Title: Local reservoir model for choice-based learning,
Abstract: Decision making based on behavioral and neural observations of living systems
has been extensively studied in brain science, psychology, and other
disciplines. Decision-making mechanisms have also been experimentally
implemented in physical processes, such as single photons and chaotic lasers.
The findings of these experiments suggest that there is a certain common basis
in describing decision making, regardless of its physical realizations. In this
study, we propose a local reservoir model to account for choice-based learning
(CBL). CBL describes decision consistency as a phenomenon where making a
certain decision increases the possibility of making that same decision again
later, which has been intensively investigated in neuroscience, psychology,
etc. Our proposed model is inspired by the viewpoint that a decision is
affected by its local environment, which is referred to as a local reservoir.
If the size of the local reservoir is large enough, consecutive decision making
will not be affected by previous decisions, thus showing lower degrees of
decision consistency in CBL. In contrast, if the size of the local reservoir
decreases, a biased distribution occurs within it, which leads to higher
degrees of decision consistency in CBL. In this study, an analytical approach
on local reservoirs is presented, as well as several numerical demonstrations.
Furthermore, a physical architecture for CBL based on single photons is
discussed, and the effects of local reservoirs is numerically demonstrated.
Decision consistency in human decision-making tasks and in recruiting empirical
data are evaluated based on local reservoir. In summary, the proposed local
reservoir model paves a path toward establishing a foundation for computational
mechanisms and the systematic analysis of decision making on different levels.