A Bayesian Model of Sensory Adaptation
Author Information
Author(s): Sato Yoshiyuki, Aihara Kazuyuki
Primary Institution: Graduate School of Information Systems, The University of Electro-Communications, Tokyo, Japan
Hypothesis
Can a Bayesian model explain the two types of sensory adaptation observed in temporal perception?
Conclusion
The study suggests that the type of sensory adaptation can be controlled by manipulating the statistical properties of the stimuli presented.
Supporting Evidence
- The model predicts that adaptation effects can differ based on the statistical properties of stimuli.
- Two types of adaptation, lag adaptation and Bayesian calibration, were identified.
- The study provides insights into how sensory adaptation can be controlled experimentally.
Takeaway
This study is about how our brains adapt to changes in what we see and hear, and it shows that we can change how we adapt by changing the way we present information.
Methodology
The study uses a Bayesian model to analyze sensory adaptation and derive updating rules for parameters related to perception.
Limitations
The physical or physiological meaning of the model parameters remains unclear.
Digital Object Identifier (DOI)
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