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Learning and attention reveal a general relationship between population activity and behavior.
A M Ni1, D A Ruff1, J J Alberts1
1Department of Neuroscience and Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA 15260, USA.
Science (New York, N.Y.)
|January 27, 2018
Summary
Correlated variability consistently tracks behavioral performance improvements, whether rapid attention or slow perceptual learning. This neural signal aligns with decision-making axes, resolving paradoxes in neural variability and behavior.
Area of Science:
- Neuroscience
- Cognitive Science
- Computational Neuroscience
Background:
- Prior research indicates neural correlated variability shifts with cognitive processes enhancing perception.
- Understanding the dynamic relationship between neural variability and behavioral performance is crucial.
Purpose of the Study:
- To investigate if neural correlated variability covaries with the rate of behavioral performance improvement.
- To determine if this relationship differs between rapid attentional changes and slower perceptual learning.
Main Methods:
- Analysis of neural population activity and corresponding behavioral performance data.
- Examining the relationship between correlated variability and performance across different timescales.
- Assessing the orientation of correlated variability in relation to decision-making dimensions.
Main Results:
- A consistent, single relationship was identified between correlated variability and behavioral performance.
- This relationship held true irrespective of the timescale over which correlated variability changed.
- Correlated variability was aligned with the specific dimensions used for trial-by-trial decision-making.
Conclusions:
- Neural correlated variability is a robust indicator of behavioral performance, independent of the learning or attention timescale.
- The alignment of correlated variability with decision axes provides a unified explanation for its role in behavior.
- This finding resolves long-standing questions regarding the link between neural population variability and cognitive performance.
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