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Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations
Published on: September 16, 2015
Attention, uncertainty, and free-energy
Harriet Feldman1, Karl J Friston
1The Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London London, UK.
Frontiers in Human Neuroscience
|December 17, 2010
Summary
Attention involves inferring uncertainty in hierarchical perception. Neuronal simulations show that optimizing precision based on world states explains attentional bias, competition, and capture in a Bayesian framework.
Area of Science:
- Cognitive Neuroscience
- Computational Neuroscience
Background:
- Attention is crucial for processing sensory information.
- Previous theories suggest attention involves inferring uncertainty or precision during hierarchical perception.
Purpose of the Study:
- To substantiate the claim that attention can be understood as inferring uncertainty or precision.
- To model directed spatial attention and biased competition using neuronal simulations.
Main Methods:
- Neuronal simulations based on Bayesian inference and free-energy optimization.
- Modeling probabilistic representations of the world where neuronal activity optimizes free-energy.
- Simulating psychophysical and electrophysiological responses within the Posner paradigm.
Main Results:
- Optimizing the precision of sensory data based on world states explains key aspects of attention.
- Simulated responses align with attentional bias, gating, competition for resources, and attentional capture.
- Biased competition for neuronal representation arises naturally from Bayes-optimal perception when stimuli are presented simultaneously.
Conclusions:
- Attention can be fundamentally understood as inferring the precision of hierarchical perceptual models.
- Bayesian inference and free-energy optimization provide a robust framework for modeling attentional mechanisms.
- This approach successfully reconciles various phenomena of attention, including biased competition and attentional capture.
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