Abstract. Humans have the ability to flexibly adjust their information process-ing strategy according to situational characteristics. However, such ability has been largely overlooked in computational modeling research in high-order hu-man cognition, particularly in learning. The present work introduces frameworks of cognitive models of human learning that take contextual factors into account. The framework assumes that human learning processes are not strictly error min-imization, but optimization of knowledge. A simulation study was conducted and showed that the present framework successfully replicated observed psychological phenomena.
Book's abstract : The book discusses the analysis, comparison and integration of computational appro...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2...
We describe how a standard reinforcement learning algorithm can be changed to include a second conte...
Computational models of cognition provide an interface to connect advanced mathematical tools and me...
An important characteristic of human learning and decision-making is the flexibility with which we r...
The reliance on subject matter experts to provide expertise may restrict development of human behavi...
Abstract. A cognitive model is a set of production rules or skills encoded in intelligent tutors to ...
Machine learning systems are increasingly a part of human lives, and so it is increasingly important...
Using a pure machine learning approach to enable the generation of behavior for agents in serious ga...
This paper deals with cognitive theories behind agent-based modeling of learning and information pro...
Within their mental and social processes, humans often learn, adapt and apply specific mental models...
Reinforcement learning (RL) has been widely used to study and model human, animal and artificial int...
It has been widely known that reasoning with and about context is an essential aspect of human cogni...
Abstract. Both learning and reasoning are important aspects of intelli-gence. However they are rarel...
<p>This chapter reviews computational representations of human behavior involving three training pri...
Book's abstract : The book discusses the analysis, comparison and integration of computational appro...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2...
We describe how a standard reinforcement learning algorithm can be changed to include a second conte...
Computational models of cognition provide an interface to connect advanced mathematical tools and me...
An important characteristic of human learning and decision-making is the flexibility with which we r...
The reliance on subject matter experts to provide expertise may restrict development of human behavi...
Abstract. A cognitive model is a set of production rules or skills encoded in intelligent tutors to ...
Machine learning systems are increasingly a part of human lives, and so it is increasingly important...
Using a pure machine learning approach to enable the generation of behavior for agents in serious ga...
This paper deals with cognitive theories behind agent-based modeling of learning and information pro...
Within their mental and social processes, humans often learn, adapt and apply specific mental models...
Reinforcement learning (RL) has been widely used to study and model human, animal and artificial int...
It has been widely known that reasoning with and about context is an essential aspect of human cogni...
Abstract. Both learning and reasoning are important aspects of intelli-gence. However they are rarel...
<p>This chapter reviews computational representations of human behavior involving three training pri...
Book's abstract : The book discusses the analysis, comparison and integration of computational appro...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2...
We describe how a standard reinforcement learning algorithm can be changed to include a second conte...