Indecision in Neural Decision Making Models

J. Milton; P. Naik; C. Chan; S. A. Campbell

Mathematical Modelling of Natural Phenomena (2010)

  • Volume: 5, Issue: 2, page 125-145
  • ISSN: 0973-5348

Abstract

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Computational models for human decision making are typically based on the properties of bistable dynamical systems where each attractor represents a different decision. A limitation of these models is that they do not readily account for the fragilities of human decision making, such as “choking under pressure”, indecisiveness and the role of past experiences on current decision making. Here we examine the dynamics of a model of two interacting neural populations with mutual time–delayed inhibition. When the input to each population is sufficiently high, there is bistability and the dynamics is determined by the relationship of the initial function to the separatrix (the stable manifold of a saddle point) that separates the basins of attraction of two co–existing attractors. The consequences for decision making include long periods of indecisiveness in which trajectories are confined in the neighborhood of the separatrix and wrong decision making, particularly when the effects of past history and irrelevant information (“noise”) are included. Since the effects of delay, past history and noise on bistable dynamical systems are generic, we anticipate that similar phenomena will arise in the setting of other physical, chemical and neural time–delayed systems which exhibit bistability.

How to cite

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Milton, J., et al. "Indecision in Neural Decision Making Models." Mathematical Modelling of Natural Phenomena 5.2 (2010): 125-145. <http://eudml.org/doc/197692>.

@article{Milton2010,
abstract = {Computational models for human decision making are typically based on the properties of bistable dynamical systems where each attractor represents a different decision. A limitation of these models is that they do not readily account for the fragilities of human decision making, such as “choking under pressure”, indecisiveness and the role of past experiences on current decision making. Here we examine the dynamics of a model of two interacting neural populations with mutual time–delayed inhibition. When the input to each population is sufficiently high, there is bistability and the dynamics is determined by the relationship of the initial function to the separatrix (the stable manifold of a saddle point) that separates the basins of attraction of two co–existing attractors. The consequences for decision making include long periods of indecisiveness in which trajectories are confined in the neighborhood of the separatrix and wrong decision making, particularly when the effects of past history and irrelevant information (“noise”) are included. Since the effects of delay, past history and noise on bistable dynamical systems are generic, we anticipate that similar phenomena will arise in the setting of other physical, chemical and neural time–delayed systems which exhibit bistability.},
author = {Milton, J., Naik, P., Chan, C., Campbell, S. A.},
journal = {Mathematical Modelling of Natural Phenomena},
keywords = {neural network; decision; inhibitory; time delay; bistability},
language = {eng},
month = {3},
number = {2},
pages = {125-145},
publisher = {EDP Sciences},
title = {Indecision in Neural Decision Making Models},
url = {http://eudml.org/doc/197692},
volume = {5},
year = {2010},
}

TY - JOUR
AU - Milton, J.
AU - Naik, P.
AU - Chan, C.
AU - Campbell, S. A.
TI - Indecision in Neural Decision Making Models
JO - Mathematical Modelling of Natural Phenomena
DA - 2010/3//
PB - EDP Sciences
VL - 5
IS - 2
SP - 125
EP - 145
AB - Computational models for human decision making are typically based on the properties of bistable dynamical systems where each attractor represents a different decision. A limitation of these models is that they do not readily account for the fragilities of human decision making, such as “choking under pressure”, indecisiveness and the role of past experiences on current decision making. Here we examine the dynamics of a model of two interacting neural populations with mutual time–delayed inhibition. When the input to each population is sufficiently high, there is bistability and the dynamics is determined by the relationship of the initial function to the separatrix (the stable manifold of a saddle point) that separates the basins of attraction of two co–existing attractors. The consequences for decision making include long periods of indecisiveness in which trajectories are confined in the neighborhood of the separatrix and wrong decision making, particularly when the effects of past history and irrelevant information (“noise”) are included. Since the effects of delay, past history and noise on bistable dynamical systems are generic, we anticipate that similar phenomena will arise in the setting of other physical, chemical and neural time–delayed systems which exhibit bistability.
LA - eng
KW - neural network; decision; inhibitory; time delay; bistability
UR - http://eudml.org/doc/197692
ER -

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