrinsic motivation, positive relationship between awareness about demonetization policy and extrinsic motivation and positive moderation effect of awareness about demonetization policy on the extrinsic motivation-behavioral intention relationships. The hypothesis that awareness about demonetization policy would have a moderating effect on intrinsic motivation–behavioral intention relationship was not supported. Practical implications From a practitioner’s perspective, this study underscores the importance of raising sufficient awareness about the demonetization policy as a determinant of users’ willingness to use M-payment services. Mandatory regulations by the government and motivation toward M-payment use can be a good starting point for a cashless economy. Originality/value This study makes a needed contribution to the literature by validating the integrated motivation model, emphasizing the importance of raising awareness about the demonetization policy among M-payment users. The model may provide a useful foundation for future research in this area.

scholarly journals Incremental Learning of Planning Actions in Model-Based Reinforcement Learning

2019 ◽  
Author(s):  
Jun Hao Alvin Ng ◽  
Ronald P. A. Petrick
Keyword(s):  
Reinforcement Learning ◽  
Intrinsic Motivation ◽  
Incremental Learning ◽  
Domain Model ◽  
Experimental Results ◽  
The Novel ◽  
Model Based ◽  
Domain Models ◽  
Novel Concept ◽  
Planning Problems

The soundness and optimality of a plan depends on the correctness of the domain model. Specifying complete domain models can be difficult when interactions between an agent and its environment are complex. We propose a model-based reinforcement learning (MBRL) approach to solve planning problems with unknown models. The model is learned incrementally over episodes using only experiences from the current episode which suits non-stationary environments. We introduce the novel concept of reliability as an intrinsic motivation for MBRL, and a method to learn from failure to prevent repeated instances of similar failures. Our motivation is to improve the learning efficiency and goal-directedness of MBRL. We evaluate our work with experimental results for three planning domains.

Representation, abstraction, and simple-minded sophisticates

2020 ◽  
Vol 43 ◽  
Author(s):  
Peter Dayan
Keyword(s):  
Decision Theory ◽  
Predictive Coding ◽  
Bayesian Decision Theory ◽  
Bayesian Decision ◽  
Model Based ◽  
Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

Quantitative Model-Based Image Analysis of NuMa Distribution Links Nuclear Organization with Cell Phenotype

2001 ◽  
Vol 7 (S2) ◽  
pp. 578-579
Author(s):  
David W. Knowles ◽  
Sophie A. Lelièvre ◽  
Carlos Ortiz de Solόrzano ◽  
Stephen J. Lockett ◽  
Mina J. Bissell ◽  
...  
Keyword(s):  
Image Analysis ◽  
Mammary Epithelial Cells ◽  
Nuclear Organization ◽  
Critical Role ◽  
Quantitative Model ◽  
Mammary Epithelial ◽  
Cell Phenotype ◽  
Proliferating Cells ◽  
Mitotic Apparatus ◽  
Model Based

The extracellular matrix (ECM) plays a critical role in directing cell behaviour and morphogenesis by regulating gene expression and nuclear organization. Using non-malignant (S1) human mammary epithelial cells (HMECs), it was previously shown that ECM-induced morphogenesis is accompanied by the redistribution of nuclear mitotic apparatus (NuMA) protein from a diffuse pattern in proliferating cells, to a multi-focal pattern as HMECs growth arrested and completed morphogenesis . A process taking 10 to 14 days.To further investigate the link between NuMA distribution and the growth stage of HMECs, we have investigated the distribution of NuMA in non-malignant S1 cells and their malignant, T4, counter-part using a novel model-based image analysis technique. This technique, based on a multi-scale Gaussian blur analysis (Figure 1), quantifies the size of punctate features in an image. Cells were cultured in the presence and absence of a reconstituted basement membrane (rBM) and imaged in 3D using confocal microscopy, for fluorescently labeled monoclonal antibodies to NuMA (fαNuMA) and fluorescently labeled total DNA.

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