A Study of Behavioral Phenomena Using ANN

Authors: Yudhajit Datta

Abstract:

Behavioral aspects of experience such as will power are rarely subjected to quantitative study owing to the numerous complexities involved. Will is a phenomenon that has puzzled humanity for a long time. It is a belief that will power of an individual affects the success achieved by them in life. It is also thought that a person endowed with great will power can overcome even the most crippling setbacks in life while a person with a weak will cannot make the most of life even the greatest assets. This study is an attempt to subject the phenomena of will to the test of an artificial neural network through a computational model. The claim being tested is that will power of an individual largely determines success achieved in life. It is proposed that data pertaining to success of individuals be obtained from an experiment and the phenomenon of will be incorporated into the model, through data generated recursively using a relation between will and success characteristic to the model. An artificial neural network trained using part of the data, could subsequently be used to make predictions regarding data points in the rest of the model. The procedure would be tried for different models and the model where the networks predictions are found to be in greatest agreement with the data would be selected; and used for studying the relation between success and will.

Keywords: Will Power, Success, ANN, Time Series Prediction, Sliding Window, Computational Model, Behavioral Phenomena.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1338267

References:

[1] Seth Herd ,1, Brian Mingus and Randall OReilly, Dopamine and self-directed learning, Biologically Inspired Cognitive Architectures : DBLP, 2010, pp. 58-63.
[2] Kent C. Berridge , Terry E. Robinson , What is the role of dopamine in reward: hedonic impact, reward learning, or incentive salience?, Brain Research Reviews : Elsevier, 1998, pp.309369.
[3] Marilyn M Nelson, W T Illingworth, A Practical Guide to Neural Networks, Prentice Hall PTR, 1991
[4] R.J.Frank, N.Davey, S.P.Hunt,Time Series Prediction and Neural Networks, Journal of Intelligent and Robotic Systems,May 2001, Volume 31, Issue 1-3, pp 91-103
[5] Ababarnel H., D., I., Brown R., Sidorowich J., L. and Tsimring L., S., 1993, The analysisof observed chaotic data in physical systems.Reviews of Modern Physics, Vol. 65, No. 4,pp1331-1392.
[6] www.apa.org/helpcenter/willpower.aspx (as of 16-11-13).
[7] today.uconn.edu/blog/2012/11/uconn-researcher(as of 16-11-13).