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Robust averaging protects decisions from noise in neural computations
Vickie Li1, Santiago Herce Castañón1, Joshua A Solomon2
1Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom.
Plos Computational Biology
|August 26, 2017
Summary
Human visual perception uses "robust averaging" to ignore outlying information, a strategy that surprisingly improves performance by increasing resilience to neural noise during decision-making.
Area of Science:
- Cognitive Neuroscience
- Computational Vision
- Human Perception
Background:
- Humans often exhibit 'robust averaging' when integrating visual information, downweighting outlying data points.
- The functional benefit of this non-ideal integration strategy, which discards potentially useful information, remains poorly understood.
Purpose of the Study:
- To investigate the adaptive value of robust averaging in human visual perception.
- To explore the relationship between robust averaging and performance in orientation judgment tasks.
Main Methods:
- Participants judged the average orientation of gratings in a circular array.
- Computational simulations were used to model the effects of noise on integration strategies.
Main Results:
- Observers demonstrated robust averaging of orientation information.
- The degree of robust averaging positively correlated with task performance.
- Simulations revealed that robust averaging enhances performance under conditions of late-stage neural noise.
Conclusions:
- Robust averaging, while seemingly suboptimal, confers resilience against noise in neural computations.
- This strategy may represent an adaptive mechanism for robust decision-making in the presence of internal noise.
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