A Neural Active Inference Model of Perceptual-Motor Learning
- URL: http://arxiv.org/abs/2211.10419v1
- Date: Wed, 16 Nov 2022 20:00:38 GMT
- Title: A Neural Active Inference Model of Perceptual-Motor Learning
- Authors: Zhizhuo Yang, Gabriel J. Diaz, Brett R. Fajen, Reynold Bailey,
Alexander Ororbia
- Abstract summary: The active inference framework (AIF) is a promising new computational framework grounded in contemporary neuroscience.
In this study, we test the ability for the AIF to capture the role of anticipation in the visual guidance of action in humans.
We present a novel formulation of the prior function that maps a multi-dimensional world-state to a uni-dimensional distribution of free-energy.
- Score: 62.39667564455059
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The active inference framework (AIF) is a promising new computational
framework grounded in contemporary neuroscience that can produce human-like
behavior through reward-based learning. In this study, we test the ability for
the AIF to capture the role of anticipation in the visual guidance of action in
humans through the systematic investigation of a visual-motor task that has
been well-explored -- that of intercepting a target moving over a ground plane.
Previous research demonstrated that humans performing this task resorted to
anticipatory changes in speed intended to compensate for semi-predictable
changes in target speed later in the approach. To capture this behavior, our
proposed "neural" AIF agent uses artificial neural networks to select actions
on the basis of a very short term prediction of the information about the task
environment that these actions would reveal along with a long-term estimate of
the resulting cumulative expected free energy. Systematic variation revealed
that anticipatory behavior emerged only when required by limitations on the
agent's movement capabilities, and only when the agent was able to estimate
accumulated free energy over sufficiently long durations into the future. In
addition, we present a novel formulation of the prior function that maps a
multi-dimensional world-state to a uni-dimensional distribution of free-energy.
Together, these results demonstrate the use of AIF as a plausible model of
anticipatory visually guided behavior in humans.
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