MMG: a learning game platform for understanding and predicting human recall memory

Authors:
Umer Fareed;Byoung-Tak Zhang
Affiliations:
School of Computer Science and Engineering, Seoul National University, Seoul, Korea;School of Computer Science and Engineering, Seoul National University, Seoul, Korea and Cognitive Science and Brain Science, Seoul National University, Seoul, Korea
Venue:
PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
Year:
2010

Citing 7
Cited 0

Artificial Intelligence: A Modern Approach

Artificial Intelligence: A Modern Approach
Markov logic networks

Machine Learning
Pattern Recognition and Machine Learning (Information Science and Statistics)

Pattern Recognition and Machine Learning (Information Science and Statistics)
Multimodal Processing and Interaction: Audio, Video, Text

Multimodal Processing and Interaction: Audio, Video, Text
Deep, narrow sigmoid belief networks are universal approximators

Neural Computation
Bayesian Networks and Decision Graphs

Bayesian Networks and Decision Graphs
Hypernetworks: A Molecular Evolutionary Architecture for Cognitive Learning and Memory

IEEE Computational Intelligence Magazine

Quantified Score

Hi-index	0.00

Visualization

Abstract

How humans infer probable information from the limited observed data? How they are able to build on little knowledge about the context in hand? Is the human memory repeatedly constructing and reconstructing the events that are being recalled? These are a few questions that we are interested in answering with our multimodal memory game (MMG) platform that studies human memory and their behaviors while watching and remembering TV dramas for a better recall. Based on the preliminary results of human learning obtained from the MMG games, we attempt to show that the human memory recall improves steadily with the number of game sessions. As an example case, we provide a comparison for the text-to-text and text-image-to-text learning and demonstrate that the addition of image context is useful in improving the learning.